Methods for evaluating oligonucleotide probe sequences

APPENDIX

[0001] This patent application includes an appendix (the “Appendix”), which contains the source code for the software used in carrying out the examples in accordance with the present invention.

[0002] A portion of the present disclosure contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure as it appears in the U.S. Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever.

BACKGROUND OF THE INVENTION

[0003] 1. Field of the Invention

[0004] Significant morbidity and mortality are associated with infectious diseases and genetically inherited disorders. More rapid and accurate diagnostic methods are required for better monitoring and treatment of these conditions. Molecular methods using DNA probes, nucleic acid hybridization and in vitro amplification techniques are promising methods offering advantages to conventional methods used for patient diagnoses.

[0005] Nucleic acid hybridization has been employed for investigating the identity and establishing the presence of nucleic acids. Hybridization is based on complementary base pairing. When complementary single stranded nucleic acids are incubated together, the complementary base sequences pair to form double-stranded hybrid molecules. The ability of single stranded deoxyribonucleic acid (ssDNA) or ribonucleic acid (RNA) to form a hydrogen bonded structure with a complementary nucleic acid sequence has been employed as an analytical tool in molecular biology research. The availability of radioactive nucleoside triphosphates of high specific activity and the development of methods for their incorporation into DNA and RNA has made it possible to identify, isolate, and characterize various nucleic acid sequences of biological interest. Nucleic acid hybridization has great potential in diagnosing disease states associated with unique nucleic acid sequences. These unique nucleic acid sequences may result from genetic or environmental change in DNA by insertions, deletions, point mutations, or by acquiring foreign DNA or RNA by means of infection by bacteria, molds, fungi, and viruses. The application of nucleic acid hybridization as a diagnostic tool in clinical medicine is limited due to the cost and effort associated with the development of sufficiently sensitive and specific methods for detecting potentially low concentrations of disease-related DNA or RNA present in the complex mixture of nucleic acid sequences found in patient samples.

[0006] One method for detecting specific nucleic acid sequences generally involves immobilization of the target nucleic acid on a solid support such as nitrocellulose paper, cellulose paper, diazotized paper, or a nylon membrane. After the target nucleic acid is fixed on the support, the support is contacted with a suitably labeled probe nucleic acid for about two to forty-eight hours. After the above time period, the solid support is washed several times at a controlled temperature to remove unhybridized probe. The support is then dried and the hybridized material is detected by autoradiography or by spectrometric methods. When very low concentrations must be detected, the above method is slow and labor intensive, and nonisotopic labels that are less readily detected than radiolabels are frequently not suitable.

[0007] A method for the enzymatic amplification of specific segments of DNA known as the polymerase chain reaction (PCR) method has been described. This in vitro amplification procedure is based on repeated cycles of denaturation, oligonucleotide primer annealing, and primer extension by thermophilic polymerase, resulting in the exponential increase in copies of the region flanked by the primers. The PCR primers, which anneal to opposite strands of the DNA, are positioned so that the polymerase catalyzed extension product of one primer can serve as a template strand for the other, leading to the accumulation of a discrete fragment whose length is defined by the distance between the 5′ ends of the oligonucleotide primers.

[0008] Other methods for amplifying nucleic acids have also been developed. These methods include single primer amplification, ligase chain reaction (LCR), transcription-mediated amplification methods including 3SR and NASBA, and the Q-beta-replicase method. Regardless of the amplification used, the amplified product must be detected.

[0009] One method for detecting nucleic acids is to employ nucleic acid probes that have sequences complementary to sequences in the target nucleic acid. A nucleic acid probe may be, or may be capable of being, labeled with a reporter group or may be, or may be capable of becoming, bound to a support. Detection of signal depends upon the nature of the label or reporter group. Usually, the probe is comprised of natural nucleotides such as ribonucleotides and deoxyribonucleotides and their derivatives although unnatural nucleotide mimetics such as peptide nucleic acids and oligomeric nucleoside phosphonates are also used. Commonly, binding of the probes to the target is detected by means of a label incorporated into the probe. Alternatively, the probe may be unlabeled and the target nucleic acid labeled. Binding can be detected by separating the bound probe or target from the free probe or target and detecting the label. In one approach, a sandwich is formed comprised of one probe, which may be labeled, the target and a probe that is or can become bound to a surface. Alternatively, binding can be detected by a change in the signal-producing properties of the label upon binding, such as a change in the emission efficiency of a fluorescent or chemiluminescent label. This permits detection to be carried out without a separation step. Finally, binding can be detected by labeling the target, allowing the target to hybridize to a surface-bound probe, washing away the unbound target and detecting the labeled target that remains.

[0010] Direct detection of labeled target hybridized to surface-bound probes is particularly advantageous if the surface contains a mosaic of different probes that are individually localized to discrete, known areas of the surface. Such ordered arrays containing a large number of oligonucleotide probes have been developed as tools for high throughput analyses of genotype and gene expression. Oligonucleotides synthesized on a solid support recognize uniquely complementary nucleic acids by hybridization, and arrays can be designed to define specific target sequences, analyze gene expression patterns or identify specific allelic variations. One difficulty in the design of oligonucleotide arrays is that oligonucleotides targeted to different regions of the same gene can show large differences in hybridization efficiency, presumably due, at least in part, to the interplay between the secondary structures of the oligonucleotides and their targets and the stability of the final probe/target hybridization product. A method for predicting which oligonucleotides will show detectable hybridization would substantially decrease the number of iterations required for optimal array design and would be particularly useful when the total number of oligonucleotide probes on the array is limited. A method to predict oligonucleotide hybridization efficiency would also streamline the empirical approaches currently used to select potential antisense therapeutics, which are designed to modulate gene expression in vivo by hybridizing to specific messenger RNA (mRNA) molecules and inhibiting their translation into proteins.

[0011] While it is well known that the structure of the target nucleic acid affects the affinity of oligonucleotide hybridization, current methods for predicting target structures from the primary sequence fail to predict target regions accessible for oligonucleotide binding. Consequently, selection of oligonucleotides for antisense reagents or oligonucleotide probe arrays has been largely empirical. As most of the target sequence is sequestered by intramolecular base pairing and not accessible for oligonucleotide binding, the process of identifying good oligonucleotides has required large numbers of low efficiency experiments.

[0012] The design and implementation of algorithms that effectively predict the ability of oligonucleotides to rapidly and avidly bind to complementary nucleotide sequences has been an important problem in molecular biology since the invention of facile methods for chemical DNA synthesis. The subsequent inventions of the polymerase chain reaction (PCR), antisense inhibition of gene expression and oligonucleotide array methods for performing massively parallel hybridization experiments have made the need for effective predictive algorithms even more critical.

[0013] Previous attempts to solve the nucleic acid probe design problem include PCR primer design software applications (e.g., OLIGO®), neural networks, PCR primer design applications that search for sequences that possess minimal ability to cross-hybridize with other targets present in a sample (e.g., HYBsimulator™), and approaches that attempt to predict the efficiency of antisense sequence suppression of mRNA translation from a combination of predicted nucleic acid duplex melting temperature and predicted target strand structure. The methods that predict effective oligonucleotide primers for performing PCR from DNA templates work well for that application where relatively stringent conditions are employed. This is because PCR experimental design greatly simplifies the prediction problem: hybridization is performed at high temperature, at relatively low ionic strength and in the presence of a large molar excess of oligonucleotide. Under these conditions, the oligonucleotide and target secondary structures are relatively unimportant.

[0014] Unfortunately, these conditions do not apply to oligonucleotide arrays, which are usually hybridized under relatively non-denaturing conditions, or to antisense suppression of gene expression, which takes place in vivo. Oligonucleotide arrays can contain hundreds of thousands of different sequences and conditions are chosen to allow the oligonucleotide with the lowest melting temperature to hybridize efficiently. These “lowest common denominator” conditions are usually relatively non-denaturing and secondary structure constraints become significant. Accordingly, the above applications require new predictive methods that are capable of estimating the effects of oligonucleotide and target structure on hybridization efficiency. For these reasons, current algorithms for designing PCR primer oligonucleotides fail badly when applied to the problems of oligonucleotide array or antisense oligonucleotide design.

[0015] To date, the most effective approach for identifying oligonucleotides with good hybridization efficiency has been an empirical one. Such an approach involves the synthesis of large numbers of oligonucleotide probes for a given target nucleotide sequence. Arrays are formed that include the above oligonucleotide probes. Hybridization experiments are carried out to determine which of the oligonucleotide probes exhibit good hybridization efficiencies. Examples of such an approach are found in D. Lockhart, et al., Nature Biotech., infra, L. Wodicka, et al., Nature Biotechnology, infra., and N. Milner et al. Nature Biotech, infra. One major drawback to this approach is the vast number of oligonucleotides that must be synthesized in order to achieve a satisfactory result. Typically, about 2%-5% of the test probes synthesized yield acceptable signal levels.

[0016] The use of neural networks for oligonucleotide design has also been investigated. Neural networks are easily taught with real data; they therefore afford a general approach to many problems. However, their performance is limited by the “senses” that they are given. An analogy works best here: the human brain is an astoundingly capable neural network, but a blind person cannot be taught to reliably distinguish colors by smell. In addition, a large amount of data is required to adequately teach a neural network to perform its job well. A comprehensive database for either oligonucleotide array design or antisense suppression of gene expression has not been made available. For these reasons, the performance reported to-date of neural network solutions against the probe design problem is mediocre.

[0017] Finally, approaches that have attempted to use target nucleic acid folding calculations to predict experimental results inferred to depend upon hybridization efficiency (e.g. antisense suppression of mRNA translation) have so far only demonstrated that the predictions of current nucleic acid folding calculations correlate poorly with observed behavior. The probable reason for this is that the structures predicted by such programs for long sequences are poor predictors of chemical reality; the results of experiments that attempt to confirm the predictions of such calculations support this assessment. Recent improvements to this approach which use predicted RNA structure topology as a predictor of relative RNA/RNA association kinetics have been more successful at forecasting the results of antisense experiments. However, these methods are not computationally efficient, and have so far only been shown to work for targets less than 100 bases long. Such methods are therefore not yet capable of predicting the behavior of full-length mRNA targets, which are typically between 1,000 and 2,000 bases in length.

[0018] 2. Description of the Related Art

[0019] U.S. Pat. No. 5,512,438 (Ecker) discloses the inhibition of RNA expression by forming a pseudo-half knot RNA at the target's RNA secondary structure using antisense oligonucleotides.

[0020] Cook, et al., in U.S. Pat. No. 5,670,633 discuss sugar-modified oligonucleotides that detect and modulate gene expression.

[0021] Antisense oligonucleotide inhibition of the RAS gene is disclosed in U.S. Pat. No. 5,582,986 (Monia, et al.).

[0022] U.S. Pat. No. 5,593,834 (Lane, et al.) discusses a method of preparing DNA sequences with known ligand binding characteristics.

[0023] Mitsuhashi, et al., in U.S. Pat. No. 5,556,749 discusses a computerized method for designing optimal DNA probes and an oligonucleotide probe design station.

[0024] U.S. Pat. No. 5,081,584 (Omichinski, et al.) discloses a computer-assisted design of anti-peptides based on the amino acid sequence of a target peptide.

[0025] A PCR primer design application that searches for sequences that possess minimal ability to cross-hybridize with other targets present in a sample is available as HYBsimulator™, version 2.0, AGCT, Inc., 2102 Business Center Drive, Suite 170, Irvine, Calif. 92715 (714) 833-9983.

[0026] A PCR primer design software application is available as OLIGO®, version 5.0, National Biosciences, Inc., 3650 Annapolis Lane North, #140, Plymouth, Minn. 55447 (800) 747-4362.

[0027] D. J. Lockhart, et al., Nature Biotech. 14:1675-1684 (1996) describe a neural network approach to the selection of efficient surface-bound oligonucleotide probes.

[0028] M. Mitsuhashi, et al., Nature, 367:759-761 (1994) disclose a method for designing specific oligonucleotide probes and primers by modeling the potential cross-hybridization of candidate probes to non-target sequences known to be present in samples.

[0029] R. A. Stull, et al., Nuc. Acids Res., 20:3501-3508 (1992) describe a method of predicting the efficacy of antisense oligonucleotides, using predicted target secondary structure and predicted oligonucleotide/target binding free energy as input parameters.

[0030] N. Milner, et al., Nature Biotechnology, 15:537-541 (1997) compare observed patterns of probe hybridization to those expected from the predicted secondary structure of the nucleic acid target.

[0031] L. Wodicka, et al., Nature Biotechnology, 15:1359-1367 (1997) describe simple rules for avoiding inefficient and non-specific probes during design and synthesis of oligonucleotides arrays.

[0032] J. SantaLucia Jr., et al., Biochemistry, 35:3555 (1996) disclose parameters and methods for the calculation of thermodynamic properties of DNA/DNA homoduplexes.

[0033] N. Sugimoto, et al., Biochemistry, 34:11211 (1995) disclose parameters and methods for the calculation of thermodynamic properties of DNA/RNA heteroduplexes.

[0034] J. A. Jaeger, et al., Proc. Nati. Acad. Sci. USA, 86:7706 (1989) disclose methods for estimation of the free energy of the most stable intramolecular structure of a single-stranded polynucleotide, by means of a dynamic programming algorithm.

[0035] S. F. Altschul, et al., Nature Genetics, 6:119-129 (1994) disclose methods for calculating the complexity and information content of amino acid and nucleic acid sequences.

[0036] T. A. Weber and E. Helfand, J. Chem. Phys., 71, 4760 (1979) describe approaches for the modeling of polymer structures by molecular dynamics simulations.

[0037] V. Patzel and G. Sczakiel, Nature Biotech.,.16, 64-68 (1998) disclose methods for estimating rate constants for association of antisense RNA molecules with mRNA targets by examination of predicted antisense RNA secondary structures.

[0038] Light-generated oligonucleotide arrays for rapid DNA sequence analysis is described by A. C. Pease, et al., Proc. Nat. Acad. Sci. USA (1994) 91:5022-5026.

[0039] Mitsuhashi discusses basic requirements for designing optimal oligonucleotide probe sequences in J. Clinical Laboratory Analysis (1996) 10:277-284.

[0040] Rychlik, et al., discloses a computer program for choosing optimal oligonucleotides for filter hybridization, sequencing and in vitro amplification of DNA in Nucleic Acids Research (1989) 17(21):8543-8551.

[0041] A strategy for designing specific antisense oligonucleotide sequences is described by Mitsuhashi in J. Gastroenterol. (1997) 32:282-287.

[0042] Mitsuhashi discusses basic requirements for designing optimal PCR primers in J. Clinical Laboratory Analysis (1996) 10:285-293.

[0043] Hyndman, et al., disclose software to determine optimal oligonucleotide sequences based on hybridization simulation data in BioTechniques (1996) 20(6):1090-1094.

[0044] Eberhardt discloses a shell program for the design of PCR primers using genetics computer group (GCG) software (7.1) on VAX/VSM™ systems in BioTechniques (1992) 13(6):914-917.

[0045] Chen, et al., disclose a computer program for calculating the melting temperature of degenerate oligonucleotides used in PCR or hybridization in BioTechniques (1997) 22(6): 1158-1160.

[0046] Partial thermodynamic parameters for prediction stability and washing behavior of DNA duplexes immobilized on gel matrix is described by Kunitsyn, et al., in J. Biomolecular Structure & Dynamics, ISSN 0739-1102 (1996) 14(1):239-244.

SUMMARY OF THE INVENTION

[0047] One embodiment of the present invention is a method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. A predetermined set of unique oligonucleotide sequences is identified. The unique oligonucleotide sequences are chosen to sample the entire length of a nucleotide sequence that is hybridizable with the target nucleotide sequence. At least one parameter that is predictive of the ability of each of the oligonucleotides specified by the set of sequences to hybridize to the target nucleotide sequence is determined and evaluated for each of the above oligonucleotide sequences. A subset of oligonucleotide sequences within the predetermined set of unique oligonucleotide sequences is identified based on the examination of the parameter values. Finally, oligonucleotide sequences in the subset are identified that are clustered along one or more regions of the nucleotide sequence that is hybridizable to the target nucleotide sequence. The oligonucleotide probes corresponding to the identified sequences find use in polynucleotide assays particularly where the assays involve oligonucleotide arrays. For a discussion of oligonucleotide arrays, see, e.g., U.S. Pat. No. 5,700,637 (E. Southern) and U.S. Pat. No. 5,667,667 (E. Southern), the relevant disclosures of which are incorporated herein by reference.

[0048] Another embodiment of the present invention is a method for predicting the potential of an oligonucleotide to hybridize to a complementary target nucleotide sequence. A set of overlapping oligonucleotide sequences is identified based on a nucleotide sequence that is complementary to the target nucleotide sequence. At least two parameters that are independently predictive of the ability of each of the oligonucleotides specified by the oligonucleotide sequences to hybridize to the target nucleotide sequence are determined and evaluated for each of the oligonucleotide sequences. Independence is assured by requiring that the parameters be poorly correlated with respect to one another. A subset of oligonucleotide sequences within the set of oligonucleotide sequences is identified based on the examination of the parameter values. Finally, oligonucleotide sequences in the subset are identified that are clustered along one or more regions of the nucleotide sequence that is complementary to the target nucleotide sequence.

[0049] Another embodiment of the present invention is a method for predicting the potential of an oligonucleotide to hybridize to a complementary target nucleotide sequence. A set of overlapping oligonucleotide sequences is obtained based on a nucleotide sequence of length L, complementary to the target nucleotide sequence. The oligonucleotide sequences of the set of overlapping oligonucleotide sequences are of identical length N and spaced one nucleotide apart. The set comprises L−N+1 oligonucleotide sequences. Parameters are determined for each of the oligonucleotide sequences of the set of overlapping oligonucleotide sequences. One parameter is the predicted melting temperature of the duplex of each of the oligonucleotides specified by the oligonucleotide sequences and the target nucleotide sequence, corrected for salt concentration. The other parameter is the predicted free energy of the most stable intramolecular structure of each of the oligonucleotides specified by the oligonucleotide sequences at the temperature of hybridization of the oligonucleotide with the target nucleotide sequence. A subset of oligonucleotide sequences within the set of oligonucleotide sequences is selected based on an examination of the parameter values by establishing cut-off values for each of the parameters. Oligonucleotide sequences in the subset that are clustered along one or more regions of the complementary nucleotide sequence are ranked based on the sizes of the clusters of oligonucleotide sequences. Finally, a subset of the clustered oligonucleotide sequences is selected that statistically samples the clusters of oligonucleotide sequences. The selected sampled subset is used to specify the synthesis of oligonucleotides for experimental evaluation.

[0050] Another aspect of the present invention is a computer based method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. A predetermined number of unique oligonucleotides within a nucleotide sequence that is hybridizable with the target nucleotide sequence is identified under computer control. The oligonucleotides are chosen to sample the entire length of the nucleotide sequence. A value is determined and evaluated under computer control for each of the oligonucleotides for at least one parameter that is independently predictive of the ability of each of the oligonucleotides to hybridize to the target nucleotide sequence. The parameter values are stored. A subset of oligonucleotides within the predetermined number of unique oligonucleotides is identified by examination of the stored parameter values under computer control. Then, oligonucleotides in the subset that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence are identified under computer control.

[0051] Another aspect of the present invention is a computer system for conducting a method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. The system comprises (a) input means for introducing a target nucleotide sequence into the computer system, (b) means for determining a number of unique oligonucleotide sequences that are within a nucleotide sequence that is hybridizable with the target nucleotide sequence where the oligonucleotide sequences are chosen to sample the entire length of the nucleotide sequence, (c) memory means for storing the oligonucleotide sequences, (d) means for controlling the computer system to carry out for each of the oligonucleotide sequences a determination and evaluation of a value for at least one parameter that is independently predictive of the ability of each of the oligonucleotide sequences to hybridize to the target nucleotide sequence, (e) means for storing the parameter values, (f) means for controlling the computer to carry out an identification from the stored parameter values a subset of oligonucleotide sequences within the number of unique oligonucleotide sequences based on the examination of the parameter, (g) means for storing the subset of oligonucleotides, (h) means for controlling the computer to carry out an identification of oligonucleotide sequences in the subset that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence, (i) means for storing the oligonucleotide sequences in the subset, and (j) means for outputting data relating to the oligonucleotide sequences in the subset.

BRIEF DESCRIPTION OF THE DRAWINGS

[0052]
FIG. 1 is a general flow chart depicting the method of the present invention.

[0053]
FIG. 2 is a flow chart depicting a preferred embodiment of a method in accordance with the present invention.

[0054]
FIG. 3 is a contour plot of normalized hybridization intensity from multiple experiments, as a function of the free energy of the most stable probe intramolecular structure (ΔGMFOLD) and the difference between the predicted RNA/DNA heteroduplex melting temperature (Tm) and the temperature of hybridization (Thyb).

[0055]
FIG. 4 shows the observed hybridization patterns for oligonucleotides selected using a method in accordance with the present invention and additional oligonucleotides to a portion of the rabbit β-globin gene (radiolabeled antisense RNA target).

[0056]
FIG. 5 shows the observed hybridization patterns for oligonucleotides selected using a method in accordance with the present invention and additional oligonucleotides to the HIV PRT gene (fluorescein-labeled sense RNA target).

[0057]
FIG. 6 shows the observed hybridization patterns for oligonucleotides selected using a method in accordance with the present invention and additional oligonucleotides to the G3PDH gene (fluorescein-labeled antisense RNA target).

[0058]
FIG. 7 shows the observed hybridization patterns for oligonucleotides selected using a method in accordance with the present invention and additional oligonucleotides to the p53 gene (fluorescein-labeled antisense RNA target).

[0059]
FIG. 8 shows the observed hybridization patterns for oligonucleotides selected using a method in accordance with the present invention and additional oligonucleotides to the HIV PRTs gene (using data from the GeneChip™ data).

DEFINITIONS

[0060] Before proceeding further with a description of the specific embodiments of the present invention, a number of terms will be defined.

[0061] Nucleic Acids:

[0062] Polynucleotide—a compound or composition that is a polymeric nucleotide or nucleic acid polymer. The polynucleotide may be a natural compound or a synthetic compound. In the context of an assay, the polynucleotide is often referred to as a polynucleotide analyte. The polynucleotide can have from about 20 to 5,000,000 or more nucleotides. The larger polynucleotides are generally found in the natural state. In an isolated state the polynucleotide can have about 30 to 50,000 or more nucleotides, usually about 100 to 20,000 nucleotides, more frequently 500 to 10,000 nucleotides. It is thus obvious that isolation of a polynucleotide from the natural state often results in fragmentation. The polynucleotides include nucleic acids, and fragments thereof, from any source in purified or unpurified form including DNA (dsDNA and ssDNA) and RNA, including tRNA, mRNA, rRNA, mitochondrial DNA and RNA, chloroplast DNA and RNA, DNA/RNA hybrids, or mixtures thereof, genes, chromosomes, plasmids, the genomes of biological material such as microorganisms, e.g., bacteria, yeasts, viruses, viroids, molds, fungi, plants, animals, humans, and the like. The polynucleotide can be only a minor fraction of a complex mixture such as a biological sample. Also included are genes, such as hemoglobin gene for sickle-cell anemia, cystic fibrosis gene, oncogenes, cDNA, and the like.

[0063] The polynucleotide can be obtained from various biological materials by procedures well known in the art. The polynucleotide, where appropriate, may be cleaved to obtain a fragment that contains a target nucleotide sequence, for example, by shearing or by treatment with a restriction endonuclease or other site specific chemical cleavage method.

[0064] For purposes of this invention, the polynucleotide, or a cleaved fragment obtained from the polynucleotide, will usually be at least partially denatured or single stranded or treated to render it denatured or single stranded. Such treatments are well known in the art and include, for instance, heat or alkali treatment, or enzymatic digestion of one strand. For example, dsDNA can be heated at 90-100° C. for a period of about 1 to 10 minutes to produce denatured material.

[0065] Target nucleotide sequence—a sequence of nucleotides to be identified, usually existing within a portion or all of a polynucleotide, usually a polynucleotide analyte. The identity of the target nucleotide sequence generally is known to an extent sufficient to allow preparation of various sequences hybridizable with the target nucleotide sequence and of oligonucleotides, such as probes and primers, and other molecules necessary for conducting methods in accordance with the present invention, an amplification of the target polynucleotide, and so forth.

[0066] The target sequence usually contains from about 30 to 5,000 or more nucleotides, preferably 50 to 1,000 nucleotides. The target nucleotide sequence is generally a fraction of a larger molecule or it may be substantially the entire molecule such as a polynucleotide as described above. The minimum number of nucleotides in the target nucleotide sequence is selected to assure that the presence of a target polynucleotide in a sample is a specific indicator of the presence of polynucleotide in a sample. The maximum number of nucleotides in the target nucleotide sequence is normally governed by several factors: the length of the polynucleotide from which it is derived, the tendency of such polynucleotide to be broken by shearing or other processes during isolation, the efficiency of any procedures required to prepare the sample for analysis (e.g. transcription of a DNA template into RNA) and the efficiency of detection and/or amplification of the target nucleotide sequence, where appropriate.

[0067] Oligonucleotide—a polynucleotide, usually single stranded, usually a synthetic polynucleotide but may be a naturally occurring polynucleotide. The oligonucleotide(s) are usually comprised of a sequence of at least 5 nucleotides, preferably, 10 to 100 nucleotides, more preferably, 20 to 50 nucleotides, and usually 10 to 30 nucleotides, more preferably, 20 to 30 nucleotides, and desirably about 25 nucleotides in length.

[0068] Various techniques can be employed for preparing an oligonucleotide. Such oligonucleotides can be obtained by biological synthesis or by chemical synthesis. For short sequences (up to about 100 nucleotides), chemical synthesis will frequently be more economical as compared to the biological synthesis. In addition to economy, chemical synthesis provides a convenient way of incorporating low molecular weight compounds and/or modified bases during a specific synthesis steps. Furthermore, chemical synthesis is very flexible in the choice of length and region of the target polynucleotide binding sequence. The oligonucleotide can be synthesized by standard methods such as those used in commercial automated nucleic acid synthesizers. Chemical synthesis of DNA on a suitably modified glass or resin can result in DNA covalently attached to the surface. This may offer advantages in washing and sample handling. For longer sequences standard replication methods employed in molecular biology can be used such as the use of M13 for single stranded DNA as described by J. Messing (1983) Methods Enzymol, 101:20-78.

[0069] Other methods of oligonucleotide synthesis include phosphotriester and phosphodiester methods (Narang, et al. (1979) Meth. Enzymol 68:90) and synthesis on a support (Beaucage, et al. (1981) Tetrahedron Letters 22:1859-1862) as well as phosphoramidite techniques (Caruthers, M. H., et al., “Methods in Enzymology,” Vol. 154, pp. 287-314 (1988)) and others described in “Synthesis and Applications of DNA and RNA,” S. A. Narang, editor, Academic Press, New York, 1987, and the references contained therein. The chemical synthesis via a photolithographic method of spatially addressable arrays of oligonucleotides bound to glass surfaces is described by A. C. Pease, et al., Proc. Nat. Acad. Sci. USA (1994) 91:5022-5026.

[0070] Oligonucleotide probe—an oligonucleotide employed to bind to a portion of a polynucleotide such as another oligonucleotide or a target nucleotide sequence. The design and preparation of the oligonucleotide probes are generally dependent upon the sensitivity and specificity required, the sequence of the target polynucleotide and, in certain cases, the biological significance of certain portions of the target polynucleotide sequence.

[0071] Oligonucleotide primer(s)—an oligonucleotide that is usually employed in a chain extension on a polynucleotide template such as in, for example, an amplification of a nucleic acid. The oligonucleotide primer is usually a synthetic nucleotide that is single stranded, containing a sequence at its 3′-end that is capable of hybridizing with a defined sequence of the target polynucleotide. Normally, an oligonucleotide primer has at least 80%, preferably 90%, more preferably 95%, most preferably 100%, complementarity to a defined sequence or primer binding site. The number of nucleotides in the hybridizable sequence of an oligonucleotide primer should be such that stringency conditions used to hybridize the oligonucleotide primer will prevent excessive random non-specific hybridization. Usually, the number of nucleotides in the oligonucleotide primer will be at least as great as the defined sequence of the target polynucleotide, namely, at least ten nucleotides, preferably at least 15 nucleotides, and generally from about 10 to 200, preferably 20 to 50, nucleotides.

[0072] In general, in primer extension, amplification primers hybridize to, and are extended along (chain extended), at least the target nucleotide sequence within the target polynucleotide and, thus, the target sequence acts as a template. The extended primers are chain “extension products.” The target sequence usually lies between two defined sequences but need not. In general, the primers hybridize with the defined sequences or with at least a portion of such target polynucleotide, usually at least a ten-nucleotide segment at the 3′-end thereof and preferably at least 15, frequently a 20 to 50 nucleotide segment thereof.

[0073] Nucleoside triphosphates—nucleosides having a 5′-triphosphate substituent. The nucleosides are pentose sugar derivatives of nitrogenous bases of either purine or pyrimidine derivation, covalently bonded to the 1′-carbon of the pentose sugar, which is usually a deoxyribose or a ribose. The purine bases include adenine (A), guanine (G), inosine (I), and derivatives and analogs thereof. The pyrimidine bases include cytosine (C), thymine (T), uracil (U), and derivatives and analogs thereof. Nucleoside triphosphates include deoxyribonucleoside triphosphates such as the four common deoxyribonucleoside triphosphates dATP, dCTP, dGTP and dTTP and ribonucleoside triphosphates such as the four common triphosphates rATP, rCTP, rGTP and rUTP.

[0074] The term “nucleoside triphosphates” also includes derivatives and analogs thereof, which are exemplified by those derivatives that are recognized and polymerized in a similar manner to the underivatized nucleoside triphosphates.

[0075] Nucleotide—a base-sugar-phosphate combination that is the monomeric unit of nucleic acid polymers, i.e., DNA and RNA. The term “nucleotide” as used herein includes modified nucleotides as defined below.

[0076] DNA—deoxyribonucleic acid.

[0077] RNA—ribonucleic acid.

[0078] Modified nucleotide—a unit in a nucleic acid polymer that contains a modified base, sugar or phosphate group. The modified nucleotide can be produced by a chemical modification of the nucleotide either as part of the nucleic acid polymer or prior to the incorporation of the modified nucleotide into the nucleic acid polymer. For example, the methods mentioned above for the synthesis of an oligonucleotide may be employed. In another approach a modified nucleotide can be produced by incorporating a modified nucleoside triphosphate into the polymer chain during an amplification reaction. Examples of modified nucleotides, by way of illustration and not limitation, include dideoxynucleotides, derivatives or analogs that are biotinylated, amine modified, alkylated, fluorophore-labeled, and the like and also include phosphorothioate, phosphite, ring atom modified derivatives, and so forth.

[0079] Nucleoside—is a base-sugar combination or a nucleotide lacking a phosphate moiety.

[0080] Nucleotide polymerase—a catalyst, usually an enzyme, for forming an extension of a polynucleotide along a DNA or RNA template where the extension is complementary thereto. The nucleotide polymerase is a template dependent polynucleotide polymerase and utilizes nucleoside triphosphates as building blocks for extending the 3′-end of a polynucleotide to provide a sequence complementary with the polynucleotide template. Usually, the catalysts are enzymes, such as DNA polymerases, for example, prokaryotic DNA polymerase (I, II, or III), T4 DNA polymerase, T7 DNA polymerase, Klenow fragment, reverse transcriptase, Vent DNA polymerase, Pfu DNA polymerase, Taq DNA polymerase, and the like, or RNA polymerases, such as T3 and T7 RNA polymerases. Polymerase enzymes may be derived from any source such as cells, bacteria such as E. coli, plants, animals, virus, thermophilic bacteria, and so forth.

[0081] Amplification of nucleic acids or polynucleotides—any method that results in the formation of one or more copies of a nucleic acid or polynucleotide molecule (exponential amplification) or in the formation of one or more copies of only the complement of a nucleic acid or polynucleotide molecule (linear amplification).

[0082] Hybridization (hybridizing) and binding—in the context of nucleotide sequences these terms are used interchangeably herein. The ability of two nucleotide sequences to hybridize with each other is based on the degree of complementarity of the two nucleotide sequences, which in turn is based on the fraction of matched complementary nucleotide pairs. The more nucleotides in a given sequence that are complementary to another sequence, the more stringent the conditions can be for hybridization and the more specific will be the binding of the two sequences. Increased stringency is achieved by elevating the temperature, increasing the ratio of co-solvents, lowering the salt concentration, and the like.

[0083] Hybridization efficiency—the productivity of a hybridization reaction, measured as either the absolute or relative yield of oligonucleotide probe/polynucleotide target duplex formed under a given set of conditions in a given amount of time.

[0084] Homologous or substantially identical polynucleotides—In general, two polynucleotide sequences that are identical or can each hybridize to the same polynucleotide sequence are homologous. The two sequences are homologous or substantially identical where the sequences each have at least 90%, preferably 100%, of the same or analogous base sequence where thymine (T) and 30 uracil (U) are considered the same. Thus, the ribonucleotides A, U, C and G are taken as analogous to the deoxynucleotides dA, dT, dC, and dG, respectively. Homologous sequences can both be DNA or one can be DNA and the other RNA.

[0085] Complementary—Two sequences are complementary when the sequence of one can bind to the sequence of the other in an anti-parallel sense wherein the 3′-end of each sequence binds to the 5′-end of the other sequence and each A, T(U), G, and C of one sequence is then aligned with a T(U), A, C, and G, respectively, of the other sequence. RNA sequences can also include complementary G/U or U/G basepairs.

[0086] Member of a specific binding pair (“sbp member”)—one of two different molecules, having an area on the surface or in a cavity that specifically binds to and is thereby defined as complementary with a particular spatial and polar organization of the other molecule. The members of the specific binding pair are referred to as cognates or as ligand and receptor (antiligand). These may be members of an immunological pair such as antigen-antibody, or may be operator-repressor, nuclease-nucleotide, biotin-avidin, hormones-hormone receptors, nucleic acid duplexes, IgG-protein A, DNA-DNA, DNA-RNA, and the like.

[0087] Ligand—any compound for which a receptor naturally exists or can be prepared.

[0088] Receptor (“antiligand”)—any compound or composition capable of recognizing a particular spatial and polar organization of a molecule, e.g., epitopic or determinant site. Illustrative receptors include naturally occurring receptors, e.g., thyroxine binding globulin, antibodies, enzymes, Fab fragments, lectins, nucleic acids, repressors, protection enzymes, protein A, complement component C1q, DNA binding proteins or ligands and the like.

[0089] Oligonucleotide Properties:

[0090] Potential of an oligonucleotide to hybridize—the combination of duplex formation rate and duplex dissociation rate that determines the amount of duplex nucleic acid hybrid that will form under a given set of experimental conditions in a given amount of time.

[0091] Parameter—a factor that provides information about the hybridization of an oligonucleotide with a target nucleotide sequence. Generally, the factor is one that is predictive of the ability of an oligonucleotide to hybridize with a target nucleotide sequence. Such factors include composition factors, thermodynamic factors, chemosynthetic efficiencies, kinetic factors, and the like.

[0092] Parameter predictive of the ability to hybridize—a parameter calculated from a set of oligonucleotide sequences wherein the parameter positively correlates with observed hybridization efficiencies of those sequences. The parameter is, therefore, predictive of the ability of those sequences to hybridize. “Positive correlation” can be rigorously defined in statistical terms. The correlation coefficient ρx,y of two experimentally measured discreet quantities x and y (N values in each set) is defined as

ρ_{x, y} = \frac{C o v a r i a n c e (x, y)}{\sqrt{V a r i a n c e (x) V a r i a n c e (y)}},

[0093] where the Covariance (x,y) is defined by

C o v a r i a n c e (x, y) = \frac{1}{N} \sum_{j = 1}^{N} (x_{j} - μ_{x}) (y_{j} - μ_{y}) .

[0094] The quantities μx and μy are the averages of the quantities x and y, while the variances are simply the squares of the standard deviations (defined below). The correlation coefficient is a dimensionless (unitless) quantity between −1 and 1. A correlation coefficient of 1 or −1 indicates that x and y have a linear relationship with a positive or negative slope, respectively. A correlation coefficient of zero indicates no relationship; for example, two sets of random numbers will yield a correlation coefficient near zero. Intermediate correlation coefficients indicate intermediate degrees of relatedness between two sets of numbers. The correlation coefficient is a good statistical measure of the degree to which one set of numbers predicts a second set of numbers.

[0095] Composition factor—a numerical factor based solely on the composition or sequence of an oligonucleotide without involving additional parameters, such as experimentally measured nearest-neighbor thermodynamic parameters. For instance, the fraction (G+C), given by the formula

f_{G C} = \frac{n_{G} + n_{C}}{n_{G} + n_{C} + n_{A} + n_{T o r U}},

[0096] where nG, nC, nA and nT or U are the numbers of G, C, A and T (or U) bases in an oligonucleotide, is an example of a composition factor. Examples of composition factors, by way of illustration and not limitation, are mole fraction (G+C), percent (G+C), sequence complexity, sequence information content, frequency of occurrence of specific oligonucleotide sequences in a sequence database and so forth.

[0097] Thermodynamic factor—numerical factors that predict the behavior of an oligonucleotide in some process that has reached equilibrium. For instance, the free energy of duplex formation between an oligonucleotide and its complement is a thermodynamic factor. Thermodynamic factors for systems that can be subdivided into constituent parts are often estimated by summing contributions from the constituent parts. Such an approach is used to calculate the thermodynamic properties of oligonucleotides.

[0098] Examples of thermodynamic factors, by way of illustration and not limitation, are predicted duplex melting temperature, predicted enthalpy of duplex formation, predicted entropy of duplex formation, free energy of duplex formation, predicted melting temperature of the most stable intramolecular structure of the oligonucleotide or its complement, predicted enthalpy of the most stable intramolecular structure of the oligonucleotide or its complement, predicted entropy of the most stable intramolecular structure of the oligonucleotide or its complement, predicted free energy of the most stable intramolecular structure of the oligonucleotide or its complement, predicted melting temperature of the most stable hairpin structure of the oligonucleotide or its complement, predicted enthalpy of the most stable hairpin structure of the oligonucleotide or its complement, predicted entropy of the most stable hairpin structure of the oligonucleotide or its complement, predicted free energy of the most stable hairpin structure of the oligonucleotide or its complement, thermodynamic partition function for intramolecular structure of the oligonucleotide or its complement and the like.

[0099] Chemosynthetic efficiency—oligonucleotides and nucleotide sequences may both be made by sequential polymerization of the constituent nucleotides. However, the individual addition steps are not perfect; they instead proceed with some fractional efficiency that is less than unity. This may vary as a function of position in the sequence. Therefore, what is really produced is a family of molecules that consists of the desired molecule plus many truncated sequences. These “failure sequences” affect the observed efficiency of hybridization between an oligonucleotide and its complementary target. Examples of chemosynthetic efficiency factors, by way of illustration and not limitation, are coupling efficiencies, overall efficiencies of the synthesis of a target nucleotide sequence or an oligonucleotide probe, and so forth.

[0100] Kinetic factor—numerical factors that predict the rate at which an oligonucleotide hybridizes to its complementary sequence or the rate at which the hybridized sequence dissociates from its complement are called kinetic factors. Examples of kinetic factors are steric factors calculated via molecular modeling or measured experimentally, rate constants calculated via molecular dynamics simulations, associative rate constants, dissociative rate constants, enthalpies of activation, entropies of activation, free energies of activation, and the like.

[0101] Predicted duplex melting temperature—the temperature at which an oligonucleotide mixed with a hybridizable nucleotide sequence is predicted to form a duplex structure (double-helix hybrid) with 50% of the hybridizable sequence. At higher temperatures, the amount of duplex is less than 50%; at lower temperatures, the amount of duplex is greater than 50%. The melting temperature Tm (°C.) is calculated from the enthalpy (ΔH), entropy (ΔS) and C, the concentration of the most abundant duplex component (for hybridization arrays, the soluble hybridization target), using the equation

T_{m} = \frac{Δ H}{Δ S + R l n C} - 273.15,

[0102] where R is the gas constant, 1.987 cal/(mole-°K.). For longer sequences (>100 nucleotides), Tm can also be estimated from the mole fraction (G+C), χG+C, using the equation

T

m
=81.5+41.0 χG+C.

[0103] Melting temperature corrected for salt concentration—polynucleotide duplex melting temperatures are calculated with the assumption that the concentration of sodium ion, Na+, is 1 M. Melting temperatures T′m calculated for duplexes formed at different salt concentrations are corrected via the semi-empirical equation

T′

m
([Na+])=Tm+16.6 log([Na+]).

[0104] Predicted enthalpy, entropy and free energy of duplex formation—the enthalpy (ΔH), entropy and free energy (ΔG) are thermodynamic state functions, related by the equation

ΔG=ΔH−T ΔS,

[0105] where T is the temperature in °K. In practice, the enthalpy and entropy are predicted via a thermodynamic model of duplex formation (the “nearest neighbor” model which is explained in more detail below), and used to calculate the free energy and melting temperature.

[0106] Predicted free energy of the most stable intramolecular structure of an oligonucleotide or its complement—single-stranded DNA and RNA molecules that contain self-complementary sequences can form intramolecular secondary structures. For instance, the oligonucleotide

15′-ACTGGCAATCACAATTGCCAGTAA-3′ (SEQ ID NO:1)

[0107] can base pair with itself, to form the structure

25′-ACTGGCAATCA (SEQ ID NO: 1) ||||||||| C3′-AATGACCGTTAA

[0108] where a vertical line indicates Watson-Crick base pair formation. Many such structures are possible for a given sequence; two are of particular interest. The first is the lowest energy “hairpin” structure (formed by folding a sequence back on itself with a connecting loop at least 3 nucleotides long). The second is the lowest energy structure that can be formed by including more complex topologies, such as “bulge loops” (unpaired duplexes between two regions of base-paired duplex) and cloverleaf structures, where 3 base-paired stretches meet at a triple-junction. A good example of a complex secondary structure is the structure of a tRNA molecule, an example of which, namely, yeast tRNAAla is shown below.

[0109] For either type of structure, a value of the free energy of that structure can be calculated, relative to the unpaired strand, by means of a thermodynamic model similar to that used to calculate the free energy of a base-paired duplex structure. Again, the free energy ΔG is calculated from the enthalpy ΔH and the entropy ΔS at a given absolute temperature T via the equation

ΔG=ΔH−T ΔS.

[0110] However, in this case there is the added difficulty that the lowest energy structure must be found. For a simple hairpin structure, this optimization can be performed via a relatively simple search algorithm. For more complex structures (such as a cloverleaf) a dynamic programming algorithm, such as that implemented in the program MFOLD, must be used.

[0111] Yeast tRNAAla—The RNA sequence includes many non-standard ribonucleotides, such as D (5,6 dihydrouridine), m1G (1-methylguanosine), m2G (N2-dimethylguanosine), ψ(pseudouridine), I (inosine), m1 (1-methylinosine) and T (ribothymidine). Dots (•) mark (non-standard) G=U base pairs. The structure is taken from A. L. Lehninger, et al., Principles of Biochemistry, 2nd Ed. (Worth Publishers, New York, N.Y., 1993).

3 3′(SEQ ID NO:2) / A C 5′ C \ A pG-C G-C G·U C-G G-C U U G-C UU DG U AGGCC AC AUGCGm1G ||||| G ·||| UCCGG CG AGCGC C Tψ GD m2G D C-GAG U-A C-G C-G C-G U ψ U m1I I C G

[0112] Coupling efficiencies—chemosynthetic efficiencies are called coupling efficiencies when the synthetic scheme involves successive attachment of different monomers to a growing oligomer; a good example is oligonucleotide synthesis via phosphoramidite coupling chemistry.

[0113] Algorithmic Operations:

[0114] Evaluating a parameter—determination of the numerical value of a numerical descriptor of a property of an oligonucleotide sequence by means of a formula, algorithm or look-up table.

[0115] Filter—a mathematical rule or formula that divides a set of numbers into two subsets. Generally, one subset is retained for further analysis while the other is discarded. If the division into two subsets is achieved by testing the numbers against a simple inequality, then the filter is referred to as a “cut-off”. In the context of the current invention, an example by way of illustration and not limitation is the statement “The predicted self structure free energy must be greater than or equal to −0.4 kcal/mole,” which can be used as a filter for oligonucleotide sequences; this particular filter is also an example of a cut-off.

[0116] Filter set—A set of rules or formulae that successively winnow a set of numbers by identifying and discarding subsets that do not meet specific criteria. In the context of the current invention, an example by way of illustration and not limitation is the compound statement “the predicted self structure free energy must be greater than or equal to −0.4 kcal/mole and the predicted RNA/DNA heteroduplex melting temperature must lie between 60° C. and 85° C.,” which can be used as a filter set for oligonucleotide sequences.

[0117] Examining a parameter—comparing the numerical value of a parameter to some cutoff-value or filter.

[0118] Statistical sampling of a cluster—extraction of a subset of oligonucleotides from a cluster of oligonucleotides based upon some statistical measure, such as rank by oligonucleotide starting position in the sequence complementary to the target sequence.

[0119] First quartile, median and third quartile—If a set of numbers is ranked by value, then the value that divides the lower ¼ from the upper ¾ of the set is the first quartile, the value that divides the set in half is the median and the value that divides the lower ¾ from the upper ¼ of the set is the third quartile.

[0120] Poorly correlated—If it is not possible to perform a “good” prediction, as defined via statistics, of one set of numbers from another set of numbers using a simple linear model, then the two sets of numbers are said to be poorly correlated.

[0121] Computer program—a written set of instructions that symbolically instructs an appropriately configured computer to execute an algorithm that will yield desired outputs from some set of inputs. The instructions may be written in one or several standard programming languages, such as C, C++, Visual BASIC, FORTRAN or the like. Alternatively, the instructions may be written by imposing a template onto a general-purpose numerical analysis program, such as a spreadsheet.

[0122] Experimental System Components:

[0123] Small organic molecule—a compound of molecular weight less than 1500, preferably 100 to 1000, more preferably 300 to 600 such as biotin, fluorescein, rhodamine and other dyes, tetracycline and other protein binding molecules, and haptens, etc. The small organic molecule can provide a means for attachment of a nucleotide sequence to a label or to a support.

[0124] Support or surface—a porous or non-porous water insoluble material. The surface can have any one of a number of shapes, such as strip, plate, disk, rod, particle, including bead, and the like. The support can be hydrophilic or capable of being rendered hydrophilic and includes inorganic powders such as glass, silica, magnesium sulfate, and alumina; natural polymeric materials, particularly cellulosic materials and materials derived from cellulose, such as fiber containing papers, e.g., filter paper, chromatographic paper, etc.; synthetic or modified naturally occurring polymers, such as nitrocellulose, cellulose acetate, poly (vinyl chloride), polyacrylamide, cross linked dextran, agarose, polyacrylate, polyethylene, polypropylene, poly(4-methylbutene), polystyrene, polymethacrylate, poly(ethylene terephthalate), nylon, poly(vinyl butyrate), etc.; either used by themselves or in conjunction with other materials; glass available as Bioglass, ceramics, metals, and the like. Natural or synthetic assemblies such as liposomes, phospholipid vesicles, and cells can also be employed.

[0125] Binding of oligonucleotides to a support or surface may be accomplished by well-known techniques, commonly available in the literature. See, for example, A. C. Pease, et al., Proc. Nat. Acad. Sci. USA, 91:5022-5026 (1994).

[0126] Label—a member of a signal producing system. Usually the label is part of a target nucleotide sequence or an oligonucleotide probe, either being conjugated thereto or otherwise bound thereto or associated therewith. The label is capable of being detected directly or indirectly. Labels include (i) reporter molecules that can be detected directly by virtue of generating a signal, (ii) specific binding pair members that may be detected indirectly by subsequent binding to a cognate that contains a reporter molecule, (iii) oligonucleotide primers that can provide a template for amplification or ligation or (iv) a specific polynucleotide sequence or recognition sequence that can act as a ligand such as for a repressor protein, wherein in the latter two instances the oligonucleotide primer or repressor protein will have, or be capable of having, a reporter molecule. In general, any reporter molecule that is detectable can be used.

[0127] The reporter molecule can be isotopic or nonisotopic, usually non-isotopic, and can be a catalyst, such as an enzyme, a polynucleotide coding for a catalyst, promoter, dye, fluorescent molecule, chemiluminescent molecule, coenzyme, enzyme substrate, radioactive group, a small organic molecule, amplifiable polynucleotide sequence, a particle such as latex or carbon particle, metal sol, crystallite, liposome, cell, etc., which may or may not be further labeled with a dye, catalyst or other detectable group, and the like. The reporter molecule can be a fluorescent group such as fluorescein, a chemiluminescent group such as luminol, a terbium chelator such as N-(hydroxyethyl) ethylenediaminetriacetic acid that is capable of detection by delayed fluorescence, and the like.

[0128] The label is a member of a signal producing system and can generate a detectable signal either alone or together with other members of the signal producing system. As mentioned above, a reporter molecule can be bound directly to a nucleotide sequence or can become bound thereto by being bound to an sbp member complementary to an sbp member that is bound to a nucleotide sequence. Examples of particular labels or reporter molecules and their detection can be found in U.S. Pat. No. 5,508,178 issued Apr. 16, 1996, at column 11, line 66, to column 14, line 33, the relevant disclosure of which is incorporated herein by reference. When a reporter molecule is not conjugated to a nucleotide sequence, the reporter molecule may be bound to an sbp member complementary to an sbp member that is bound to or part of a nucleotide sequence.

[0129] Signal Producing System—the signal producing system may have one or more components, at least one component being the label. The signal producing system generates a signal that relates to the presence or amount of a target polynucleotide in a medium. The signal producing system includes all of the reagents required to produce a measurable signal. Other components of the signal producing system may be included in a developer solution and can include substrates, enhancers, activators, chemiluminescent compounds, cofactors, inhibitors, scavengers, metal ions, specific binding substances required for binding of signal generating substances, and the like. Other components of the signal producing system may be coenzymes, substances that react with enzymic products, other enzymes and catalysts, and the like. The signal producing system provides a signal detectable by external means, by use of electromagnetic radiation, desirably by visual examination. Signal-producing systems that may be employed in the present invention are those described more fully in U.S. Pat. No. 5,508,178, the relevant disclosure of which is incorporated herein by reference.

[0130] Ancillary Materials—Various ancillary materials will frequently be employed in the methods and assays utilizing oligonucleotide probes designed in accordance with the present invention. For example, buffers and salts will normally be present in an assay medium, as well as stabilizers for the assay medium and the assay components. Frequently, in addition to these additives, proteins may be included, such as albumins, organic solvents such as formamide, quaternary ammonium salts, polycations such as spermine, surfactants, particularly non-ionic surfactants, binding enhancers, e.g., polyalkylene glycols, or the like.

DETAILED DESCRIPTION OF THE INVENTION

[0131] The invention is directed to methods or algorithms for predicting oligonucleotides specific for a nucleic acid target where the oligonucleotides exhibit a high potential for hybridization. The algorithm uses parameters of the oligonucleotide and the oligonucleotide/target nucleotide sequence duplex, which can be readily predicted from the primary sequences of the target polynucleotide and candidate oligonucleotides. In the methods of the present invention, oligonucleotides are filtered based on one or more of these parameters, then further filtered based on the sizes of clusters of oligonucleotides along the input polynucleotide sequence. The methods or algorithms of the present invention may be carried out using either relatively simple user-written subroutines or publicly available stand-alone software applications (e.g., dynamic programming algorithm for calculating self-structure free energies of oligonucleotides). The parameter calculations may be orchestrated and the filtering algorithms may be implemented using any of a number of commercially available computer programs as a framework such as, e.g., Microsoft® Excel spreadsheet, Microsoft® Access relational database and the like. The basic steps involved in the present methods involve parsing a sequence that is complementary to a target nucleotide sequence into a set of overlapping oligonucleotide sequences, evaluating one or more parameters for each of the oligonucleotide sequences, said parameter or parameters being predictive of probe hybridization to the target nucleotide sequence, filtering the oligonucleotide sequences based on the values for each parameter, filtering the oligonucleotide sequences based on the length of contiguous sequence elements and ranking the contiguous sequence elements based on their length. We have found that oligonucleotides in the longest contiguous sequence elements generally show the highest hybridization efficiencies.

[0132] The present methods are based on our recognition that oligonucleotides showing high hybridization efficiencies tend to form clusters. It is believed that this clustering reflects local regions of the target nucleotide sequence that are unstructured and accessible for oligonucleotide binding. Oligonucleotides that are contiguous along a region of the input nucleic acid sequence are identified. These oligonucleotides are sorted based on the length of the contiguous sequence elements. The sorting approach used in the present invention apparently serves as a surrogate for the calculation of local secondary structure of the target nucleotide sequence. This is supported by our observation that treatments intended to eliminate long-range nucleic acid structure (e.g., random fragmentation) do not eliminate the differences in hybridization yields across oligonucleotide probe arrays. This implies that major determinants of efficient hybridization are local regions of the target sequence. The identification of contiguous sequence elements is a simple and efficient method for recognizing clusters of such determinants and, thus, for identifying oligonucleotide probes that exhibit high hybridization efficiency for a target nucleotide sequence.

[0133] As mentioned above one embodiment of the present invention is a method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. A predetermined number of unique oligonucleotides is identified. The length of the oligonucleotides may be the same or different. The oligonucleotides are unique in that no two of the oligonucleotides are identical. The unique oligonucleotides are chosen to sample the entire length of a nucleotide sequence that is hybridizable with the target nucleotide sequence. The actual number of oligonucleotides is generally determined by the length of the nucleotide sequence and the desired result. The number of oligonucleotides should be sufficient to achieve a consensus behavior. In other words, the oligonucleotide sequences should be sufficiently numerous that several possible probes overlap or fall within a given region that is expected to yield acceptable hybridization efficiency. Since the location of these regions is not known before hand, the best strategy is to equally space the probe sequences along the sequence that is hybridizable to the target sequence. Since regions of acceptable hybridization efficiency are generally on the order of 20 nucleotides in length, a practical strategy is to space the starting nucleotides of the oligonucleotide sequences no more than five basepairs apart. If computation time needed to calculate the predictive parameters is not an issue, then the best strategy is to space the starting nucleotides one nucleotide apart. An important feature of the present invention is to determine oligonucleotides that are clustered along a region of the nucleotide sequence. The individual predictions made for individual oligonucleotide sequences are not very good. However, we have found that the predictions that are experimentally observed tend to form contiguous clusters, while the spurious predictions tend to be solitary. Thus, the number of oligonucleotides should be sufficient to achieve the desired clustering.

[0134] Preferably, a set of overlapping sequences is chosen. To this end, the subsequences are chosen so that there is overlap of at least one nucleotide from one oligonucleotide to the next. More preferably, the overlap is two or more nucleotides. Most preferably, the oligonucleotides are spaced one nucleotide apart and the predetermined number is L-N+1 oligonucleotides where L is the length of the nucleotide sequence and N is the length of the oligonucleotides. In the latter situation, the unique oligonucleotides are of identical length N. Thus, a set of overlapping oligonucleotides is a set of oligonucleotides that are subsequences derived from some master sequence by subdividing that sequence in such a way that each subsequence contains either the start or end of at least one other subsequence in the set.

[0135] An example of the above for purposes of illustration and not limitation is presented by the sequence ATGGACTTAGCATTCG (SEQ ID NO:3), from which the following set of overlapping oligonucleotides can be identified:

4ATGGACTTAGCA (SEQ ID NO:4) TGGACTTAGCAT (SEQ ID NO:5) GGACTTAGCATT (SEQ ID NO:6) GACTTAGCATTC (SEQ ID NO:7) ACTTAGCATTCG (SEQ ID NO:8)

[0136] In this example the overlapping oligonucleotides are spaced one nucleotide apart. In other words, there is overlap of all but one nucleotide from one oligonucleotide to the next. In the example above, the original nucleotide sequence is 16 nucleotides long (L=16). The length of each of the overlapping oligonucleotides is 12 nucleotides long (N=12) and there are L−N+1=5 oligonucleotides.

[0137] The length of the oligonucleotides may be the same or different and may vary depending on the length of the nucleotide sequence. The length of the oligonucleotides is determined by a practical compromise between the limits of current chemistries for oligonucleotide synthesis and the need for longer oligonucleotides, which exhibit greater binding affinity for the target sequence and are more likely to occur only once in complicated mixtures of polynucleotide targets. Usually, the length of the oligonucleotides is from about 10 to 50 nucleotides, more usually, from about 25 to 35 nucleotides.

[0138] In the next step of the method at least one parameter that is independently predictive of the ability of each of the oligonucleotides of the set to hybridize to the target nucleotide sequence is determined and evaluated for each of the above oligonucleotides. Examples of such a parameter, by way of illustration and not limitation, is a parameter selected from the group consisting of composition factors, thermodynamic factors, chemosynthetic efficiencies, kinetic factors and mathematical combinations of these quantities.

[0139] The determination of a parameter may be carried out by known methods. For example, melting temperature of the oligonucleotide/target duplex may be determined using the nearest neighbor method and parameters appropriate for the nucleotide acids involved. For DNA/DNA parameters, see J. SantaLucia Jr., et al., (1996) Biochemistry, 35:3555. For RNA/DNA parameters, see N. Sugimoto, et al., (1995) Biochemistry, 34:11211. Briefly, these methods are based on the observation that the thermodynamics of a nucleic acid duplex can be modeled as the sum of a term arising from the entire duplex and a set of terms arising from overlapping pairs of nucleotides (“nearest neighbor” model). For a discussion of the nearest neighbor see J. SantaLucia Jr., et al., (1996) Biochemistry, supra, and N. Sugimoto, et al., (1995) Biochemistry, supra. For example, the enthalpy ΔH of the duplex formed by the sequence

5ATGGACTTAGCA (SEQ ID NO:4)

[0140] and its perfect complement can be approximated by the equation

ΔH≈Hunit+HAT+HTG+HGG+HGA+HAC+HCT+HTT+HTA+HAG+HGC+HCA.

[0141] In the above equation, the term Hinit is the initiation enthalpy for the entire duplex, while the terms HAT, . . . , HCA are the so-called “nearest neighbor” enthalpies. Similar equations can be written for the entropy, for the corresponding quantities for RNA homoduplexes, or for DNA/RNA heteroduplexes. The free energy can then be calculated from the enthalpy, entropy and absolute temperature, as described previously.

[0142] Predicted free energy of the most stable intramolecular structure of an oligonucleotide (ΔGMFOLD) may be determined using the nucleic acid folding algorithm MFOLD and parameters appropriate for the oligonucleotide, e.g., DNA or RNA. For MFOLD, see J. A. Jaeger, et al., (1989), supra. For DNA folding parameters, see J. SantaLucia Jr., et al., (1996), supra. Briefly, these methods operate in two steps. First, a map of all possible compatible intramolecular base pairs is made. Second, the global minimum of the free energy of the various possible base pairing configurations is found, using the nearest neighbor model to estimate the enthalpy and entropy, the user input temperature to complete the calculation of free energy, and a dynamic programming algorithm to find the global minimum. The algorithm is computationally intensive; calculation times scale as the third power of the sequence length.

[0143] The following Table 1 summarizes groups of parameters that are independently predictive of the ability of each of the oligonucleotides to hybridize to the target nucleotide sequence together with a reference to methods for their determination. Parameters within a given group are known or expected to be strongly correlated to one another, while parameters in different groups are known or expected to be poorly correlated with one another.

6TABLE 1GroupParameterSource or ReferenceIduplex enthalpy, ΔHSanta Lucia et al., 1996; Sugimoto et al., 1995duplex entropy, ΔSSanta Lucia et al., 1996; Sugimoto et al., 1995duplex free energy, ΔGΔG = ΔH − TΔS (see text)melting temperature, Tm(see text)mole fraction (or percent) G + Cself-explanatorysubsequence duplex enthalpySanta Lucia et al., 1996; Sugimoto et al., 1995subsequence duplex entropySanta Lucia et al., 1996; Sugimoto et al., 1995subsequence duplex free energyΔG = ΔH − TΔS (see text)subsequence duplex Tm(see text)subsequence duplex mole fractionself-explanatory(or percent) G + CIIintramolecular enthalpy, ΔHMFOLDJaeger et al., 1989; Santa Lucia et al., 1996intramolecular entropy, ΔSMFOLDJaeger et al., 1989; Santa Lucia et al., 1996intramolecular free energy, ΔGMFOLDΔG = ΔH − TΔS (see text)hairpin enthalpy, ΔHhairpinJaeger et al., 1989; Santa Lucia et al., 1996hairpin entropy, ΔShairpinJaeger et al., 1989; Santa Lucia et al., 1996hairpin free energy, ΔGhairpinΔG = ΔH − TΔS (see text)intramolecular partition function, Z

Z = \sum_{k structures} \exp (- Δ G_{intramolecular}^{(k)} / RT)

IIIsequence complexityAltschul et al., 1994sequence information contentAltschul et al., 1994IVsteric factorsmolecular modeling or experimentmolecular dynamic simulationWeber & Hefland, 1979enthalpy, entropy & free energy ofmeasured experimentallyactivationassociation & dissociation ratesPatzel & Sczakiel, 1998Voligonucleotide chemosyntheticmeasured experimentallyefficienciesVItarget synthetic efficienciesmeasured experimentally

[0144] In a next step of the present method, a subset of oligonucleotides within the predetermined number of unique oligonucleotides is identified based on the above evaluation of the parameter. A number of mathematical approaches may be followed to sort the oligonucleotides based on a parameter. In one approach a cut-off value is established. The cut-off value is adjustable and can be optimized relative to one or more training data sets. This is done by first establishing some metric for how well a cutoff value is performing; for example, one might use the normalized signal observed for each oligonucleotide in the training set. Once such a metric is established, the cutoff value can be numerically optimized to maximize the value of that metric, using optimization algorithms well known to the art. Alternatively, the cutoff value can be estimated using graphical methods, by graphing the value of the metric as a function of one or more parameters, and then establishing cutoff values that bracket the region of the graph where the chosen metric exceeds some chosen threshold value. In essence, the cut off values are chosen so that the rule set used yields training data that maximizes the inclusion of oligonucleotides that exhibit good hybridization efficiency and minimizes the inclusion of oligonucleotides that exhibit poor hybridization efficiency.

[0145] A preferred approach to performing such a graph-based optimization of filter parameters is shown in FIG. 3. In FIG. 3, hybridization data from several different genes have been used to prepare a contour plot of relative hybridization intensity as a function of DNA/RNA heteroduplex melting temperature and free energy of the most stable intramolecular structure of the probe. Contours are shown only for regions for which there are data; the white space outside of the outermost contour indicates that there are no experimental data for that region. The details of how the data were obtained can be found in Example 1 below. A summary of the sequences and number of data points employed is shown in Table 2 below. The measured hybridization intensities for each data set were normalized prior to construction of the contour plot depicted in FIG. 3 by dividing each observed intensity by the maximum intensity observed for that gene. In addition, differences in hybridization salt concentrations and hybridization temperatures were accounted for by using the salt concentration-corrected values of the melting temperatures and by subtracting the hybridization temperature from each predicted melting temperature, respectively. The filter set determined by examination of FIG. 3 is indicated by both the dotted open box in the figure and by the inequalities above the box.

[0146] One way in which such a contour plot may be prepared involves the use of an appropriate software application such as Microsoft® Excel® or the like. For example, the cross-tabulation tool may be used in the Microsoft® Excel® program. Data is accumulated into rectangular bins that are 0.5 kcal ΔGMFOLD wide and 2.5° C. Tm wide. In each bin the average values of ΔGMFOLD, Tm−Thyb, and the normalized hybridization intensity are calculated. The data is output to the software application DeltaGraph® (Deltapoint, Inc., Monterey, Calif.) and the contour plot is prepared using the tools and instructions provided.

7TABLE 2Target (GenBankTargetNo. Data[Na+]Accession No.)StrandPointsThybCorrectionHIV protease-reverseSense1,02235° C.−1.4° C.transcriptase (PRT)a(M15654)HIV protease-reverseantisense1,04130° C.−1.4° C.transcriptase (PRT)a(M15654)HIV protease-reverseSense8835° C.−1.4° C.transcriptase (PRT)b(M15654)Human G3PDHantisense9335° C.−1.4° C.(glyceraldehyde-3-dehydrogenase)b (X01677)Human p53b (X02469)antisense9335° C.−1.4° C.Rabbit β-globinc (K03256)antisense10630° C. 0° C.aData from Affymetrix GeneChip ™ Array bData from biotinylated probes bound to streptavidin-coated microtiter wells cLiterature data: see N. Milner, K. U. Mir & E. M. Southern (1997) Nature Biotech. 15, 537-541.

[0147] Once the cut-off value is selected, a subset of oligonucleotides having parameter values greater than or equal to the cut-off value is identified. This refers to the inclusion of oligonucleotides in a subset based on whether the value of a predictive parameter satisfies an inequality.

[0148] Examples of identifying a subset of oligonucleotides by establishing cut-off values for predictive parameters are as follows: for melting temperature an inequality might be 60° C.≦Tm; for predicted free energy an inequality, preferably, might be

Δ G_{M F O L D} \geq - 0.4 \frac{kcal}{m o l e} .

[0149] In a variation of the above, both a maximum and a minimum cut-off value may be selected. A subset of oligonucleotides is identified whose values fall within the maximum and minimum values, i.e., values greater than or equal to the minimum cut-off value and less than or equal to the maximum cut-off value. An example of this approach for melting temperature might be the inequality 60° C.≦Tm≦85° C.

[0150] With regard to cut off values for Tm the lower limit is most important, and is preferably Tm=Thyb, more preferably, Tm=Thyb+15° C. The upper cutoff is important when the sequence region under consideration is unusually rich in G and C, and is preferably Tm=Thyb+40° C. With regard to ΔGMFOLD the cutoff value is usually greater than or equal to −1.0 kcal/mole. As mentioned above, the cutoff values preferably are determined from real data through experimental observations.

[0151] In another approach the parameter values may be converted into dimensionless numbers. The parameter value is converted into a dimensionless number by determining a dimensionless score for each parameter resulting in a distribution of scores having a mean value of zero and a standard deviation of one. The dimensionless score is a number that is used to rank some object (such as an oligonucleotide) to which that score relates. A score that has no units (i.e., a pure number) is called a dimensionless score.

[0152] In one approach the following equations are used for converting the values of said parameters into dimensionless numbers:

s_{i, x} = \frac{x_{i} - ⟨ x ⟩}{σ_{{x}}},

[0153] where si,x is the dimensionless score derived from parameter x calculated for oligonucleotide i, xi is the value of parameter x calculated for oligonucleotide i, <x> is the average of parameter x calculated for all of the oligonucleotides under consideration for a given nucleotide sequence target, and σ{x} is the standard deviation of parameter x calculated for all of the oligonucleotides under consideration for a given nucleotide sequence target, and is given by the equation

σ_{{x}} = \sqrt{\frac{\sum_{j = 1}^{M} {(x_{j} - ⟨ x ⟩)}^{2}}{M - 1}},

[0154] where M is the number of oligonucleotides. The resulting distribution of scores, {s} has a mean value of zero and a standard deviation of one. These properties can be important for a combination of the scores discussed below.

[0155] The use of a dimensionless number approach may further include calculating a combination score Si by evaluating a weighted average of the individual values of the dimensionless scores si,x by the equation:

S_{i} = \sum_{{x}} q_{x} s_{i, x},

[0156] where qx is the weight assigned to the score derived from parameter x, the individual values of qx are always greater than zero, and the sum of the weights qx is unity.

[0157] In another variation of the above approach, the method of calculation of the composite parameter is optimized based on the correlation of the individual composite scores to real data, as explained more fully below.

[0158] In one approach the calculation of the composite score further involves determining a moving window-averaged combination score <Si> for the ith probe by the equation:

⟨ S_{i} ⟩ = \frac{1}{w} \sum_{j = i - \frac{w - 1}{2}}^{i + \frac{w - 1}{2}} S_{J},

[0159] w=an odd integer,

[0160] where w is the length of the window for averaging (i.e., w nucleotides long), and then applying a cutoff filter to the value of <Si>. This procedure results in smoothing (smoothing procedure) by turning each score into a consensus metric for a set of w adjacent oligonucleotide probes. The score, referred to as the “smoothed score,” is essentially continuous rather than a few discrete values. The value of the smoothed score is strongly influenced by clustering of scores with high or low values; window averaging therefore provides a measurement of cluster size.

[0161] An advantage of the dimensionless score approach to the probe prediction algorithm is that it is easy to objectively optimize. In one approach to training the algorithm, optimization of the weights qx above may be performed by varying the values of the weights so that the correlation coefficient ρ{<Si>},{Vi} between the set of window-averaged combination scores {<Si>} and a set of calibration experimental measurements {Vi} is maximized. The correlation coefficient ρ{<Si>},{Vi} is calculated from the equation

ρ_{{⟨ S_{i} ⟩}, {V_{i}}} = (\frac{1}{M}) \frac{C o v a r i a n c e (⟨ S ⟩, V)}{σ_{{⟨ S_{i} ⟩}} σ_{{V_{i}}}},

[0162] where M is the number of window averaged, combination dimensionless scores and the number of corresponding measurements, the covariance is as defined earlier (see earlier equations) and σ{<Si>} and σ{Vi} are the standard deviations of {<Si>} and {Vi}, as defined previously. An example of this approach is shown in Example 2, below.

[0163] In another approach the parameter is derived from one or more factors by mathematical transformation of the factors. This involves the calculation of a new predictive parameter from one or more existing predictive parameters, by means of an equation. For instance, the equilibrium constant Kopen for formation of an oligonucleotide with no intramolecular structure from its structured form can be calculated from the intramolecular structure free energy ΔGMFOLD, using the equation:

K_{open} = \exp (\frac{Δ G_{MFOLD}}{RT}) .

[0164] In a next step of the method oligonucleotides in the subset are then identified that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence. For example, consider a set of overlapping oligonucleotides identified by dividing a nucleotide sequence into subsequences. A subset of the oligonucleotides is obtained as described above. In general, this subset is obtained by applying a rule that rejects some members of the set. For the remaining members of the set, namely, the subset, there will be some average number of nucleotides in the nucleotide sequence between the first nucleotides of adjacent remaining subsequences. If, for some sub-region of the nucleotide sequence, the average number of nucleotides in the nucleotide sequence between the first nucleotides of adjacent remaining subsequences is less than the average for the entire nucleotide sequence, then the oligonucleotides are clustered. The smaller the average number of nucleotides between the first nucleotides of adjacent oligonucleotides, the stronger the clustering. The strongest clustering occurs when there are no intervening nucleotides between adjacent starting nucleotides. In this case, the oligonucleotides are said to be contiguous and may be referred to as contiguous sequence elements or “contigs.”

[0165] Accordingly, in this step oligonucleotides are sorted based on length of contiguous sequence elements. Oligonucleotides in the subset determined above are identified that are contiguous along a region of the input nucleic acid sequence. The length of each contig that is equal to the number of oligonucleotides in each contig, namely, oligonucleotides from the above step whose complement begin at positions m+1, m+2 . . . . , m+k in the target sequence, form a contig of length k. Contigs can be identified and contig length can be calculated using, for example, a Visual Basic ® module that can be incorporated into a Microsoft® Excel workbook.

[0166] Cluster size can be defined in several ways:

[0167] For contiguous clusters, the size is simply the number of adjacent oligonucleotides in the cluster. Again, this may also be referred to as contiguous sequence elements. The number may also be referred to as “contig length”. For example, consider the nucleotide sequence discussed above, namely, ATGGACTTAGCATTCG (SEQ ID NO:3) and the identified set of overlapping oligonucleotides

8ATGGACTTAGCA (SEQ ID NO:4) TGGACTTAGCAT (SEQ ID NO:5) GGACTTAGCATT (SEQ ID NO:6) GACTTAGCATTC (SEQ ID NO:7) ACTTAGCATTCG (SEQ ID NO:8)

[0168] Suppose that, after calculation and evaluation of the predictive parameters, four nucleotides remain:

[0169] A “contig” encompassing three of the oligonucleotides of the subset is present together with a single oligonucleotide. The contig length is 3 oligonucleotides.

[0170] Alternatively, cluster size at some position in the sequence hybridizable or complementary to the target sequence may be defined as the number of oligonucleotides whose center nucleotides fall inside a region of length M centered about the position in question, divided by M. This definition of clustering allows small gaps in clusters. In the example used above for contiguous clusters, if M was 10, then the cluster size would step through the values 0/10, . . . , 0/10, 1/10, 2/10, 3/10, 3/10, 4/10, 4/10, 4/10, 4/10, 4/10, 3/10, 2110, 1110, 1/10, 0/10 as the center of the window of length 10 passed through the cluster. In each fraction, the numerator is the number of oligonucleotide sequences that have satisfied the filter set and whose central nucleotides are within a window 10 nucleotides long, centered about the nucleotide under consideration. The denominator (10) is simply the window length.

[0171] Another alternative is to define the size of a cluster at some position in the sequence hybridizable or complementary to the target sequence as the number of oligonucleotide sequences overlapping that position. This definition is equivalent to the last definition with M set equal to the oligonucleotide probe length and omission of the division by M.

[0172] Finally, cluster size can be approximated at each position in a nucleotide sequence by dividing the sequence into oligonucleotides, evaluating a numerical score for each oligonucleotide, and then averaging the scores in the neighborhood of each position by means of a moving window average as described above. Window averaging has the effect of reinforcing clusters of high or low values around a particular position, while canceling varying values about that position. The window average, therefore, provides a score that is sensitive to both the hybridization potential of a given oligonucleotide and the hybridization potentials of its neighbors.

[0173] In a next step of the present method, the oligonucleotides in the subset are ranked. Generally, this ranking is based on the lengths of the clusters or contigs, sizes of the clusters or values of a window averaged score. Oligonucleotides found in the longest contigs or largest clusters, or possessing the highest window averaged scores usually show the highest hybridization efficiencies. Often, the highest signal intensity within the cluster corresponds to the median oligonucleotide of the cluster. However, the peak signal intensity within the contig can be determined experimentally, by sampling the cluster at its first quartile, midpoint and third quartile, measuring the hybridization efficiencies of the sampled oligonucleotides, interpolating or extrapolating the results, predicting the position of the optimal probe, and then iterating the probe design process.

[0174]
FIG. 1 shows a diagram of an example of the above-described method by way of illustration and not limitation. Referring to FIG. 1 a target sequence of length L from, e.g., a database, is used to generate a sequence that is hybridizable to the target sequence from which candidate oligonucleotide probe sequences are generated. One or more parameters are calculated for each of the oligonucleotide probe sequences. The candidate oligonucleotide probe sequences are filtered based on the values of the parameters. Clustering of the filtered candidate probe sequences is evaluated and the clusters are ranked by size. Then, the oligonucleotide probes are statistically sampled and synthesized. Further evaluation may be made by evaluating the hybridization of the selected oligonucleotide probes in real hybridization experiments. The above process may be reiterated to further define the selection. In this way only a small fraction of the potential oligonucleotide probe candidates are synthesized and tested. This is in sharp contrast to the known method of synthesizing and testing all or a major portion of potential oligonucleotide probes for a given target sequence.

[0175] The methods of the present invention are preferably carried out at least in part with the aid of a computer. For example, an IBM® compatible personal computer (PC) may be utilized. The computer is driven by software specific to the methods described herein.

[0176] The preferred computer hardware capable of assisting in the operation of the methods in accordance with the present invention involves a system with at least the following specifications: Pentium® processor or better with a clock speed of at least 100 MHz, at least 32 megabytes of random access memory (RAM) and at least 80 megabytes of virtual memory, running under either the Windows 95 or Windows NT 4.0 operating system (or successor thereof).

[0177] As mentioned above, software that may be used to carry out the methods may be either Microsoft Excel or Microsoft Access, suitably extended via user-written functions and templates, and linked when necessary to stand-alone programs that calculate specific parameters (e.g., MFOLD for intramolecular thermodynamic parameters). Examples of software programs used in assisting in conducting the present methods may be written, preferably, in Visual BASIC, FORTRAN and C++, as exemplified below in the Examples. It should be understood that the above computer information and the software used herein are by way of example and not limitation. The present methods may be adapted to other computers and software. Other languages that may be used include, for example, PASCAL, PERL or assembly language.

[0178]
FIG. 2 depicts a more specific approach to a method in accordance with the present invention. Referring to FIG. 2, a sequence of length L is obtained from a database such as GenBank, UniGene or a proprietary sequence database. Probe length N is determined by the user based on the requirements for sensitivity and specificity and the limitations of the oligonucleotide synthetic scheme employed. The probe length and sequence length are used to generate L−N+1 candidate oligonucleotide probes, i.e., from every possible starting position. An initial selection is made based on local sequence predicted thermodynamic properties. To this end, melting temperature Tm and the self-structure free energy ΔGMFOLD, are calculated for each of the potential oligonucleotide probe: target nucleotide sequence complexes. Next, M probes that satisfy Tm and ΔGMFOLD filters are selected. A further selection can be made based on clustering of “good” parameters. Good parameters are parameters that satisfy all of the filters in the filter set. Clustering is defined by any of the methods described previously; in FIG. 2, the “contig length” definition of clustering is used.

[0179] For each of the M oligonucleotide sequences that satisfied all filters the question is asked whether the oligonucleotide sequence immediately following the sequence under consideration is also one of the sequences that satisfied all of the filters. If the answer to this question is NO, then one stores the current value of the contig length counter, resets the counter to zero and proceeds to the next oligonucleotide sequence that satisfied all filters. If the answer to the question is YES, then 1 is added to the contig length counter and, if the counter now equals 1 (i.e., this is the first oligonucleotide probe sequence in the contig), the starting position of the oligonucleotide is stored. One then moves to the next oligonucleotide that satisfied all filters, which, in this case, is the same as the next oligonucleotide before the application of the filter set. The process is repeated until all M filtered oligonucleotide sequences have been examined. In this way, a single pass through the set of M filtered oligonucleotide sequences generates the lengths and starting positions of all contigs.

[0180] Next, contigs are ranked based on the lengths of their contiguous sequence elements. Longer contig lengths generally correlate with higher hybridization efficiencies. All oligonucleotides of the higher-ranking contigs may be considered, or candidate oligonucleotide probes may be picked. For example, candidate oligonucleotide probes can be picked one quarter, one half and three quarters of the way through each contig. The latter approach provides local curvature determination after experimental determination of hybridization efficiencies, which allows either interpolation or extrapolation of the positions of the next probes to be synthesized in order to close in on the optimal probe in the region. If the contig brackets the actual peak of hybridization efficiency, the process will converge in 2-3 iterations. If the contig lies to one side of the actual peak, the process will converge in 3-4 iterations.

[0181] The above illustrative approach is further described with reference to the following DNA nucleotide sequence, which is the complement of the target RNA nucleotide sequence:

10GTCCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAACTCATGTTCAAGA (SEQ ID NO:9).

[0182] In the first step of the method, the nucleotide sequence is divided into overlapping oligonucleotides that are 25 nucleotides in length. This length is chosen because it is an effective compromise between the need for sensitivity (enhanced by longer oligonucleotides) and the chemosynthetic efficiency of schemes for synthesis of surface-bound arrays of oligonucleotide probes.

[0183] Next, the estimated duplex melting temperatures (Tm) and self-structure free energies (ΔGMFOLD) are calculated for each oligonucleotide in the set of overlapping oligonucleotides. The values are obtained from a user-written function that calculates DNA/RNA heteroduplex thermodynamic parameters (see N. Sugimoto, et al., Biochemistry, 34:11211 (1995)) and a modified version of the program MFOLD that estimates the free energy of the most stable intramolecular structure of a single stranded DNA molecule (see J. A. Jaeger, et al., (1989), supra, respectively. The steps are illustrated below.

11GTCCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAACTCATGTTCAAGA(target complement sequence)Tm (°C)ΔGMFOLDGTCCAAAAAGGGTCAGTCTACCTCC71.77−1.20 SEQ ID NO:10 TCCAAAAAGGGTCAGTCTACCTCCC71.99−1.20 SEQ ID NO:11 CCAAAAAGGGTCAGTCTACCTCCCG70.78−1.20 SEQ ID NO:12 CAAAAAGGGTCAGTCTACCTCCCGC71.23−1.20 SEQ ID NO:13 AAAAAGGGTCAGTCTACCTCCCGCC73.07−1.20 SEQ ID NO:14 AAAAGGGTCAGTCTACCTCCCGCCA75.68−1.20 SEQ ID NO:15 AAAGGGTCAGTCTACCTCCCGCCAT77.53−1.20 SEQ ID NO:16 AAGGGTCAGTCTACCTCCCGCCATA79.03−1.20 SEQ ID NO:17 AGGGTCAGTCTACCTCCCGCCATAA79.03−1.20 SEQ ID NO:18 GGGTCAGTCTACCTCCCGCCATAAA76.85−1.20 SEQ ID NO:19 GGTCAGTCTACCTCCCGCCATAAAA73.10−0.80 SEQ ID NO:20 GTCAGTCTACCTCCCGCCATAAAAA69.50 0.90 SEQ ID NO:21 TCAGTCTACCTCCCGCCATAAAAAA65.60 0.90 SEQ ID NO:22 CAGTCTACCTCCCGCCATAAAAAAC64.96 0.90 SEQ ID NO:23 AGTCTACCTCCCGCCATAAAAAACT65. 1.10 SEQ ID NO:24 GTCTACCTCCCGCCATAAAAAACTC66.36 2.40 SEQ ID NO:25 TCTACCTCCCGCCATAAAAAACTCA64.97 2.90 SEQ ID NO:26 CTACCTCCCGCCATAAAAAACTCAT63.96 2.70 SEQ ID NO:27 TACCTCCCGCCATAAAAAACTCATG62.58 1.10 SEQ ID NO:28 ACCTCCCGCCATAAAAAACTCATGT65.10 0.40 SEQ ID NO:29 CCTCCCGCCATAAAAAACTCATGTT64.96 0.10 SEQ ID NO:30 CTCCCGCCATAAAAAACTCATGTTC63.37−0.10 SEQ ID NO:31 TCCCGCCATAAAAAACTCATGTTCA62.86−0.10 SEQ ID NO:32 CCCGCCATAAAAAACTCATGTTCAA60.47−0.10 SEQ ID NO:33 CCGCCATAAAAAACTCATGTTCAAG57.98−0.10 SEQ ID NO:34 CGCCATAAAAAACTCATGTTCAAGA56.20−0.10 SEQ ID NO:35

[0184] Next, the oligonucleotide sequences are filtered on the basis of Tm. A high and low cut-off value may be selected, for example, 60° C.≦Tm≦85° C. Thus, oligonucleotides having Tm values falling within the above range are retained. Those outside the range are discarded, which is indicated below by lining out of those oligonucleotides and parameter values.

[0185] Next, the oligonucleotide sequences remaining after the above exercise are filtered on the basis of ΔGMFOLD and are retained if the value is greater than −0.4. Those oligonucleotides with a ΔGMFOLD less than −0.4 are discarded, which is indicated below by double lining out of those oligonucleotides and parameter values.

[0186] Clusters of retained oligonucleotides are identified and ranked based on cluster size. In this example, a contiguous cluster of 13 retained oligonucleotides is identified by the vertical black bar on the left. Any or all of the oligonucleotides in this cluster may be evaluated experimentally.

[0187] Alternatively, in one approach the oligonucleotides at the first quartile, the median and the third quartile of the cluster may be selected for experimental evaluation, indicated below by bold print.

[0188] In one aspect of the present method, at least two parameters are determined wherein the parameters are poorly correlated with respect to one another. The reason for requiring that the different parameters chosen are poorly correlated with one another is that an additional parameter that is strongly correlated to the original parameter brings no additional information to the prediction process. The correlation to the original parameter is a strong indication that both parameters represent the same physical property of the system. Another way of stating this is that correlated parameters are linearly dependent on one another, while poorly correlated parameters are linearly independent of one another. In practice, the absolute value of the correlation coefficient between any two parameters should be less than 0.5, more preferably, less than 0.25, and, most preferably, as close to zero as possible.

[0189] In one preferred approach instead of Tm, for each oligonucleotide/target nucleotide sequence duplex, the difference between the predicted duplex melting temperature corrected for salt concentration and the temperature of hybridization of each of the oligonucleotides with the target nucleotide sequence is determined.

[0190] In one aspect the present method comprises determining two parameters at least one of the parameters being the association free energy between a subsequence within each of the oligonucleotides and its complementary sequence on the target nucleotide sequence, or some similar, strongly correlated parameter. The object of this approach is to identify a particularly stable subsequence of the oligonucleotide that might be capable of acting as a nucleation site for the beginning of the heteroduplex formation between the oligonucleotide and the target nucleotide sequence. Such nucleation is believed to be the rate-limiting step for process of heteroduplex formation.

[0191] The subsequence within the oligonucleotide is from about 3 to 9 nucleotides in length, usually, 5 to 7 nucleotides in length. The subsequence is at least three nucleotides from the terminus of the oligonucleotide. For support-bound oligonucleotides the subsequence is at least three nucleotides from the free end of the oligonucleotide, i.e., the end that is not attached to the support. Generally, this free end is the 5′ end of the oligonucleotide. When the oligonucleotide is attached to a support, the subsequence is at least three nucleotides from the end of the oligonucleotide that is bound to the surface of the support to which the oligonucleotide is attached. Generally, the 3′ end of the oligonucleotide is bound to the support.

[0192] The predictive parameter can be, for example, either melting temperature or duplex free energy of the subsequence with the target nucleotide sequence. The subsequence with the maximum (melting temperature) or minimum (free energy) value of one of the above parameters is chosen as the representative subsequence for that oligonucleotide probe. For example, if the oligonucleotide is 20 nucleotides in length and a subsequence of 5 nucleotides is chosen, i.e., a 5-mer, then parameter values are calculated for all 5-mer subsequences of the oligonucleotide that do not include the 2 nucleotides at the free end of the oligonucleotide. Where 5′ is the free end of the oligonucleotide with designated nucleotide number 1, the values are calculated for all 5-mer subsequences with starting nucleotides from position number 3 to position number 16. Thus, in this example, parameter values for 14 different subsequences are calculated. The subsequence with the maximum value for the parameter is then assigned as the stability subsequence for the oligonucleotide.

[0193] The inclusion of the above determination of a stability subsequence results in the following algorithm for determining the potential of an oligonucleotide to hybridize to a target nucleotide sequence. A predetermined number of unique oligonucleotides are identified within a nucleotide sequence that is hybridizable with said target nucleotide sequence. The oligonucleotides are chosen to sample the entire length of the nucleotide sequence. For each of the oligonucleotides, parameters that are independently predictive of the ability of each of said oligonucleotides to hybridize to said target nucleotide sequence are determined and evaluated. Two parameters that may be used are the thermodynamic parameters of Tm and ΔGMFOLD. These parameters give rise to associated parameter filters. In one approach evaluation of the parameters involves establishing cut-off values as described above. Application of these cut-off values results in the identification of a subset of oligonucleotides for further scrutiny under the algorithm. In accordance with this embodiment of the present invention, there is included a stability subsequence limit in addition to the above. Cutoff values are determined either by means of objective optimization algorithms well known to the art or via graphical estimation methods; both approaches have been described previously in this document. In either case, the optimization of cutoff values involves comparison of predictions to known hybridization efficiency data sets. This process results in objective optimization as it looks at prediction versus experimental results and is otherwise referred to herein as “training the algorithm.” The experimental data used to train the algorithm is referred to herein as “training data.”

[0194] In the present approach filters are assigned to the Tm oligonucleotide probe data. The Tm of each oligonucleotide probe needs to be greater than or equal to the assigned filter (Tm probe limit) to be given a filter score of “1”; otherwise, the filter score is “0”. In addition, one can also impose a second filter for this parameter; that is, that the Tm of the oligonucleotide probe also has to be less than a defined upper limit. Filters are also assigned to the ΔGMFOLD data. The ΔGMFOLD of each oligonucleotide probe should be greater than or equal to the assigned filter (ΔGMFOLD limit) to be given a filter score of “1”; otherwise, the filter score is “0”. The filter scores are added. Furthermore, one can also impose a second filter for this parameter; that is, that the ΔGMFOLD also has to be less than a defined upper limit. In accordance with the above discussion stability subsequences are identified. This leads to another filter. Accordingly, filters are assigned to the stability sequence data. The stability subsequence of each oligonucleotide probe needs to be greater than or equal to the assigned filter limit to be given a filter score of “1 ”; otherwise, the filter score is “0”. In addition, one can also impose a second filter for this parameter; that is, that the stability subsequence also has to be less than a defined upper limit. In all cases, the filter values are determined by objective optimization (algorithmic or graphical) of the predictions of the present method versus training data, as described previously.

[0195] On the basis of the above filter sets a subset of oligonucleotides within said predetermined number of unique oligonucleotides is identified. Oligonucleotides in the subset are identified that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence. The resulting number of oligonucleotide probe regions is examined. The above filters may then be loosened or tightened by changing the filter limits to obtain more or fewer clusters of oligonucleotides to match the goal, which is set by the needs of the investigator. For instance, a particular application might require that the investigator design 5 non-overlapping probes that efficiently hybridize to a given target sequence.

[0196] As mentioned above, the contigs may be selected on the basis of contig length. In another approach, the scores defined above may be summed for cluster size determination. To this end the probe score of the particular filter set (e.g., Tm probe limit, ΔGMFOLD limit and stability sequence limit) is calculated for each oligonucleotide probe. The probe score is the sum of the filter scores. Thus, the probe score is 0 if no parameters pass their respective filters. The probe score is 1, 2 or 3 if one, two or three parameters, respectively, pass their filters for that oligonucleotide probe. This summing is continued for each parameter that is in the current filter set of the algorithm used. For a given algorithm a minimum probe score limit is set. In the current example this limit will be at least 1 and could be 2 or 3 depending on the needs of the investigator, the number of probe clusters required and the results of objective optimizations of algorithm performance against training data. The probe score is compared to this probe score limit. If the probe score of oligonucleotide probe i is greater than or equal to the probe score limit, then oligonucleotide probe i is assigned a score passed value of 1. Next, a window is chosen for the evaluation of clustering (the “cluster window”). This will be the next filter applied. The cluster window (“w”) smoothes the score passed values by summing the values in a window w nucleotides long, centered about position i. The resulting sum is called the cluster sum. Usually, the cluster window is an odd integer, usually 7 or 9 nucleotides. The cluster sum values are then filtered, by comparing to a user-set threshold, cluster filter. If cluster sum is greater than or equal to cluster filter, this filter is passed, and the probe is predicted to hybridize efficiently to its target.

[0197] This window summing procedure converts the score for the passed value for each oligonucleotide into a consensus metric for a set of w adjacent probes. A “consensus metric” is a measurement that distills a number of values into one consensus value. In this case, the consensus value is calculated by simply summing the individual values. The window summing procedure therefore evaluates a property similar to the contig length metric discussed above. However, the summed score has the advantage of allowing for a few probes within a cluster to have not passed their individual probe score limits. We have found that this allows more observed hybridization peaks to be predicted.

[0198] It may be desired in some circumstances to combine the results of multiple algorithm versions. We refer to this operation as “tiling”. This may be explained more fully as follows. Tiling generally involves joining together the predicted oligonucleotide probe sets identified by multiple algorithm versions. In the context of the present invention, tiling multiple algorithm versions involves forming the union of multiple sets of predictions. These predictions may arise from different embodiments of the present invention. Alternatively, the different sets of predictions may arise from the same embodiment, but different filter sets. The different filter sets may additionally be restricted to different combinations of parameter values. For instance, one filter set might be used when the predicted duplex melting temperature Tm is greater than or equal to some value, while another might be used when Tm is below that value.

[0199] An example of the logical endpoint of tiling multiple filter sets across different regions of the possible combinations of predictive parameters and then forming the union of the resulting predictions is the contour plot shown in FIG. 3, with the associated rule that “the value of the normalized hybridization intensity associated with a particular combination of (Tm−Thyb) and ΔGMFOLD must be greater than or equal to some threshold value.” In this case, the contour at the threshold value becomes the filter. This contour and its interior can be thought of as the union of many small rectangular regions (“tiles”), each of which is bracketed by low and high cutoff values for each of the parameters.

[0200] The predictions of different algorithm versions can also be combined by forming the intersection of two or more different predictions. The reliability of predictions within such intersection sets is enhanced because such sets are, by definition, insensitive to changes in the details of the predictive algorithm. Intersection is a useful method for reducing the number of predicted probes when a single algorithm version produces too many candidate probes for efficient experimental evaluation.

[0201] The most specific oligonucleotide probe set (i.e., the set least likely to include poor probes) will be the intersection set from multiple algorithms. Clusters that have overlapping oligonucleotide probes from multiple algorithms constitute the intersection set of oligonucleotide probes. The oligonucleotide probe that is in the center of an intersection cluster is chosen. This central oligonucleotide probe may have the highest probability of predicting a peak or, in other words, of binding well to the target nucleotide sequence. Oligonucleotide probes on either side of center, which are still within the intersection cluster, may also be selected. The distance of these “side” oligonucleotide probes from the center generally will be shorter or longer depending upon the length of the cluster.

[0202] The most sensitive set of oligonucleotide probes (i.e., the set most likely to include at least one good probe) is generally the union set from multiple algorithms. Clusters that are predicted by at least one type of algorithm constitute the union set of oligonucleotide probes. The oligonucleotide probe in the center of a union cluster is chosen. Oligonucleotide probes on either side of center, which are still within the union cluster, usually are also chosen. The distance of these side probes from the center will be shorter or longer depending upon the length of the cluster. In summary, the combination of using the stability subsequence parameter, tiling multiple filter sets, and making union and intersection cluster sets of oligonucleotide probes exhibits very high sensitivity and specificity in predicting oligonucleotide probes that effectively hybridize to a target nucleotide sequence of interest.

[0203] Another aspect of the present invention is a computer based method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. A predetermined number of unique oligonucleotides within a nucleotide sequence that is hybridizable with the target nucleotide sequence is identified under computer control. The oligonucleotides are chosen to sample the entire length of the nucleotide sequence. A value is determined and evaluated under computer control for each of the oligonucleotides for at least one parameter that is independently predictive of the ability of each of the oligonucleotides to hybridize to the target nucleotide sequence. The parameter values are stored. Based on the examination of the stored parameter values, a subset of oligonucleotides within the predetermined number of unique oligonucleotides is identified under computer control. Then, oligonucleotides in the subset that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence are identified under computer control.

[0204] A computer program is utilized to carry out the above method steps. The computer program provides for input of a target-hybridizable or target-complementary nucleotide sequence, efficient algorithms for computation of oligonucleotide sequences and their associated predictive parameters, efficient, versatile mechanisms for filtering sets of oligonucleotide sequences based on parameter values, mechanisms for computation of the size of clusters of oligonucleotide sequences that pass multiple filters, and mechanisms for outputting the final predictions of the method of the present invention in a versatile, machine-readable or human-readable form.

[0205] Another aspect of the present invention is a computer system for conducting a method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. An input means for introducing a target nucleotide sequence into the computer system is provided. The input means may permit manual input of the target nucleotide sequence. The input means may also be a database or a standard format file such as GenBank. Also included in the system is means for determining a number of unique oligonucleotide sequences that are within a nucleotide sequence that is hybridizable with the target nucleotide sequence. The oligonucleotide sequences is chosen to sample the entire length of the nucleotide sequence. Suitable means is a computer program or software, which also provides memory means for storing the oligonucleotide sequences. The system also includes means for controlling the computer system to carry out a determination and evaluation for each of the oligonucleotide sequences a value for at least one parameter that is independently predictive of the ability of each of the oligonucleotide sequences to hybridize to the target nucleotide sequence. Suitable means is a computer program or software such as, for example, Microsoft® Excel spreadsheet, Microsoft® Access relational database or the like, which also provides memory means for storing the parameter values. The system further comprises means for controlling the computer to carry out an identification of a subset of oligonucleotide sequences within the number of unique oligonucleotide sequences based on the automated examination of the stored parameter values. Suitable means is a computer program or software, which also allocates memory means for storing the subset of oligonucleotides. The system also includes means for controlling the computer to carry out an identification of oligonucleotide sequences in the subset that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence. Suitable means is a computer program or software, which also allocates memory means for storing the oligonucleotide sequences in the subset. The computer system also includes means for outputting data relating to the oligonucleotide sequences in the subset. Such means may be machine readable or human readable and may be software that communicates with a printer, electronic mail, another computer program, and the like. One particularly attractive feature of the present invention is that the outputting means may communicate directly with software that is part of an oligonucleotide synthesizer. In this way the results of the method of the present invention may be used directly to provide instruction for the synthesis of the desired oligonucleotides.

[0206] Another advantage of the present invention is that it may be used to predict efficient hybridization oligonucleotides for each of multiple target sequences. Thus, very large arrays may be constructed and tested with minimal synthesis of oligonucleotides.

EXAMPLES

[0207] The invention is demonstrated further by the following illustrative examples. Parts and percentages are by weight unless otherwise indicated. Temperatures are in degrees Centigrade (°C.) unless otherwise specified. The following preparations and examples illustrate the invention but are not intended to limit its scope. All reagents used herein were from Amresco, Inc., Solon, Ohio (buffers), Pharmacia Biotech, Piscataway, N.J. (nucleoside triphosphates) or Promega, Madison, Wisconsin (RNA polymerases) unless indicated otherwise.

Example 1

[0208] Synopsis: Data from labeled RNA target hybridizations to surface-bound DNA probes directed against 4 different gene sequences were compared to the predictions of the preferred version of the prediction algorithm illustrated by the flow chart in FIG. 2. The RNA targets were sequences derived from the human immunodeficiency virus protease-reverse transcriptase region (HIV PRT; sense-strand target polynucleotide), human glyceraldehyde-3-phosphate dehydrogenase gene (G3PDH; antisense-strand target polynucleotide), human tumor suppressor p53 gene (p53; antisense-strand target polynucleotide) and rabbit β-globin gene (β-globin; antisense-strand target polynucleotide). The GenBank accession numbers for the gene sequences, number of data points collected and temperature of hybridization have all been previously listed in Table 2.

[0209] Materials and Methods: Three different experimental systems and two different labeling schemes were used to collect data.

[0210] The sequence and hybridization data for β-globin were taken from the literature (see Milner et al., (1997), supra; in this experiment, 32P-radiolabeled RNA target was used.

[0211] The hybridization data for HIV PRT were obtained using an Affymetrix GeneChip™ HIV PRT-sense probe array (i.e. sense strand target polynucleotide) (GeneChip™ HIV PRT 440s, Affymetrix Corporation, Santa Clara, Calif.) as specified by the manufacturer, except that the fluorescein-labeled RNA target was not fragmented prior to hybridization and that hybridization was performed for 24 hours. The concentration of fluorescein-labeled RNA used was 26.3 nM; label density was approximately 18 fluoresceinated uridyl nucleotides per 1 kilobase (kb) RNA transcript. The raw data were collected by scanning the array with a GeneChip™ Scanner 50 (Affymetrix Corporation, Santa Clara, Calif.), as specified by the manufacturer. The raw data were reduced to a feature-averaged (“.CEL”) file, using the GeneChip™ software supplied with the scanner. Finally, a table of hybridization intensities for perfect-complement 20-mer probes was constructed using the ASCII feature map file supplied with the GeneChip™ software to connect probe sequences to measured hybridization intensities. The resulting data set contained data for every overlapping 20-mer probe to the target sequence.

[0212] The data for G3PDH and p53 were measured using 93-feature arrays constructed using commercially available streptavidin-coated microtiter plates (Pierce Chemical Company, Rockford, Ill.). Every tenth possible 25-mer probe complementary to each target was synthesized and 3′-biotinylated by a contract synthesis vendor (Operon, Inc., Alameda, Calif.). The 3′-linked biotin was used to anchor individual probes to microtiter wells, via the well known, strong affinity of streptavidin for biotin. Biotinylated DNA probes were resuspended to a concentration of 10 μM in hybridization buffer (5× sodium chloride-sodium phosphate-disodium ethylenediaminetetraacetate (SSPE), 0.05% Triton X-100, filter-sterilized; 1× SSPE is 150 mM sodium chloride, 10 mM sodium phosphate, 1 mM disodium ethylenediaminetetraacetate (EDTA), pH 7.4). Individual probes were diluted 1:10 in hybridization buffer into specified wells (100 μl total volume per well) of a streptavidin-coated microtiter plate; probes were allowed to bind to the covered plates overnight at 35° C. The other 3 wells of the 96-well microtiter plate were probe-less controls. The coated plates were washed with 3×200 μl of wash buffer (6× SSPE, 0.005% Triton X-100, filter-sterilized). Fluorescein-labeled RNA (100 μl of a 10 nM solution in hybridization buffer) was added to each well. The plates were covered and hybridized at 35° C. for 20-24 hours. The hybridized plates were washed with 3×200 μl of wash buffer. Label was then released in each well by adding 100 μl of 20 μg/ml RNAase I (Sigma Chemical Company, St. Louis, Mo.) in Tris-EDTA (TE) (10 mM Tris(hydroxymethyl)aminomethane (Tris), 1 mM EDTA, pH 8.0, sterile) and incubating at 35° C. for at least 30 minutes. The fluorescence released from the surface of each well was quantitated with a PerSeptive Biosystems Cytofluor II microtiter plate fluorimeter (PerSeptive Biosystems, Inc., Framingham, Mass.) using the manufacturer's recommended excitation and emission filter sets for fluorescein. Each plate hybridization was performed in quadruplicate, and the data for each probe were averaged to obtain the hybridization intensity.

[0213] Labeled RNA targets specific for G3PDH and p53 were produced via T7 RNA polymerase transcription of DNA templates in the presence of fluorescein-UTP (Boehringer Mannheim Corporation, Indianapolis, Ind.), using the same method as that outlined by Affymetrix for their GeneChip™ HIV PRT sense probe array. The DNA template for G3PDH was purchased from a commercial source (Clontech, Inc., Palo Alto, Calif.). The DNA template for p53 was obtained by sub-cloning a PCR fragment from an ATCC-derived reference clone (No. 57254) of human p53 into the commercially-available PCR cloning vector pCR2.1-TOPO (Invitrogen, Inc., Carlsbad, Calif.), then linearizing the plasmid at the end of the polycloning site opposite the vector-derived T7 promoter.

[0214] Probe predictions were performed using a software application (referred to as “p5”) that was built atop Microsoft's Access relational database application, using added Visual Basic modules, the TrueDB Grid Pro 5.0 (Apex Software Corporation, Pittsburgh, Pa.) enhancement to Visual Basic, and a version of the FORTRAN application MFOLD, modified to run in a Windows NT 4.0 environment, as an ActiveX control. The Visual Basic source code for the p5 software application is found in the Microfiche appendix to this specification. The DNA target sequence complements that were input into p5 for division into potential oligonucleotide probe sequences are listed below:

[0215] Parent Sequence Accession No.: K03256

[0216] Locus: BUNGLOB.DNA (portion of rabbit β-globin)

[0217] Length: 122

161TTCTTCCACA TTCACCTTGC CCCACAGGGC AGTGACCGCA GACTTCTCCT CACTGGACAGSEQ ID NO:3661ATGCACCATT CTGTCTGTTT TGGGGGATTG CAAGTAAACA CAGTTGTGTC AAAAGCAAGT121GT

[0218] Parent Sequence Accession No.: M15654

[0219] Locus: HIV_PRTA.S (HIV PRT antisense; parses into probes specific for sense-strand target)

[0220] Length: 1040

171TGTACTGTCC ATTTATCAGG ATGGAGTTCA TAACCCATCC AAAGGAATCG AGGTTCTTTCSEQ ID NO:3761TGATGTTTTT TGTCTGGTGT GGTAAGTCCC CACCTCAACA GATGTTGTCT CAGCTCCTCT121ATTTTTGTTC TATGCTGCCC TATTTCTAAG TCAGATCCTA CATACAAATC ATCCATGTAT181TGATAGATAA CTATGTCTGG ATTTTGTTTT TTAAAAGGCT CTAAGATTTT TGTCATGCTA241CTTTGGAATA TTGCTGGTGA TCCTTTCCAT CCCTGTGGAA GCACATTGTA CTGATATCTA301ATCCCTGGTG TCTCATTGTT TATACTAGGT ATGGTAAATG CAGTATACTT CCTGAAGTCT361TCATCTAAGG GAACTGAAAA ATATGCATCA CCCACATCCA GTACTGTTAC TGATTTTTTC421TTTTTTAACC CTGCGGGATG TGGTATTCCT AATTGAACTT CCCAGAAGTC TTGAGTTCTC481TTATTAAGTT CTCTGAAATC TACTAATTTT CTCCATTTAG TACTGTCTTT TTTCTTTATG541GCAAATACTG GAGTATTGTA TGGATTCTCA GGCCCAATTT TTGAAATTTT CCCTTCCTTT601TCCATTTCTG TACAAATTTC TACTAATGCT TTTATTTTTT CTTCTGTCAA TGGCCATTGT661TTAACTTTTG GGCCATCCAT TCCTGGCTTT AATTTTACTG GTACAGTCTC AATAGGGCTA721ATGGGAAAAT TTAAAGTGCA ACCAATCTGA GTCAACAGAT TTCTTCCAAT TATGTTGACA781GGTGTAGGTC CTACTAATAC TGTACCTATA GCTTTATGTC CACAGATTTC TATGAGTATC841TGATCATACT GTCTTACTTT GATAAAACCT CCAATTCCCC CTATCATTTT TGGTTTCCAT901CTTCCTGGCA AACTCATTTC TTCTAATACT GTATCATCTG CTCCTGTATC TAATAGAGCT961TCCTTTAGTT GCCCCCCTAT CTTTATTGTG ACGAGGGGTC GTTGCCAAAG AGTGATCTGA1021GGGAAGTTAA AGGATACAGT

[0221] Parent Sequence Accession No.: X01677

[0222] Locus: G3PDH (Clontech G3PDH template—parses into probes specific for antisense-strand target)

[0223] Length: 999

181GAAGGTCGGA GTCAACGGAT TTGGTCGTAT TGGGCGCCTG GTCACCAGGG CTGCTTTTAASEQ ID NO:3861CTCTGGTAAA GTGGATATTG TTGCCATCAA TGACCCCTTC ATTGACCTCA ACTACATGGT121TTACATGTTC CAATATGATT CCACCCATGG CAAATTCCAT GGCACCGTCA AGGCTGAGAA181CGGGAAGCTT GTCATCAATG GAAATCCCAT CACCATCTTC CAGGAGCGAG ATCCCTCCAA241AATCAAGTGG GGCGATGCTG GCGCTGAGTA CGTCGTGGAG TCCACTGGCG TCTTCACCAC301CATGGAGAAG GCTGGGGCTC ATTTGCAGGG GGGAGCCAAA AGGGTCATCA TCTCTGCCCC361CTCTGCTGAT GCCCCCATGT TCGTCATGGG TGTGAACCAT GAGAAGTATG ACAACAGCCT421CAAGATCATC AGCAATGCCT CCTGCACCAC CAACTGCTTA GCACCCCTGG CCAAGGTCAT481CCATGACAAC TTTGGTATCG TGGAAGGACT CATGACCACA GTCCATGCCA TCACTGCCAC541CCAGAAGACT GTGGATGGCC CCTCCGGGAA ACTGTGGCGT GATGGCCGCG GGGCTCTCCA601GAACATCATC CCTGCCTCTA CTGGCGCTGC CAAGGCTGTG GGCAAGGTCA TCCCTGAGCT661AGACGGGAAG CTCACTGGCA TGGCCTTCCG TGTCCCCACT GCCAACGTGT CAGTGGTGGA721CCTGACCTGC CGTCTAGAAA AACCTGCCAA ATATGATGAC ATCAAGAAGG TGGTGAAGCA781GGCGTCGGAG GGCCCCCTCA AAGGCATCCT GGGCTACACT GAGCACCAGG TGGTCTCCTC841TGACTTCAAC AGCGACACCC ACTCCTCCAC CTTTGACGCT GGGGCTGGCA TTGCCCTCAA901CGACCACTTT GTCAAGCTCA TTTCCTGGTA TGACAACGAA TTTGGCTACA GCAACAGGGT961GGTGGACCTC ATGGCCCACA TGCTATAGTG AGTCGTATT

[0224] Parent Sequence Accession No.: X54156

[0225] Locus: HSP53PCRa (p53 template—parses into probes specific for antisense-strand target)

[0226] Length: 1049

191GAGGTGCGTG TTTGTGCCTG TCCTGGGAGA GACCGGCGCA CAGAGGAAGA GAATCTCCGCSEQ ID NO:3961AAGAAAGGGG AGCCTCACCA CGAGCTGCCC CCAGGGAGCA CTAAGCGAGC ACTGCCCAAC121AACACCAGCT CCTCTCCCCA GCCAAAGAAG AAACCACTGG ATGGAGAATA TTTCACCCTT181CAGATCCGTG GGCGTGAGCG CTTCGAGATG TTCCGAGAGC TGAATGAGGC CTTGGAACTC241AAGGATGCCC AGGCTGGGAA GGAGCCAGGG GGGAGCAGGG CTCACTCCAG CCACCTGAAG301TCCAAAAAGG GTCAGTCTAC CTCCCGCCAT AAAAAACTCA TGTTCAAGAC AGAAGGGCCT361GACTCAGACT GACATTCTCC ACTTCTTGTT CCCCACTGAC AGCCTCCCTC CCCCATCTCT421CCCTCCCCTG CCATTTTGGG TTTTGGGTCT TTGAACCCTT GCTTGCAATA GGTGTGCGTC481AGAAGCACCC AGGACTTCCA TTTGCTTTGT CCCGGGGCTC CACTGAACAA GTTGGCCTGC541ACTGGTGTTT TGTTGTGGGG AGGAGGATGG GGAGTAGGAC ATACCAGCTT AGATTTTAAG601GTTTTTACTG TGAGGGATGT TTGGGAGATG TAAGAAATGT TCTTGCAGTT AAGGGTTAGT661TTACAATCAG CCACATTCTA GGTAGGTAGG GGCCCACTTC AGCGTACTAA CCAGGGAAGC721TGTCCCTCAT GTTGAATTTT CTCTAACTTC AAGGCCCATA TCTGTGAAAT GCTGGCATTT781GCACCTACCT CACAGAGTGC ATTGTGAGGG TTAATGAAAT AATGTACATC TGGCCTTGAA841ACCACCTTTT ATTACATGGG GTCTAAAACT TGACCCCCTT GAGGGTGCCT GTTCCCTCTC901CCTCTCCCTG TTGGCTGGTG GGTTGGTAGT TTCTACAGTT GGGCAGCTGG TTAGGTAGAG961GGAGTTGTCA AGTCTTGCTG GCCCAGCCAA ACCCTGTCTG ACAACCTCTT GGTCGACCTT1021AGTACCTAAA AGGAAATCTC ACCCCATCC

[0227] The sequences indicated above, which are complements of the target sequences, were divided into overlapping oligonucleotide sequences with one nucleotide between starting positions. The oligonucleotide sequence lengths were 17 (rabbit β-globin), 20 (HIV PRT) or 25 (G3PDH; p53). The oligonucleotide sequence lengths were dictated by the probe lengths used in the experiments to which the predictions were compared. The RNA target concentrations used to calculate predicted RNA/DNA duplex melting temperatures were 100 pM (rabbit β-globin), 26.3 nM (HIV PRT) and 10 nM (G3PDH; p53). These were also dictated by experimental conditions for the comparison data. The cut-off filter used for the predicted free energy of the most stable probe sequence intramolecular structure, ΔGMFOLD, was

Δ G_{MFOLD} \geq - 0.4 \frac{kcal}{mole} .

[0228] The filter condition used for the predicted RNA/DNA duplex melting temperature was

25° C.≦Tm+16.6 log([Na+])−Thyb≦50° C.,

[0229] where Tm is the target concentration-dependent value of the predicted RNA/DNA duplex melting temperature before correction for salt concentration, the term “16.6 log([Na+])” corrects the melting temperature for salt effects, and Thyb is the hybridization temperature. The values of the salt correction term and Thyb have already been listed in Table 2. For convenient use within p5, the above condition was algebraically rearranged into the equivalent form

25° C. −16.6 log([Na+])+Thyb≦Tm≦50° C.−16.6 log([Na+])+Thyb.

[0230] Clusters were ranked according to the number of contiguous oligonucleotide sequences that passed through the filter set (“contig” length).

[0231] Results: The detailed analysis results for rabbit β-globin are presented in Table 3; a graphical summary of the results is shown in FIG. 4. In Table 3, values of Tm and ΔGMFOLD that were excluded by the filter set are shown with a line through them, and table entries for contig length are shown in gray when the oligonucleotide sequence in question was not in a contig. The top 20% of the observed hybridization intensities are shown underlined.

20TABLE 3OligonucleotideSEQ IDΔGMFOLDContigHybridization IntensityPositionSequenceNO:Tm (° C.)(kcal/mole)Length(Milner et al., 1997)1TTCTTCCACATTCACCT40

5.001002TCTTCCACATTCACCTT41

5.001303CTTCCACATTCACCTTG42

0.901304TTCCACATTCACCTTGC43

0.502005TCCACATTCACCTTGCC4458.460.5071206CCACATTCACCTTGCCC4561.100.5071807CACATTCACCTTGCCCC4661.100.5072308ACATTCACCTTGCCCCA4761.100.5072209CATTCACCTTGCCCCAC4861.100.90732010ATTCACCTTGCCCCACA4961.100.70731011TTCACCTTGCCCCACAG5061.330.50732012TCACCTTGCCCCACAGG5163.70

39013CACCTTGCCCCACAGGG5264.85

41014ACCTTGCCCCACAGGGC5368.01

24015CCTTGCCCCACAGGGCA5468.63

5016CTTGCCCCACAGGGCAG5564.95

2017TTGCCCCACAGGGCAGT5666.31

2018TGCCCCACAGGGCAGTG5765.79

2019GCCCCACAGGGCAGTGA5867.37

2020CCCCACAGGGCAGTGAC5963.42

4021CCCACAGGGCAGTGACC6063.42

2022CCACAGGGCAGTGACCG6159.85

2023CACAGGGCAGTGACCGC6260.14

2024ACAGGGCAGTCACCGCA6360.14

2025CAGGGCAGTGACCGCAG6459.76

3026AGGGCAGTGACCGCAGA6559.83

2027GGGCAGTGACCGCAGAC6660.22

3028GGCAGTGACCGCAGACT6759.53

3029GCAGTGACCGCAGACTT6857.06

3030CAGTGACCGCAGACTTC69

4031AGTGACCGCAGACTTCT70

−0.20 4032GTGACCGCAGACTTCTC7155.990.60710033TGACCGCAGACTTCTCC7257.010.60712034GACCGCAGACTTCTCCT7359.220.60718035ACCGCAGACTTCTCCTC7459.280.60721036CCGCAGACTTCTCCTCA7560.070.60720037CGCAGACTTCTCCTCAC7656.340.60719038GCAGACTTCTCCTCACT7757.790.60724039CAGACTTCTCCTCACTG78

0.6024040AGACTTCTCCTCACTGG79

0.0034041GACTTCTCCTCACTGGA8055.77

34042ACTTCTCCTCACTGGAC81

24043CTTCTCCTCACTGGACA8255.75

24044TTCTCCTCACTGGACAG83

12045TCTCCTCACTGGACAGA84

10046CTCCTCACTGGACAGAT85

11047TCCTCACTGGACAGATG86

8048CCTCACTGGACAGATGC87

0.0024049CTCACTGGACAGATGCA88

0.20 9050TCACTGGACAGATGCAC89

0.20 3051CACTGGACAGATGCACC90

0.5010052ACTGGACAGATGCACCA91

8053CTGGACAGATGCACCAT92

9054TGGACAGATGCACCATT93

8055GGACAGATGCACCATTC94

0.3018056GACAGATGCACCATTCT95

−0.10 22057ACAGATGCACCATTCTG96

12058CAGATGCACCATTCTGT97

12059AGATGCACCATTCTGTC98

−0.10 25060GATGCACCATTCTGTCT99

0.3052061ATGCACCATTCTGTCTG100

0.4098062TGCACCATTCTGTCTGT10156.050.20278063GCACCATTCTGTCTGTT10256.520.20281064CACCATTCTGTCTGTTT103

0.2022065ACCATTCTGTCTGTTTT104

0.2012066CCATTCTGTCTGTTTTG105

0.2012067CATTCTGTCTGTTTTGG106

0.6016068ATTCTGTCTGTTTTGGG107

1.7031069TTCTGTCTGTTTTGGGG108

1.7025070TCTGTCTGTTTTGGGGG10955.901.702 8071CTGTCTGTTTTGGGGGA11055.911.402 3072TGTCTGTTTTGGGGGAT111

0.90 5073GTCTGTTTTGGGGGATT112

0.90 1074TCTGTTTTGGGGGATTG113

1.10 1075CTGTTTTGGGGGATTGC114

2.20 1076TGTTTTGGGGGATTGCA115

1.20 1077GTTTTGGGGGATTGCAA116

0.00 578TTTTGGGGGATTGCAAG117

−0.20 579TTTGGGGGATTGCAAGT118

−0.20 580TTGGGGGATTGCAAGTA119

0.00 581TGGGGGATTGCAAGTAA120

1.20 582GGGGGATTGCAAGTAAA121

1.40 583GGGGATTGCAAGTAAAC122

1.40 584GGGATTGCAAGTAAACA123

1.30 585GGATTGCAAGTAAACAC124

0.90 586GATTGCAAGTAAACACA125

0.50 587ATTGCAAGTAAACACAG126

0.50 588TTGCAAGTAAACACAGT127

0.50 589TGCAAGTAAACACAGTT128

0.30 590GCAAGTAAACACAGTTG129

0.10 1091CAAGTAAACACAGTTGT130

−0.30 592AAGTAAACACAGTTGTG131

593AGTAAACACAGTTGTGT132

594GTAAACACAGTTGTGTC133

595TAAACACAGTTGTGTCA134

596AAACACAGTTGTGTCAA135

597AACACAGTTGTGTCAAA136

100

101

598ACACAGTTGTGTCAAAA137

102

103

1099CACAGTTGTGTCAAAAG138

104

105

15100ACAGTTGTGTCAAAAGC139

106

107

30101CAGTTGTGTCAAAAGCA140

108

0.20 25102AGTTGTGTCAAAAGCAA141

109

−0.10 25103GTTGTGTCAAAAGCAAG142

110

−0.30 20104TTGTGTCAAAAGCAAGT143

111

−0.10 120105TGTGTCAAAAGCAAGTG144

112

0.50 20

[0232] In FIG. 4, the hybridization intensity observed experimentally is plotted as a function of oligonucleotide starting position in the target-complementary sequence that was input into p5. The identified contigs are plotted as horizontal bars, with the contig rank (by length) shown in parentheses next to each bar. It is clear from Table 3 and FIG. 4 that the prediction algorithm identified contigs that overlap all of the “top 20%” hybridization intensity peaks observed. Iterative experimental improvement of these predictions would converge on each of the observed intensity maxima in 3-4 iterations.

[0233] Prediction worksheets for HIV PRT, G3PDH and p53 were prepared in a manner similar to that for rabbit β-globin as shown in Table 3, except that the probes were longer as indicated above and that approximately 1,000 probes were analyzed for each of these genes. The results of these analyses are shown in FIG. 5 (HIV PRT), FIG. 6 (G3PDH) and FIG. 7 (p53). In FIG. 5, data are plotted for all possible 20-mer oligonucleotide probes. In FIGS. 6 and 7, data were available for only every 10th 25-mer probe, and the actual data points are plotted as open diamonds.

[0234] It is clear from FIGS. 5-7 that the hybridization efficiency prediction algorithm of the present invention performed well in the task of identifying regions with observed high hybridization intensity. In each case, the 4 longest contigs point to good-to-excellent regions for experimental investigation. It should be noted that the contigs usually bracket observed intensity peaks; experimental iterative refinement would therefore be expected to converge in 2-3 iterations. By this is meant that certain oligonucleotides from the identified contigs are prepared and subjected to evaluation in actual hybridization experiments. Based on the results of such experiments, the observed signal is evaluated to determine whether the oligonucleotides are hybridizing to the left of, the right of, or on the center of a peak with respect to the graphed data. The next iteration is carried out to experimentally evaluate the hybridization efficiency of probes that are inferred to lie closer to the peak of hybridization efficiency, based on the data from the previous iteration. Iteration is continued until the signal level is deemed acceptable by the user, or the local hybridization efficiency maximum is reached (i.e. the best probe in the cluster identified by the method of the current invention has been experimentally identified). A detailed illustration of this process is shown in Example 3.

[0235] It should be noted that clusters of predictions that overlap the maxima of observed peaks of hybridization efficiency will often yield user-acceptable probes on the first iteration. Thus, the method of the present invention is much more efficient than current methods in which every potential probe is synthesized. For instance, in the HIV PRT example shown in FIG. 5, at least 3 good probes would be identified after synthesis of ˜10 test probes (i.e. statistical sampling of the 3 longest contigs). This is much more efficient than the ˜1,000 probes represented by the data in FIG. 5.

Example 2

[0236] Synopsis: Data from a labeled RNA target hybridization to an Affymetrix GeneChip™ HIV PRT-sense probe array (GeneChip™ HIV PRT 440s, Affymetrix Corporation, Santa Clara, Calif.) were compared to the predictions of the window-averaged composite dimensionless score version of the method of the present invention.

[0237] Materials and Methods: Data were obtained as described for the Affymetrix GeneChip™ HIV PRT-sense probe array (GeneChip™ HIV PRT 440s, Affymetrix Corporation, Santa Clara, Calif.) in Example 1. The DNA sequence (SEQ ID NO: 37) complementary to the fluorescein-labeled RNA target was divided into overlapping 20-mer oligonucleotide sequences spaced one nucleotide apart, using the prototype application p5; p5 was also used to calculate the predicted values of the RNA/DNA heteroduplex melting temperature (Tm) and the free energy of the most stable predicted probe intramolecular structure, ΔGMFOLD, as described in Example 1. The probe sequences and parameter values were then transferred to a Microsoft Excel spreadsheet, which was used to complete the predictions of efficient and inefficient probes. The weight was obtained by optimizing the performance of the algorithm with the data of Milner et al., supra, as the training data using the Microsoft® Excel® spreadsheet software. The composite score was calculated using a weight of 0.62 for the dimensionless Tm score and a weight of 0.38 for the ΔGMFOLD dimensionless score. The windowed-averaging was performed using a window width of 7 and Microsofte Excel® spreadsheet software. Finally, the oligonucleotide sequences having the top 10% of the window-averaged composite dimensionless scores were predicted to be efficient probes, while the oligonucleotide sequences having the bottom 10% of the window-averaged composite dimensionless scores were predicted to be inefficient probes.

[0238] Results: The calculated parameters and scores are shown in Table 4; the algorithm predictions are also shown diagrammatically in FIG. 8. In Table 4, window-averaged composite score values that were in the top 10% of the distribution of values are shown in bold type, values that were in the bottom 10% are shown in italics, and all other values are shown with a line through them. It is clear from both Table 4 and FIG. 8 that the window-averaged composite dimensionless score embodiment of the current invention correctly predicted both efficient and inefficient hybridization probes for HIV PRT sense-strand RNA. As in Example 1, statistical sampling of contiguous stretches of predicted “good” probes would lead to convergence of the design process to the best probes in each region in 24 design iterations.

21TABLE 4Window-SEQΔGMFOLDAveragedHIV PRTp5 ProbeIDRNA/DNA(kcal/moleTmΔGMFOLDCompositeCompositeGeneChip ™PositionDNA Probe SequenceNO:Tm (° C.)@ 35° C.)ScoreScoreScoreScoreData1GTACTGTCCATTTATCAGGA14564.16−0.100.557−0.1990.2691152.22TACTGTCCATTTATCAGGAT14660.91−0.400.080−0.460−0.1251040.73ACTGTCCATTTATCAGGATG14761.41−0.900.152−0.895−0.246291.94CTGTCCATTTATCAGGATGG14863.46−0.900.453−0.895−0.059

113

221.85TGTCCATTTATCAGGATGGA14962.82−0.900.360−0.895−0.117

114

148.36GTCCATTTATCAGGATGGAG15063.15−1.900.408−1.764−0.418

115

84.67TCCATTTATCAGGATGGAGT15163.15−2.100.408−1.938−0.484

116

128.78CCATTTATCAGGATGGAGTT15262.03−1.900.245−1.764−0.519

117

94.69CATTTATCAGGATGGAGTTC15359.53−0.60−0.122−0.634−0.317

118

157.510ATTTATCAGGATGGAGTTCA15459.530.80−0.1220.5830.146

119

316.911TTTATCAGGATGGAGTTCAT15559.530.40−0.1220.2360.014

120

360.212TTATCAGGATGGAGTTCATA15658.580.40−0.2620.236−0.073

121

403.813TATCAGGATGGAGTTCATAA15756.210.20−0.6090.062−0.354

122

382.514ATCAGGATGGAGTTCATAAC15857.340.20−0.4440.062−0.252

123

324.415TCAGGATGGAGTTCATAACC15961.250.200.1290.0620.104

124

320.516CAGGATGGAGTTCATAACCC16063.570.200.4700.0620.315

125

238.917AGGATGGAGTTCATAACCCA16163.57−0.100.470−0.1990.216

126

202.318GGATGGAGTTCATAACCCAT16263.34−1.300.436−1.243−0.202

127

113.619GATGGAGTTCATAACCCATC16362.24−2.000.275−1.851−0.533

128

97.720ATGGAGTTCATAACCCATCC16464.62−3.300.624−2.982−0.746

129

143.321TGGAGTTCATAACCCATCCC16568.18−2.001.146−1.8510.007

130

484.622GGAGTTCATAACCCATCCCA16669.39−1.601.324−1.5040.249

131

857.623GAGTTCATAACCCATCCCAA16764.93−0.200.670−0.2860.307

132

991.424AGTTCATAACCCATCCCAAA16861.820.200.2130.0620.155

133

907.025GTTCATAACCCATCCCAAAG16961.820.200.2130.0620.155

134

887.926TTCATAACCCATCCCAAAGG17061.360.600.1450.4100.246

135

1015.327TCATAACCCATCCCAAAGGA17162.21−0.100.270−0.1990.092

136

279.728CATAACCCATCCCAAAGGAA17259.26−0.30−0.163−0.373−0.243

137

210.729ATAACCCATCCCAAAGGAAT17358.19−0.30−0.320−0.373−0.340

138

179.930TAACCCATCCCAAAGGAATG17458.13−0.30−0.328−0.373−0.345

139

91.831AACCCATCCCAAAGGAATGG17560.78−1.300.061−1.243−0.435

140

44.632ACCCATCCCAAAGGAATGGA17663.69−2.000.487−1.851−0.401

141

42.933CCCATCCCAAAGGAATGGAG17763.40−2.200.445−2.025−0.494

142

45.034CCATCCCAAAGGAATGGAGG17862.34−2.300.290−2.112−0.623

143

45.335CATCCCAAAGGAATGGAGGT17961.72−2.600.199−2.373−0.778

144

47.936ATCCCAAAGGAATGGAGGTT18060.90−2.200.079−2.025−0.721

145

49.237TCCCAAAGGAATGGAGGTTC18162.24−2.200.274−2.025−0.600

146

74.238CCCAAAGGAATGGAGGTTCT18262.71−2.000.344−1.851−0.490

147

125.539CCAAAGGAATGGAGGTTCTT18359.47−0.70−0.132−0.721−0.356

148

183.340CAAAGGAATGGAGGTTCTTT18456.10−0.30−0.627−0.373−0.530

149

261.441AAAGGAATGGAGGTTCTTTC18556.11−0.30−0.625−0.373−0.529

150

518.342AAGGAATGGAGGTTCTTTCT18660.05−0.30−0.046−0.373−0.170

151

716.543AGGAATGGAGGTTCTTTCTG18762.09−0.300.253−0.3730.015

152

1056.044GGAATGGAGGTTCTTTCTGA18863.23−0.300.420−0.3730.119

153

1084.345GAATGGAGGTTCTTTCTGAT18960.560.100.028−0.0250.008

154

1241.146AATGGAGGTTCTTTCTGATG19059.120.30−0.1830.149−0.057

155

1278.847ATGGAGGTTCTTTCTGATGT19164.580.300.6180.1490.440

156

1616.048TGGAGGTTCTTTCTGATGTT19264.980.300.6770.1490.476

157

1677.549GGAGGTTCTTTCTGATGTTT19365.490.300.7510.1490.522

158

1963.150GAGGTTCTTTCTGATGTTTT19463.040.300.3920.1490.300

159

2126.151AGGTTCTTTCTGATGTTTTT19561.970.300.2350.1490.202

160

2143.352GGTTCTTTCTGATGTTTTTT19662.110.300.2560.1490.215

161

3540.653GTTCTTTCTGATGTTTTTTG19759.210.30−0.1700.149−0.049

162

1728.754TTCTTTCTGATGTTTTTTGT19859.210.30−0.1700.149−0.049

163

1364.355TCTTTCTGATGTTTTTTGTC19960.350.50−0.0020.3230.121

164

1788.456CTTTCTGATGTTTTTTGTCT20060.961.200.0860.9310.407

165

2670.957TTTCTGATGTTTTTTGTCTG20158.761.20−0.2350.9310.208

166

3336.258TTCTGATGTTTTTTGTCTGG20261.171.200.1180.9310.427

167

6683.659TCTGATGTTTTTTGTCTGGT20364.201.200.5620.9310.702

168

10227.060CTGATGTTTTTTGTCTGGTG20462.511.200.3150.9310.549

169

10965.061TGATGTTTTTTGTCTGGTGT20563.801.200.5040.9310.666

170

11133.062GATGTTTTTTGTCTGGTGTG20663.801.600.5041.2790.7980.89411503.063ATGTTTTTTGTCTGGTGTGG20765.181.900.7051.5401.0230.8949492.864TGTTTTTTGTCTGGTGTGGT20868.781.701.2341.3661.2840.91410704.065GTTTTTTGTCTGGTGTGGTA20968.281.701.1611.3661.2390.93310741.066TTTTTTGTCTGGTGTGGTAA21062.371.700.2941.3660.7010.9509187.567TTTTTGTCTGGTGTGGTAAG21162.231.700.2731.3660.6890.9417871.068TTTTGTCTGGTGTGGTAAGT21265.281.200.7210.9310.8010.9217209.169TTTGTCTGGTGTGGTAAGTC21366.561.200.9080.9310.9170.9598052.370TTGTCTGGTGTGGTAAGTCC21470.250.301.4490.1490.9551.0227230.671TGTCTGGTGTGGTAAGTCCC21573.77−0.101.966−0.1991.1430.9986809.572GTCTGGTGTGGTAAGTCCCC21677.74−0.102.549−0.1991.5040.9137442.873TCTGGTGTGGTAAGTCCCCA21775.28−0.502.187−0.5471.148

171

2627.774CTGGTGTGGTAAGTCCCCAC21874.18−2.102.026−1.9380.519

172

1315.075TGGTGTGGTAAGTCCCCACC21975.80−3.502.263−3.1560.204

173

4182.376GGTGTGGTAAGTCCCCACCT22077.89−3.802.571−3.4170.296

174

474.777GTGTGGTAAGTCCCCACCTC22177.05−2.502.448−2.2860.649

175

682.478TGTGGTAAGTCCCCACCTCA22274.71−2.502.105−2.2860.436

176

679.179GTGGTAAGTCCCCACCTCAA22372.54−2.101.785−1.9380.370

177

924.080TGGTAAGTCCCCACCTCAAC22469.94−0.901.404−0.8950.531

178

835.581GGTAAGTCCCCACCTCAACA22571.14−0.501.580−0.5470.772

179

1213.682GTAAGTCCCCACCTCAACAG22668.970.901.2620.6701.037

180

1106.183TAAGTCCCCACCTCAACAGA22767.180.900.9990.6700.8740.8721009.084AAGTCCCCACCTCAACAGAT22867.680.501.0730.3230.7880.9081656.285AGTCCCCACCTCAACAGATG22969.680.501.3660.3230.970

181

2178.386GTCCCCACCTCAACAGATGT23072.560.201.7890.0621.132

182

2567.087TCCCCACCTCAACAGATGTT23169.77−0.101.379−0.1990.779

183

3000.588CCCCACCTCAACAGATGTTG23268.19−1.301.148−1.2430.240

184

2025.489CCCACCTCAACAGATGTTGT23367.78−2.001.087−1.851−0.030

185

429.290CCACCTCAACAGATGTTGTC23465.65−2.000.775−1.851−0.223

186

157.991CACCTCAACAGATGTTGTCT23563.85−2.000.511−1.851−0.387

187

135.392ACCTCAACAGATGTTGTCTC23664.11−2.000.549−1.851−0.363

188

330.893CCTCAACAGATGTTGTCTCA23764.77−2.000.646−1.851−0.303

189

900.094CTCAACAGATGTTGTCTCAG23861.08−2.000.104−1.851−0.639

190

1177.095TCAACAGATGTTGTCTCAGC23963.40−2.000.444−1.851−0.428

191

795.196CAACAGATGTTGTCTCAGCT24063.91−1.600.520−1.504−0.249

192

889.297AACAGATGTTGTCTCAGCTC24164.19−0.100.560−0.1990.272

193

1703.698ACAGATGTTGTCTCAGCTCC24270.610.001.503−0.1120.889

194

3115.299CAGATGTTGTCTCAGCTCCT24372.080.001.719−0.1121.0230.8474445.0100AGATGTTGTCTCAGCTCCTC24472.660.201.8030.0621.1411.0706762.8101GATGTTGTCTCAGCTCCTCT24574.490.902.0710.6701.5391.2278845.0102ATGTTGTCTCAGCTCCTCTA24672.380.801.7630.5831.3141.2539010.6103TGTTGTCTCAGCTCCTCTAT24772.380.801.7630.5831.3141.26019941.0104GTTGTCTCAGCTCCTCTATT24872.970.801.8490.5831.3681.25712577.0105TTGTCTCAGCTCCTCTATTT24969.700.801.3690.5831.0711.1497503.3106TGTCTCAGCTCCTCTATTTT25069.700.801.3690.5831.0711.0987033.8107GTCTCAGCTCCTCTATTTTT25170.260.801.4510.5831.1211.0248276.7108TCTCAGCTCCTCTATTTTTG25266.570.800.9100.5830.7860.9422899.0109CTCAGCTCCTCTATTTTTGT25368.390.801.1770.5830.9520.9232935.0110TCAGCTCCTCTATTTTTGTT25466.690.800.9270.5830.7960.9301512.8111CAGCTCCTCTATTTTTGTTC25566.690.800.9270.5830.7960.8721708.8112AGCTCCTCTATTTTTGTTCT25667.521.001.0500.7570.9390.8331977.3113GCTCCTCTATTTTTGTTCTA25766.631.800.9191.4531.122

195

2114.8114CTCCTCTATTTTTGTTCTAT25862.131.800.2591.4530.713

196

1527.3115TCCTCTATTTTTGTTCTATG25959.971.80−0.0581.4530.516

197

1536.8116CCTCTATTTTTGTTCTATGC26062.841.800.3631.4530.777

198

1824.5117CTCTATTTTTGTTCTATGCT26160.871.500.0741.1920.499

199

1169.2118TCTATTTTTGTTCTATGCTG26258.711.50−0.2441.1920.302

200

683.7119CTATTTTTGTTCTATGCTGC26361.601.500.1811.1920.565

201

1306.8120TATTTTTGTTCTATGCTGCC26463.531.500.4641.1920.741

202

2523.6121ATTTTTGTTCTATGCTGCCC26567.961.501.1131.1921.1430.9316682.0122TTTTTGTTCTATGCTGCCCT26669.961.501.4071.1921.3251.0609417.4123TTTTGTTCTATGCTGCCCTA26769.011.501.2671.1921.2391.15110339.0124TTTGTTCTATGCTGCCCTAT26868.621.501.2101.1921.2031.25410750.0125TTGTTCTATGCTGCCCTATT26968.621.501.2101.1921.2031.28211180.0126TGTTCTATGCTGCCCTATTT27068.621.501.2101.1921.2031.27111060.0127GTTCTATGCTGCCCTATTTC27170.371.801.4681.4531.4621.22116074.0128TTCTATGCTGCCCTATTTCT27269.001.801.2661.4531.3371.1449183.8129TCTATGCTGCCCTATTTCTA27368.051.801.1271.4531.2511.0828617.8130CTATGCTGCCCTATTTCTAA27464.381.700.5891.3660.8841.0407286.8131TATGCTGCCCTATTTCTAAG27562.711.500.3441.1920.6660.9783642.4132ATGCTGCCCTATTTCTAAGT27666.390.800.8830.5830.7690.8833799.7133TGCTGCCCTATTTCTAAGTC27767.950.801.1120.5830.911

203

3408.3134GCTGCCCTATTTCTAAGTCA27869.250.801.3030.5831.030

204

4017.4135CTGCCCTATTTCTAAGTCAG27965.260.800.7180.5830.667

205

2197.2136TGCCCTATTTCTAAGTCAGA28064.63−0.100.626−0.1990.312

206

1125.0137GCCCTATTTCTAAGTCAGAT28164.73−0.600.639−0.6340.156

207

1306.3138CCCTATTTCTAAGTCAGATC28261.98−0.600.236−0.634−0.094

208

1019.5139CCTATTTCTAAGTCAGATCC28361.98−0.600.236−0.634−0.094

209

1852.3140CTATTTCTAAGTCAGATCCT28460.05−0.60−0.046−0.634−0.270

210

3159.3141TATTTCTAAGTCAGATCCTA28557.43−0.60−0.430−0.634−0.508

211

2604.8142ATTTCTAAGTCAGATCCTAC28658.59−0.60−0.261−0.634−0.402

212

3986.1143TTTCTAAGTCAGATCCTACA28759.91−0.60−0.068−0.634−0.283

213

4500.7144TTCTAAGTCAGATCCTACAT28859.55−0.60−0.120−0.634−0.315

214

4754.5145TCTAAGTCAGATCCTACATA28958.62−0.40−0.257−0.460−0.334

215

3802.1146CTAAGTCAGATCCTACATAC29057.801.20−0.3770.9310.120

216

5069.4147TAAGTCAGATCCTACATACA29157.131.30−0.4761.0180.092

217

3965.2148AAGTCAGATCCTACATACAA29255.781.30−0.6731.018−0.030

218

3862.3149AGTCAGATCCTACATACAAA29355.781.30−0.6731.018−0.030

219

2868.9150GTCAGATCCTACATACAAAT29455.621.70−0.6971.3660.087

220

3542.9151TCAGATCCTACATACAAATC29554.021.50−0.9321.192−0.125

221

2477.1152CAGATCCTACATACAAATCA29654.071.10−0.9240.844−0.252

222

2522.4153AGATCCTACATACAAATCAT29752.831.10−1.1060.844−0.365

223

2554.6154GATCCTACATACAAATCATC29853.871.50−0.9531.192−0.138

224

3580.0155ATCCTACATACAAATCATCC29956.331.80−0.5911.4530.185

225

5937.7156TCCTACATACAAATCATCCA30057.541.80−0.4151.4530.295

226

4606.7157CCTACATACAAATCATCCAT30156.321.80−0.5941.4530.184

227

4877.2158CTACATACAAATCATCCATG30252.681.10−1.1280.844−0.379

228

2608.6159TACATACAAATCATCCATGT30353.560.30−0.9990.149−0.563

229

1491.7160ACATACAAATCATCCATGTA30453.56−0.10−0.999−0.199−0.695

230

1364.3161CATACAAATCATCCATGTAT30553.07−0.80−1.071−0.808−0.971−0.7511089.8162ATACAAATCATCCATGTATT30652.11−1.10−1.211−1.069−1.157−0.8181008.6163TACAAATCATCCATGTATTG30752.08−0.40−1.215−0.460−0.928−0.891624.8164ACAAATCATCCATGTATTGA30853.860.20−0.9550.062−0.568−0.921535.8165CAAATCATCCATGTATTGAT30953.36−0.50−1.027−0.547−0.845−0.8603019.6166AAATCATCCATGTATTGATA31051.57−0.70−1.291−0.721−1.074−0.753214.0167AATCATCCATGTATTGATAG31153.47−0.70−1.012−0.721−0.901

231

212.7168ATCATCCATGTATTGATAGA31256.66−0.50−0.543−0.547−0.545

232

165.2169TCATCCATGTATTGATAGAT31356.66−0.10−0.543−0.199−0.412

233

166.0170CATCCATGTATTGATAGATA31454.800.30−0.8170.149−0.450

234

151.0171ATCCATGTATTGATAGATAA31551.690.30−1.2730.149−0.733

235

101.8172TCCATGTATTGATAGATAAC31652.190.30−1.1990.149−0.687

236

84.0173CCATGTATTGATAGATAACT31752.890.30−1.0970.149−0.623−0.850130.3174CATGTATTGATAGATAACTA31848.470.70−1.7460.496−0.894−0.93767.8175ATGTATTGATAGATAACTAT31947.120.00−1.944−0.112−1.248−1.00665.7176TGTATTGATAGATAACTATG32047.11−0.20−1.945−0.286−1.315−1.04890.0177GTATTGATAGATAACTATGT32149.90−0.20−1.536−0.286−1.061−1.099125.9178TATTGATAGATAACTATGTC32248.24−0.20−1.779−0.286−1.212−1.083132.6179ATTGATAGATAACTATGTCT32350.78−0.20−1.407−0.286−0.981−0.998167.4180TTGATAGATAACTATGTCTG32450.75−0.20−1.411−0.286−0.984−0.916219.0181TGATAGATAACTATGTCTGG32553.01−0.20−1.080−0.286−0.778−0.866722.6182GATAGATAACTATGTCTGGA32654.36−0.20−0.881−0.286−0.655−0.774825.1183ATAGATAACTATGTCTGGAT32753.04−0.10−1.074−0.199−0.742

237

844.4184TAGATAACTATGTCTGGATT32853.37−0.10−1.027−0.199−0.712

238

912.6185AGATAACTATGTCTGGATTT32954.270.10−0.895−0.025−0.565

239

1301.8186GATAACTATGTCTGGATTTT33054.430.80−0.8700.583−0.318

240

1367.4187ATAACTATGTCTGGATTTTG33153.081.50−1.0701.192−0.210

241

1284.2188TAACTATGTCTGGATTTTGT33256.051.50−0.6341.1920.060

242

1162.5189AACTATGTCTGGATTTTGTT33356.971.50−0.4991.1920.144

243

1396.7190ACTATGTCTGGATTTTGTTT33459.381.50−0.1451.1920.363

244

1348.3191CTATGTCTGGATTTTGTTTT33559.161.50−0.1771.1920.343

245

1092.8192TATGTCTGGATTTTGTTTTT33657.451.50−0.4281.1920.188

246

912.6193ATGTCTGGATTTTGTTTTTT33758.411.70−0.2871.3660.341

247

994.3194TGTCTGGATTTTGTTTTTTA33857.812.00−0.3751.6270.386

248

840.7195GTCTGGATTTTGTTTTTTAA33955.821.00−0.6670.757−0.126

249

941.9196TCTGGATTTTGTTTTTTAAA34050.980.80−1.3770.583−0.632

250

84.9197CTGGATTTTGTTTTTTAAAA34148.160.30−1.7900.149−1.054

251

78.6198TGGATTTTGTTTTTTAAAAG34246.410.10−2.048−0.025−1.279−0.85193.2199GGATTTTGTTTTTTAAAAGG34348.870.10−1.686−0.025−1.055−0.93356.0200GATTTTGTTTTTTAAAAGGC34450.220.10−1.488−0.025−0.932−0.91249.9201ATTTTGTTTTTTAAAAGGCT34550.840.10−1.397−0.025−0.876−0.84355.0202TTTTGTTTTTTAAAAGGCTC34652.030.30−1.2230.149−0.702−0.76864.6203TTTGTTTTTTAAAAGGCTCT34753.640.50−0.9870.323−0.489

252

162.8204TTGTTTTTTAAAAGGCTCTA34852.760.50−1.1150.323−0.569

253

265.8205TGTTTTTTAAAAGGCTCTAA34950.710.50−1.4170.323−0.756

254

288.5206GTTTTTTAAAAGGCTCTAAG35050.860.50−1.3950.323−0.742

255

548.4207TTTTTTAAAAGGCTCTAAGA35149.400.70−1.6090.496−0.809

256

524.7208TTTTTAAAAGGCTCTAAGAT35249.111.20−1.6510.931−0.670−0.746937.9209TTTTAAAAGGCTCTAAGATT35349.111.20−1.6510.931−0.670−0.7901440.3210TTTAAAAGGCTCTAAGATTT35449.111.20−1.6510.931−0.670−0.8201633.3211TTAAAAGGCTCTAAGATTTT35549.110.50−1.6510.323−0.901−0.7351987.4212TAAAAGGCTCTAAGATTTTT35649.110.00−1.651−0.112−1.067

257

1792.3213AAAAGGCTCTAAGATTTTTG35749.630.20−1.5750.062−0.953

258

2218.9214AAAGGCTCTAAGATTTTTGT35854.131.20−0.9140.931−0.213

259

2371.4215AAGGCTCTAAGATTTTTGTC35957.381.20−0.4390.9310.082

260

3308.9216AGGCTCTAAGATTTTTGTCA36060.780.800.0610.5830.260

261

4070.5217GGCTCTAAGATTTTTGTCAT36160.560.800.0280.5830.239

262

5394.5218GCTCTAAGATTTTTGTCATG36257.810.80−0.3760.583−0.011

263

2025.5219CTCTAAGATTTTTGTCATGC36357.810.80−0.3760.583−0.011

264

1741.9220TCTAAGATTTTTGTCATGCT36457.810.80−0.3760.583−0.011

265

1707.6221CTAAGATTTTTGTCATGCTA36555.870.80−0.6600.583−0.187

266

1783.0222TAAGATTTTTGTCATGCTAC36654.430.80−0.8720.583−0.319

267

3131.4223AAGATTTTTGTCATGCTACT36756.990.60−0.4950.410−0.151

268

4892.5224AGATTTTTGTCATGCTACTT36859.390.60−0.1440.4100.067

269

5856.4225GATTTTTGTCATGCTACTTT36959.540.60−0.1220.4100.080

270

6439.0226ATTTTTGTCATGCTACTTTG37058.090.60−0.3340.410−0.051

271

5820.3227TTTTTGTCATGCTACTTTGG37160.780.600.0600.4100.193

272

5189.6228TTTTGTCATGCTACTTTGGA37261.790.600.2090.4100.285

273

4721.7229TTTGTCATGCTACTTTGGAA37359.350.60−0.1490.4100.063

274

4221.0230TTGTCATGCTACTTTGGAAT37459.000.60−0.2000.4100.032

275

4279.0231TGTCATGCTACTTTGGAATA37558.100.60−0.3330.410−0.051

276

4102.0232GTCATGCTACTTTGGAATAT37658.160.90−0.3240.6700.054

277

5069.8233TCATGCTACTTTGGAATATT37755.520.90−0.7110.670−0.186

278

2407.9234CATGCTACTTTGGAATATTG37854.231.30−0.9001.018−0.171

279

2443.0235ATGCTACTTTGGAATATTGC37956.901.40−0.5081.1050.105

280

2324.3236TGCTACTTTGGAATATTGCT38058.820.90−0.2270.6700.114

281

1894.1237GCTACTTTGGAATATTGCTG38158.821.30−0.2271.0180.246

282

2363.8238CTACTTTGGAATATTGCTGG38257.351.70−0.4431.3660.244

283

1363.0239TACTTTGGAATATTGCTGGT38358.391.70−0.2901.3660.339

284

1217.5240ACTTTGGAATATTGCTGGTG38458.881.70−0.2171.3660.384

285

1621.8241CTTTGGAATATTGCTGGTGA38559.641.70−0.1061.3660.453

286

1438.2242TTTGGAATATTGCTGGTGAT38657.721.80−0.3881.4530.311

287

1608.0243TTGGAATATTGCTGGTGATC38758.731.80−0.2411.4530.403

288

2334.6244TGGAATATTGCTGGTGATCC38862.180.500.2660.3230.288

289

3776.7245GGAATATTGCTGGTGATCCT38964.19−0.200.561−0.2860.239

290

5648.7246GAATATTGCTGGTGATCCTT39061.99−0.200.238−0.2860.039

291

5358.8247AATATTGCTGGTGATCCTTT39161.03−0.200.097−0.286−0.049

292

5517.2248ATATTGCTGGTGATCCTTTC39264.63−0.200.625−0.2860.279

293

6246.4249TATTGCTGGTGATCCTTTCC39368.48−0.201.190−0.2860.629

294

9975.1250ATTGCTGGTGATCCTTTCCA39470.22−0.201.446−0.2860.788

295

11990.0251TTGCTGGTGATCCTTTCCAT39570.22−0.601.446−0.6340.655

296

11543.0252TGCTGGTGATCCTTTCCATC39671.48−0.601.631−0.6340.7700.86214125.0253GCTGGTGATCCTTTCCATCC39775.32−0.602.193−0.6341.1190.93623489.0254CTGGTGATCCTTTCCATCCC39874.58−0.602.085−0.6341.0521.02215975.0255TGGTGATCCTTTCCATCCCT39974.58−0.702.085−0.7211.0191.08216053.0256GGTGATCCTTTCCATCCCTG40074.58−0.302.085−0.3731.1511.13619205.0257GTGATCCTTTCCATCCCTGT40175.400.202.2060.0621.3911.08017872.0258TGATCCTTTCCATCCCTGTG40271.890.201.6910.0621.0720.95512871.0259GATCCTTTCCATCCCTGTGG40374.58−0.302.085−0.3731.151

297

8792.7260ATCCTTTCCATCCCTGTGGA40474.58−1.602.085−1.5040.721

298

5609.6261TCCTTTCCATCCCTGTGGAA40572.27−2.601.746−2.3730.181

299

3018.0262CCTTTCCATCCCTGTGGAAG40671.00−2.801.559−2.547−0.001

300

1802.6263CTTTCCATCCCTGTGGAAGC40771.60−2.801.648−2.5470.054

301

1074.0264TTTCCATCCCTGTGGAAGCA40870.81−2.801.532−2.547−0.018

302

1132.5265TTCCATCCCTGTGGAAGCAC40971.02−2.601.562−2.3730.067

303

1454.5266TCCATCCCTGTGGAAGCACA41071.74−1.701.669−1.5910.430

304

1676.8267CCATCCCTGTGGAAGCACAT41170.20−2.201.443−2.0250.125

305

2268.9268CATCCCTGTGGAAGCACATT41267.07−2.200.983−2.025−0.160

306

1682.6269ATCCCTGTGGAAGCACATTG41365.82−2.200.801−2.025−0.273

307

1753.9270TCCCTGTGGAAGCACATTGT41468.98−2.201.263−2.0250.014

308

1281.8271CCCTGTGGAAGCACATTGTA41566.92−2.200.962−2.025−0.173

309

1227.8272CCTGTGGAAGCACATTGTAC41663.84−2.200.509−2.025−0.454

310

700.3273CTGTGGAAGCACATTGTACT41762.01−2.200.241−2.025−0.620

311

618.7274TGTGGAAGCACATTGTACTG41859.99−2.00−0.056−1.851−0.738

312

771.5275GTGGAAGCACATTGTACTGA41961.39−0.500.149−0.547−0.115

313

1180.6276TGGAAGCACATTGTACTGAT42058.350.50−0.2960.323−0.061

314

1160.5277GGAAGCACATTGTACTGATA42157.860.50−0.3680.323−0.106

315

1314.7278GAAGCACATTGTACTGATAT42255.320.50−0.7400.323−0.336

316

1102.5279AAGCACATTGTACTGATATC42355.300.50−0.7440.323−0.339

317

1222.1280AGCACATTGTACTGATATCT42459.260.50−0.1620.3230.022

318

1893.2281GCACATTGTACTGATATCTA42558.480.50−0.2770.323−0.049

319

2097.7282CACATTGTACTGATATCTAA42652.510.50−1.1520.323−0.592

320

1237.8283ACATTGTACTGATATCTAAT42751.200.50−1.3450.323−0.711

321

959.5284CATTGTACTGATATCTAATC42851.890.10−1.244−0.025−0.781

322

1149.1285ATTGTACTGATATCTAATCC42954.53−0.30−0.856−0.373−0.672

323

2351.3286TTGTACTGATATCTAATCCC43058.41−0.30−0.287−0.373−0.320

324

4191.6287TGTACTGATATCTAATCCCT43159.99−0.30−0.055−0.373−0.176

325

5565.8288GTACTGATATCTAATCCCTG43259.99−0.30−0.055−0.373−0.176

326

9980.2289TACTGATATCTAATCCCTGG43359.52−0.30−0.124−0.373−0.218

327

6318.9290ACTGATATCTAATCCCTGGT43463.07−0.300.397−0.3730.104

328

7749.5291CTGATATCTAATCCCTGGTG43562.43−0.300.303−0.3730.046

329

8165.3292TGATATCTAATCCCTGGTGT43663.60−0.300.474−0.3730.152

330

9107.6293GATATCTAATCCCTGGTGTC43765.190.100.707−0.0250.429

331

13914.0294ATATCTAATCCCTGGTGTCT43865.821.500.8001.1920.949

332

15093.0295TATCTAATCCCTGGTGTCTC43967.411.501.0331.1921.093

333

18647.0296ATCTAATCCCTGGTGTCTCA44069.201.301.2961.0181.1900.90421810.0297TCTAATCCCTGGTGTCTCAT44169.200.801.2960.5831.0250.99620102.0298CTAATCCCTGGTGTCTCATT44267.980.801.1170.5830.9141.05220967.0299TAATCCCTGGTGTCTCATTG44365.900.800.8110.5830.7251.09218200.0300AATCCCTGGTGTCTCATTGT44469.780.801.3800.5831.0771.08819845.0301ATCCCTGGTGTCTCATTGTT44572.610.801.7970.5831.3361.05719231.0302TCCCTGGTGTCTCATTGTTT44673.040.801.8600.5831.3750.98117629.0303CCCTGGTGTCTCATTGTTTA44770.720.801.5190.5831.1640.91817009.0304CCTGGTGTCTCATTGTTTAT44866.820.800.9460.5830.808

334

11580.0305CTGGTGTCTCATTGTTTATA44962.170.800.2640.5830.386

335

8374.6306TGGTGTCTCATTGTTTATAC45060.650.900.0420.6700.281

336

6153.3307GGTGTCTCATTGTTTATACT45162.880.200.3690.0620.252

337

7134.0308GTGTCTCATTGTTTATACTA45259.430.20−0.1380.062−0.062

338

4435.2309TGTCTCATTGTTTATACTAG45356.350.20−0.5890.062−0.342

339

2035.5310GTCTCATTGTTTATACTAGG45459.210.20−0.1700.062−0.082

340

2466.6311TCTCATTGTTTATACTAGGT45559.210.20−0.1700.062−0.082

341

1080.9312CTCATTGTTTATACTAGGTA45657.150.20−0.4720.062−0.269

342

956.0313TCATTGTTTATACTAGGTAT45755.080.20−0.7760.062−0.458

343

529.4314CATTGTTTATACTAGGTATG45853.700.20−0.9780.062−0.583

344

471.4315ATTGTTTATACTAGGTATGG45955.010.20−0.7850.062−0.463

345

510.4316TTGTTTATACTAGGTATGGT46058.170.20−0.3220.062−0.176

346

531.0317TGTTTATACTAGGTATGGTA46157.210.20−0.4630.062−0.264

347

613.3318GTTTATACTAGGTATGGTAA46255.230.00−0.753−0.112−0.510

348

685.1319TTTATACTAGGTATGGTAAA46350.420.00−1.459−0.112−0.947

349

300.0320TTATACTAGGTATGGTAAAT46450.120.00−1.504−0.112−0.975

350

316.1321TATACTAGGTATGGTAAATG46549.790.00−1.551−0.112−1.004

351

387.5322ATACTAGGTATGGTAAATGC46654.300.00−0.889−0.112−0.594

352

685.7323TACTAGGTATGGTAAATGCA46755.590.20−0.7000.062−0.411

353

759.6324ACTAGGTATGGTAAATGCAG46856.320.80−0.5930.583−0.146

354

1050.2325CTAGGTATGGTAAATGCAGT46958.781.10−0.2320.8440.177

355

1020.4326TAGGTATGGTAAATGCAGTA47056.241.10−0.6050.844−0.054

356

742.6327AGGTATGGTAAATGCAGTAT47156.811.10−0.5210.844−0.002

357

889.6328GGTATGGTAAATGCAGTATA47256.071.10−0.6310.844−0.070

358

858.8329GTATGGTAAATGCAGTATAC47354.021.10−0.9310.844−0.256

359

379.0330TATGGTAAATGCAGTATACT47453.060.40−1.0710.236−0.575

360

166.7331ATGGTAAATGCAGTATACTT47553.940.40−0.9430.236−0.495

361

215.3332TGGTAAATGCAGTATACTTC47655.210.40−0.7570.236−0.380

362

103.2333GGTAAATGCAGTATACTTCC47759.150.40−0.1780.236−0.021

363

246.3334GTAAATGCAGTATACTTCCT47858.530.80−0.2690.5830.055

364

163.4335TAAATGCAGTATACTTCCTG47955.540.10−0.708−0.025−0.448

365

294.1336AAATGCAGTATACTTCCTGA48057.36−0.30−0.441−0.373−0.415

366

531.4337AATGCAGTATACTTCCTGAA48157.36−0.30−0.441−0.373−0.415

367

1995.5338ATGCAGTATACTTCCTGAAG48259.50−0.30−0.128−0.373−0.221

368

510.1339TGCAGTATACTTCCTGAAGT48362.63−0.900.332−0.895−0.134

369

555.4340GCAGTATACTTCCTGAAGTC48464.24−1.100.568−1.069−0.054

370

1214.0341CAGTATACTTCCTGAAGTCT48561.94−1.100.230−1.069−0.263

371

825.7342AGTATACTTCCTGAAGTCTT48661.00−1.100.094−1.069−0.348

372

1582.6343GTATACTTCCTGAAGTCTTC48762.28−1.100.281−1.069−0.232

373

2391.8344TATACTTCCTGAAGTCTTCA48860.34−1.10−0.004−1.069−0.409

374

2276.3345ATACTTCCTGAAGTCTTCAT48960.91−1.200.080−1.156−0.389

375

2702.8346TACTTCCTGAAGTCTTCATC49062.40−1.200.299−1.156−0.254

376

3781.7347ACTTCCTGAAGTCTTCATCT49165.05−1.200.686−1.156−0.014

377

5343.4348CTTCCTGAAGTCTTCATCTA49263.86−1.200.512−1.156−0.122

378

6309.0349TTCCTGAAGTCTTCATCTAA49359.70−1.20−0.098−1.156−0.500

379

6372.4350TCCTGAAGTCTTCATCTAAG49459.55−1.20−0.120−1.156−0.513

380

3835.3351CCTGAAGTCTTCATCTAAGG49560.76−1.200.057−1.156−0.404

381

8925.5352CTGAAGTCTTCATCTAAGGG49659.48−1.20−0.130−1.156−0.520

382

1211.8353TGAAGTCTTCATCTAAGGGA49758.84−1.00−0.224−0.982−0.512

383

609.4354GAAGTCTTCATCTAAGGGAA49856.91−0.10−0.507−0.199−0.390

384

629.1355AAGTCTTCATCTAAGGGAAC49956.13−0.10−0.622−0.199−0.461

385

749.3356AGTCTTCATCTAAGGGAACT50060.12−0.10−0.036−0.199−0.098

386

805.6357GTCTTCATCTAAGGGAACTG50159.84−0.10−0.077−0.199−0.124

387

817.0358TCTTCATCTAAGGGAACTGA50258.11−0.10−0.331−0.199−0.281

388

327.1359CTTCATCTAAGGGAACTGAA50354.95−0.60−0.794−0.634−0.733

389

320.0360TTCATCTAAGGGAACTGAAA50451.39−0.60−1.316−0.634−1.057−0.82284.1361TCATCTAAGGGAACTGAAAA50549.500.10−1.595−0.025−0.998−1.00267.7362CATCTAAGGGAACTGAAAAA50646.980.10−1.963−0.025−1.227−1.17162.2363ATCTAAGGGAACTGAAAAAT50745.780.10−2.140−0.025−1.336−1.29878.9364TCTAAGGGAACTGAAAAATA50845.270.10−2.214−0.025−1.382−1.32843.2365CTAAGGGAACTGAAAAATAT50944.360.10−2.349−0.025−1.466−1.32250.4366TAAGGGAACTGAAAAATATG51042.710.10−2.591−0.025−1.616−1.24243.7367AAGGGAACTGAAAAATATGC51146.540.10−2.028−0.025−1.267−1.16345.6368AGGGAACTGAAAAATATGCA51249.210.30−1.6370.149−0.958−1.11949.8369GGGAACTGAAAAATATGCAT51349.111.20−1.6510.931−0.670−1.08253.2370GGAACTGAAAAATATGCATC51447.871.20−1.8340.931−0.783−0.95856.6371GAACTGAAAAATATGCATCA51546.820.60−1.9870.410−1.076−0.84445.3372AACTGAAAAATATGCATCAC51646.120.40−2.0900.236−1.206−0.77356.3373ACTGAAAAATATGCATCACC51751.180.40−1.3470.236−0.746

390

61.7374CTGAAAAATATGCATCACCC51854.200.40−0.9050.236−0.471

391

224.5375TGAAAAATATGCATCACCCA51953.650.60−0.9850.410−0.455

392

413.0376GAAAAATATGCATCACCCAC52054.141.30−0.9131.018−0.179

393

1584.0377AAAAATATGCATCACCCACA52154.141.30−0.9131.018−0.179

394

1846.7378AAAATATGCATCACCCACAT52255.781.10−0.6730.844−0.096

395

2445.8379AAATATGCATCACCCACATC52358.720.90−0.2410.6700.105

396

3709.4380AATATGCATCACCCACATCC52464.130.900.5520.6700.597

397

4548.4381ATATGCATCACCCACATCCA52567.270.901.0130.6700.883

398

5254.1382TATGCATCACCCACATCCAG52667.530.901.0510.6700.9060.8645527.2383ATGCATCACCCACATCCAGT52771.210.901.5900.6701.2410.9916916.9384TGCATCACCCACATCCAGTA52870.680.701.5130.4961.1271.0305861.4385GCATCACCCACATCCAGTAC52971.390.701.6170.4961.1911.0438078.4386CATCACCCACATCCAGTACT53069.160.701.2900.4960.9881.0134148.8387ATCACCCACATCCAGTACTG53167.910.701.1070.4960.8750.9133317.1388TCACCCACATCCAGTACTGT53271.150.101.582−0.0250.971

399

2486.4389CACCCACATCCAGTACTGTT53369.94−0.401.404−0.4600.696

400

2746.4390ACCCACATCCAGTACTGTTA53468.25−0.401.157−0.4600.543

401

2133.0391CCCACATCCAGTACTGTTAC53568.25−0.401.157−0.4600.543

402

2197.0392CCACATCCAGTACTGTTACT53666.50−0.400.900−0.4600.383

403

1824.0393CACATCCAGTACTGTTACTG53762.61−1.900.329−1.764−0.467

404

1675.2394ACATCCAGTACTGTTACTGA53862.71−2.300.344−2.112−0.590

405

1219.8395CATCCAGTACTGTTACTGAT53962.12−2.300.258−2.112−0.643

406

1414.0396ATCCAGTACTGTTACTGATT54061.21−2.300.124−2.112−0.726

407

1710.7397TCCAGTACTGTTACTGATTT54161.58−2.300.178−2.112−0.692

408

2280.7398CCAGTACTGTTACTGATTTT54260.48−2.300.017−2.112−0.792

409

2847.7399CAGTACTGTTACTGATTTTT54356.84−1.90−0.518−1.764−0.992

410

2830.2400AGTACTGTTACTGATTTTTT54455.82−0.30−0.666−0.373−0.555

411

4336.3401GTACTGTTACTGATTTTTTC54557.040.40−0.4880.236−0.213

412

6581.1402TACTGTTACTGATTTTTTCT54655.95−0.10−0.649−0.199−0.478

413

5406.6403ACTGTTACTGATTTTTTCTT54756.89−0.10−0.510−0.199−0.392

414

6083.1404CTGTTACTGATTTTTTCTTT54856.67−0.10−0.542−0.199−0.412

415

6585.7405TGTTACTGATTTTTTCTTTT54954.96−0.10−0.793−0.199−0.567

416

3923.2406GTTACTGATTTTTTCTTTTT55055.36−0.10−0.734−0.199−0.531

417

4093.5407TTACTGATTTTTTCTTTTTT55152.62−0.10−1.136−0.199−0.780

418

1381.5408TACTGATTTTTTCTTTTTTA55251.70−0.10−1.272−0.199−0.864−0.7841194.3409ACTGATTTTTTCTTTTTTAA55350.45−0.10−1.454−0.199−0.977−0.7462371.3410CTGATTTTTTCTTTTTTAAC55450.45−0.10−1.454−0.199−0.977

419

395.9411TGATTTTTTCTTTTTTAACC55552.50−0.10−1.155−0.199−0.792

420

230.7412GATTTTTTCTTTTTTAACCC55656.430.30−0.5780.149−0.302

421

314.9413ATTTTTTCTTTTTTAACCCT55757.050.80−0.4870.583−0.080

422

276.1414TTTTTTCTTTTTTAACCCTG55856.990.80−0.4950.583−0.085

423

273.3415TTTTTCTTTTTTAACCCTGC55960.680.800.0450.5830.250

424

628.4416TTTTCTTTTTTAACCCTGCG56060.850.800.0710.5830.265

425

4661.4417TTTCTTTTTTAACCCTGCGG56162.930.700.3770.4960.422

426

411.2418TTCTTTTTTAACCCTGCGGG56265.01−0.600.681−0.6340.181

427

289.5419TCTTTTTTAACCCTGCGGGA56365.91−1.000.813−0.9820.131

428

244.8420CTTTTTTAACCCTGCGGGAT56464.52−1.000.610−0.9820.005

429

250.7421TTTTTTAACCCTGCGGGATG56562.66−1.000.337−0.982−0.164

430

207.8422TTTTTAACCCTGCGGGATGT56665.23−1.000.713−0.9820.069

431

255.8423TTTTAACCCTGCGGGATGTG56764.80−1.000.651−0.9820.030

432

356.8424TTTAACCCTGCGGGATGTGG56866.83−1.000.949−0.9820.215

433

497.8425TTAACCCTGCGGGATGTGGT56969.50−1.001.339−0.9820.457

434

754.3426TAACCCTGCGGGATGTGGTA57068.63−1.001.212−0.9820.378

435

902.4427AACCCTGCGGGATGTGGTAT57169.14−1.001.286−0.9820.424

436

1186.6428ACCCTGCGGGATGTGGTATT57271.66−1.001.657−0.9820.654

437

1514.9429CCCTGCGGGATGTGGTATTC57372.66−0.601.804−0.6340.878

438

2407.6430CCTGCGGGATGTGGTATTCC57472.66−0.601.804−0.6340.878

439

3019.4431CTGCGGGATGTGGTATTCCT57571.02−1.301.563−1.2430.497

440

3275.3432TGCGGGATGTGGTATTCCTA57668.54−1.301.199−1.2430.271

441

2830.8433GCGGGATGTGGTATTCCTAA57766.48−1.300.896−1.2430.083

442

2620.5434CGGGATGTGGTATTCCTAAT57862.46−1.300.307−1.243−0.282

443

1827.8435GGGATGTGGTATTCCTAATT57962.37−1.300.294−1.243−0.290

444

1957.4436GGATGTGGTATTCCTAATTG58059.71−0.90−0.097−0.895−0.400

445

1686.2437GATGTGGTATTCCTAATTGA58158.45−0.20−0.281−0.286−0.283

446

1395.0438ATGTGGTATTCCTAATTGAA58255.24−0.20−0.752−0.286−0.575

447

1245.7439TGTGGTATTCCTAATTGAAC58355.76−0.30−0.675−0.373−0.561

448

1314.0440GTGGTATTCCTAATTGAACT58457.73−0.30−0.387−0.373−0.382

449

1818.7441TGGTATTCCTAATTGAACTT58555.15−0.30−0.765−0.373−0.616

450

880.3442GGTATTCCTAATTGAACTTC58656.47−0.30−0.572−0.373−0.496

451

1419.0443GTATTCCTAATTGAACTTCC58757.76−0.30−0.383−0.373−0.379

452

1567.9444TATTCCTAATTGAACTTCCC58858.57−0.30−0.264−0.373−0.306

453

1959.4445ATTCCTAATTGAACTTCCCA58960.26−0.30−0.016−0.373−0.152

454

2971.8446TTCCTAATTGAACTTCCCAG59060.45−0.100.013−0.199−0.068

455

1898.5447TCCTAATTGAACTTCCCAGA59161.360.700.1460.4960.279

456

1392.3448CCTAATTGAACTTCCCAGAA59258.270.70−0.3080.496−0.002

457

1143.2449CTAATTGAACTTCCCAGAAG59354.92−0.70−0.800−0.721−0.770

458

427.7450TAATTGAACTTCCCAGAAGT59455.84−1.90−0.664−1.764−1.082

459

148.5451AATTGAACTTCCCAGAAGTC59557.61−2.10−0.404−1.938−0.987

460

259.1452ATTGAACTTCCCAGAAGTCT59661.42−2.100.154−1.938−0.641−0.751241.9453TTGAACTTCCCAGAAGTCTT59761.76−2.100.205−1.938−0.609−0.730808.1454TGAACTTCCCAGAAGTCTTG59861.34−2.100.143−1.938−0.648

461

351.6455GAACTTCCCAGAAGTCTTGA59962.71−2.100.344−1.938−0.523

462

499.7456AACTTCCCAGAAGTCTTGAG60061.63−2.100.186−1.938−0.621

463

407.4457ACTTCCCAGAAGTCTTGAGT60166.97−1.900.969−1.764−0.069

464

492.1458CTTCCCAGAAGTCTTGAGTT60266.75−1.000.937−0.9820.208

465

736.1459TTCCCAGAAGTCTTGAGTTC60366.31−0.200.872−0.2860.432

466

815.2460TCCCAGAAGTCTTGAGTTCT60467.98−1.201.116−1.1560.253

467

888.8461CCCAGAAGTCTTGAGTTCTC60567.98−1.401.116−1.3300.187

468

2021.6462CCAGAAGTCTTGAGTTCTCT60666.10−1.400.842−1.3300.017

469

1988.5463CAGAAGTCTTGAGTTCTCTT60762.41−1.400.300−1.330−0.319

470

2008.8464AGAAGTCTTGAGTTCTCTTA60860.43−1.200.009−1.156−0.434

471

2631.8465GAAGTCTTGAGTTCTCTTAT60960.20−0.50−0.025−0.547−0.223

472

3052.8466AAGTCTTGAGTTCTCTTATT61059.120.30−0.1830.149−0.057

473

3509.3467AGTCTTGAGTTCTCTTATTA61160.750.300.0560.1490.091

474

3221.6468GTCTTGAGTTCTCTTATTAA61258.290.30−0.3050.149−0.132

475

3677.1469TCTTGAGTTCTCTTATTAAG61355.250.30−0.7510.149−0.409

476

1176.6470CTTGAGTTCTCTTATTAAGT61457.040.10−0.488−0.025−0.312

477

1168.1471TTGAGTTCTCTTATTAAGTT61555.290.10−0.745−0.025−0.471

478

666.3472TGAGTTCTCTTATTAAGTTC61656.350.10−0.589−0.025−0.375

479

674.0473GAGTTCTCTTATTAAGTTCT61758.570.10−0.263−0.025−0.173

480

1471.4474AGTTCTCTTATTAAGTTCTC61858.610.10−0.257−0.025−0.169

481

1493.5475GTTCTCTTATTAAGTTCTCT61960.590.100.032−0.0250.011

482

2191.5476TTCTCTTATTAAGTTCTCTG62057.160.10−0.471−0.025−0.301

483

1410.3477TCTCTTATTAAGTTCTCTGA62158.230.10−0.314−0.025−0.204

484

1262.8478CTCTTATTAAGTTCTCTGAA62254.790.10−0.817−0.025−0.516

485

1072.9479TCTTATTAAGTTCTCTGAAA62350.950.10−1.382−0.025−0.866

486

540.9480CTTATTAAGTTCTCTGAAAT62449.770.50−1.5540.323−0.841

487

539.2481TTATTAAGTTCTCTGAAATC62548.990.50−1.6680.323−0.912−0.768709.0482TATTAAGTTCTCTGAAATCT62650.640.50−1.4270.323−0.762−0.775978.1483ATTAAGTTCTCTGAAATCTA62750.640.50−1.4270.323−0.762−0.7321217.7484TTAAGTTCTCTGAAATCTAC62851.150.50−1.3520.323−0.716

488

1748.1485TAAGTTCTCTGAAATCTACT62952.790.50−1.1120.323−0.567

489

2511.5486AAGTTCTCTGAAATCTACTA63052.790.50−1.1120.323−0.567

490

2997.2487AGTTCTCTGAAATCTACTAA63152.790.50−1.1120.323−0.567

491

2887.6488GTTCTCTGAAATCTACTAAT63252.650.50−1.1330.323−0.580

492

4421.3489TTCTCTGAAATCTACTAATT63350.140.70−1.5000.496−0.741−0.8321937.7490TCTCTGAAATCTACTAATTT63450.140.20−1.5000.062−0.906−0.9621773.3491CTCTGAAATCTACTAATTTT63549.31−0.30−1.622−0.373−1.147−1.1021491.1492TCTGAAATCTACTAATTTTC63648.55−0.60−1.734−0.634−1.316−1.171376.6493CTGAAATCTACTAATTTTCT63749.31−1.30−1.622−1.243−1.478−1.178371.9494TGAAATCTACTAATTTTCTC63848.55−1.30−1.734−1.243−1.547−1.092415.2495GAAATCTACTAATTTTCTCC63952.45−0.90−1.161−0.895−1.060−0.9381097.9496AAATCTACTAATTTTCTCCA64052.47−0.10−1.158−0.199−0.794−0.7781429.1497AATCTACTAATTTTCTCCAT64154.250.90−0.8970.670−0.301

493

1812.5498ATCTACTAATTTTCTCCATT64256.461.00−0.5720.757−0.067

494

1943.4499TCTACTAATTTTCTCCATTT64356.800.50−0.5230.323−0.202

495

1506.1500CTACTAATTTTCTCCATTTA64454.930.50−0.7970.323−0.372

496

1694.7501TACTAATTTTCTCCATTTAG64553.140.30−1.0600.149−0.600

497

946.7502ACTAATTTTCTCCATTTAGT64656.69−0.70−0.539−0.721−0.608

498

1114.3503CTAATTTTCTCCATTTAGTA64755.570.00−0.704−0.112−0.479

499

963.9504TAATTTTCTCCATTTAGTAC64854.120.50−0.9170.323−0.446

500

1347.9505AATTTTCTCCATTTAGTACT64956.690.70−0.5390.496−0.145

501

2067.7506ATTTTCTCCATTTAGTACTG65058.660.80−0.2500.5830.067

502

2724.2507TTTTCTCCATTTAGTACTGT65161.920.600.2280.4100.297

503

3367.9508TTTCTCCATTTAGTACTGTC65263.100.600.4010.4100.404

504

5235.8509TTCTCCATTTAGTACTGTCT65364.840.600.6560.4100.562

505

6423.5510TCTCCATTTAGTACTGTCTT65464.840.600.6560.4100.562

506

7758.9511CTCCATTTAGTACTGTCTTT65563.630.600.4790.4100.453

507

8001.5512TCCATTTAGTACTGTCTTTT65661.920.600.2280.4100.297

508

5512.4513CCATTTAGTACTGTCTTTTT65760.780.600.0610.4100.194

509

5300.0514CATTTAGTACTGTCTTTTTT65857.040.80−0.4890.583−0.081

510

3902.1515ATTTAGTACTGTCTTTTTTC65957.080.80−0.4820.583−0.077

511

4641.8516TTTAGTACTGTCTTTTTTCT66059.260.80−0.1620.5830.121

512

4888.4517TTAGTACTGTCTTTTTTCTT66159.260.80−0.1620.5830.121

513

5477.3518TAGTACTGTCTTTTTTCTTT66259.260.80−0.1620.5830.121

514

5064.9519AGTACTGTCTTTTTTCTTTA66359.261.00−0.1620.7570.187

515

5580.3520GTACTGTCTTTTTTCTTTAT66459.042.70−0.1952.2360.729

516

5478.3521TACTGTCTTTTTTCTTTATG66555.712.90−0.6832.4100.492

517

2275.5522ACTGTCTTTTTTCTTTATGG66659.071.70−0.1901.3660.402

518

1730.8523CTGTCTTTTTTCTTTATGGC66762.921.700.3741.3660.751

519

2405.5524TGTCTTTTTTCTTTATGGCA66862.141.700.2601.3660.680

520

1942.0525GTCTTTTTTCTTTATGGCAA66960.051.50−0.0471.1920.424

521

2085.6526TCTTTTTTCTTTATGGCAAA67054.990.60−0.7880.410−0.333

522

493.2527CTTTTTTCTTTATGGCAAAT67153.750.10−0.971−0.025−0.612

523

532.7528TTTTTTCTTTATGGCAAATA67251.300.10−1.331−0.025−0.835

524

280.0529TTTTTCTTTATGGCAAATAC67351.490.10−1.302−0.025−0.817

525

440.8530TTTTCTTTATGGCAAATACT67453.080.10−1.069−0.025−0.672

526

463.1531TTTCTTTATGGCAAATACTG67552.740.10−1.119−0.025−0.704

527

579.0532TTCTTTATGGCAAATACTGG67654.900.10−0.802−0.025−0.507

528

673.7533TCTTTATGGCAAATACTGGA67755.850.10−0.663−0.025−0.421

529

837.0534CTTTATGGCAAATACTGGAG67854.780.10−0.820−0.025−0.518

530

1061.9535TTTATGGCAAATACTGGAGT67955.740.30−0.6790.149−0.365

531

855.0536TTATGGCAAATACTGGAGTA68054.870.60−0.8060.410−0.344

532

775.0537TATGGCAAATACTGGAGTAT68154.560.00−0.852−0.112−0.571

533

773.6538ATGGCAAATACTGGAGTATT68255.42−1.00−0.726−0.982−0.823

534

702.5539TGGCAAATACTGGAGTATTG68355.37−1.20−0.733−1.156−0.893−0.775387.5540GGCAAATACTGGAGTATTGT68458.33−1.20−0.298−1.156−0.624−0.924435.3541GCAAATACTGGAGTATTGTA68555.24−1.20−0.753−1.156−0.906−0.97493.7542CAAATACTGGAGTATTGTAT68651.30−1.20−1.331−1.156−1.264−0.91350.0543AAATACTGGAGTATTGTATG68749.96−1.20−1.527−1.156−1.386−0.80950.4544AATACTGGAGTATTGTATGG68854.30−1.00−0.890−0.982−0.925

535

64.7545ATACTGGAGTATTGTATGGA68957.60−0.30−0.406−0.373−0.394

536

76.0546TACTGGAGTATTGTATGGAT69057.600.40−0.4060.236−0.162

537

86.0547ACTGGAGTATTGTATGGATT69158.531.30−0.2691.0180.220

538

123.4548CTGGAGTATTGTATGGATTC69259.392.00−0.1441.6270.529

539

121.5549TGGAGTATTGTATGGATTCT69359.391.80−0.1441.4530.463

540

641.3550GGAGTATTGTATGGATTCTC69460.950.600.0860.4100.209

541

161.5551GAGTATTGTATGGATTCTCA69559.520.60−0.1240.4100.079

542

129.9552AGTATTGTATGGATTCTCAG69658.311.10−0.3020.8440.134

543

88.7553GTATTGTATGGATTCTCAGG69760.871.100.0740.8440.367

544

112.5554TATTGTATGGATTCTCAGGC69861.971.100.2360.8440.467

545

134.6555ATTGTATGGATTCTCAGGCC69966.521.100.9020.8440.880

546

191.6556TTGTATGGATTCTCAGGCCC70070.340.701.4630.4961.096

547

254.5557TGTATGGATTCTCAGGCCCA70171.110.201.5770.0621.001

548

332.2558GTATGGATTCTCAGGCCCAA70268.950.001.259−0.1120.738

549

415.6559TATGGATTCTCAGGCCCAAT70365.780.000.795−0.1120.450

550

285.0560ATGGATTCTCAGGCCCAATT70466.680.000.925−0.1120.531

551

464.0561TGGATTCTCAGGCCCAATTT70567.040.200.9790.0620.630

552

492.5562GGATTCTCAGGCCCAATTTT70667.511.101.0480.8440.970

553

639.7563GATTCTCAGGCCCAATTTTT70765.341.300.7291.0180.839

554

512.4564ATTCTCAGGCCCAATTTTTG70863.940.600.5240.4100.481

555

393.4565TTCTCAGGCCCAATTTTTGA70965.240.200.7160.0620.467

556

334.3566TCTCAGGCCCAATTTTTGAA71062.850.200.3640.0620.249

557

308.2567CTCAGGCCCAATTTTTGAAA71159.620.20−0.1090.062−0.044

558

199.2568TCAGGCCCAATTTTTGAAAT71257.850.20−0.3690.062−0.205

559

164.3569CAGGCCCAATTTTTGAAATT71356.95−0.50−0.501−0.547−0.518

560

125.6570AGGCCCAATTTTTGAAATTT71456.09−1.00−0.627−0.982−0.762

561

102.6571GGCCCAATTTTTGAAATTTT71556.23−1.00−0.606−0.982−0.749

562

91.6572GCCCAATTTTTGAAATTTTC71655.07−1.00−0.777−0.982−0.855−0.80676.2573CCCAATTTTTGAAATTTTCC71754.96−1.00−0.792−0.982−0.864−0.88178.8574CCAATTTTTGAAATTTTCCC71854.96−1.00−0.792−0.982−0.864−0.84184.8575CAATTTTTGAAATTTTCCCT71953.17−1.00−1.055−0.982−1.027−0.755162.0576AATTTTTGAAATTTTCCCTT72052.25−0.80−1.190−0.808−1.045

563

539.5577ATTTTTGAAATTTTCCCTTC72155.170.10−0.762−0.025−0.482

564

1787.3578TTTTTGAAATTTTCCCTTCC72258.880.10−0.219−0.025−0.145

565

6354.2579TTTTGAAATTTTCCCTTCCT72360.390.100.004−0.025−0.007

566

9513.6580TTTGAAATTTTCCCTTCCTT72460.390.100.004−0.025−0.007

567

10660.0581TTGAAATTTTCCCTTCCTTT72560.390.100.004−0.025−0.007

568

11202.0582TGAAATTTTCCCTTCCTTTT72660.390.100.004−0.025−0.007

569

11543.0583GAAATTTTCCCTTCCTTTTC72761.810.400.2120.2360.221

570

14774.0584AAATTTTCCCTTCCTTTTCC72864.171.200.5570.9310.6990.95218197.0585AATTTTCCCTTCCTTTTCCA72967.391.701.0301.3661.1581.30721410.0586ATTTTCCCTTCCTTTTCCAT73069.584.001.3513.3662.1171.67922869.0587TTTTCCCTTCCTTTTCCATT73169.965.001.4084.2362.4822.03921818.0588TTTCCCTTCCTTTTCCATTT73269.965.001.4084.2362.4822.11321341.0589TTCCCTTCCTTTTCCATTTC73371.195.001.5884.2362.5942.08522063.0590TCCCTTCCTTTTCCATTTCT73472.775.001.8204.2362.7381.86322152.0591CCCTTCCTTTTCCATTTCTG73571.010.901.5610.6701.2231.57120764.0592CCTTCCTTTTCCATTTCTGT73670.680.201.5130.0620.9611.28912579.0593CTTCCTTTTCCATTTCTGTA73766.300.200.8700.0620.5630.9459036.3594TTCCTTTTCCATTTCTGTAC73864.870.200.6600.0620.433

571

8251.8595TCCTTTTCCATTTCTGTACA73965.740.200.7880.0620.512

572

20788.0596CCTTTTCCATTTCTGTACAA74062.110.200.2560.0620.182

573

7073.9597CTTTTCCATTTCTGTACAAA74156.390.20−0.5830.062−0.338

574

2932.4598TTTTCCATTTCTGTACAAAT74254.490.20−0.8620.062−0.511

575

1897.3599TTTCCATTTCTGTACAAATT74354.49−0.30−0.862−0.373−0.676

576

2158.1600TTCCATTTCTGTACAAATTT74454.49−0.30−0.862−0.373−0.676

577

2215.9601TCCATTTCTGTACAAATTTC74555.43−0.30−0.724−0.373−0.591

578

2168.6602CCATTTCTGTACAAATTTCT74656.07−0.30−0.631−0.373−0.533

579

2025.8603CATTTCTGTACAAATTTCTA74751.65−0.30−1.278−0.373−0.934

580

1277.2604ATTTCTGTACAAATTTCTAC74850.83−0.10−1.398−0.199−0.943−0.7361944.8605TTTCTGTACAAATTTCTACT74952.780.40−1.1120.236−0.600−0.7902504.3606TTCTGTACAAATTTCTACTA75051.900.40−1.2420.236−0.681−0.8762941.5607TCTGTACAAATTTCTACTAA75149.840.40−1.5440.236−0.868−0.8462694.8608CTGTACAAATTTCTACTAAT75248.730.40−1.7070.236−0.969−0.8272610.7609TGTACAAATTTCTACTAATG75346.880.40−1.9790.236−1.137−0.8451678.1610GTACAAATTTCTACTAATGC75450.660.60−1.4240.410−0.727−0.8545877.3611TACAAATTTCTACTAATGCT75549.820.60−1.5470.410−0.803−0.8494461.0612ACAAATTTCTACTAATGCTT75650.650.60−1.4250.410−0.728−0.8165943.2613CAAATTTCTACTAATGCTTT75750.460.60−1.4530.410−0.745−0.7536492.9614AAATTTCTACTAATGCTTTT75849.470.60−1.5990.410−0.836−0.7456875.0615AATTTCTACTAATGCTTTTA75950.610.60−1.4310.410−0.731

581

7950.3616ATTTCTACTAATGCTTTTAT76052.400.20−1.1690.062−0.701

582

8314.8617TTTCTACTAATGCTTTTATT76152.720.20−1.1220.062−0.672

583

6885.8618TTCTACTAATGCTTTTATTT76252.720.20−1.1220.062−0.672

584

6443.2619TCTACTAATGCTTTTATTTT76352.720.20−1.1220.062−0.672−0.7316331.0620CTACTAATGCTTTTATTTTT76451.810.20−1.2550.062−0.755

585

5952.5621TACTAATGCTTTTATTTTTT76550.180.20−1.4940.062−0.903

586

2662.8622ACTAATGCTTTTATTTTTTC76651.960.20−1.2330.062−0.741

587

3034.0623CTAATGCTTTTATTTTTTCT76753.410.20−1.0210.062−0.609

588

2198.5624TAATGCTTTTATTTTTTCTT76851.760.40−1.2630.236−0.694

589

1670.1625AATGCTTTTATTTTTTCTTC76953.611.10−0.9920.844−0.294

590

3039.4626ATGCTTTTATTTTTTCTTCT77057.662.10−0.3971.7140.405

591

3873.8627TGCTTTTATTTTTTCTTCTG77157.602.80−0.4062.3230.631

592

3609.7628GCTTTTATTTTTTCTTCTGT77260.963.100.0872.5831.036

593

4891.4629CTTTTATTTTTTCTTCTGTC77357.963.10−0.3532.5830.763

594

3071.6630TTTTATTTTTTCTTCTGTCA77457.223.10−0.4612.5830.696

595

2667.2631TTTATTTTTTCTTCTGTCAA77554.811.70−0.8161.3660.013

596

2293.1632TTATTTTTTCTTCTGTCAAT77654.461.20−0.8660.931−0.183

597

2123.0633TATTTTTTCTTCTGTCAATG77754.081.20−0.9220.931−0.218

598

1914.7634ATTTTTTCTTCTGTCAATGG77857.361.20−0.4420.9310.080

599

2174.1635TTTTTTCTTCTGTCAATGGC77961.671.200.1920.9310.473

600

3659.7636TTTTTCTTCTGTCAATGGCC78065.261.200.7170.9310.799

601

5217.7637TTTTCTTCTGTCAATGGCCA78166.111.200.8430.9310.877

602

4559.7638TTTCTTCTGTCAATGGCCAT78265.731.000.7870.7570.776

603

4347.7639TTCTTCTGTCAATGGCCATT78365.731.000.7870.7570.776

604

5267.4640TCTTCTGTCAATGGCCATTG78465.26−0.600.718−0.6340.204

605

3922.8641CTTCTGTCAATGGCCATTGT78566.97−1.300.968−1.2430.128

606

3608.6642TTCTGTCAATGGCCATTGTT78665.36−1.300.733−1.243−0.018

607

1881.6643TCTGTCAATGGCCATTGTTT78765.36−1.300.733−1.243−0.018

608

1658.0644CTGTCAATGGCCATTGTTTA78863.32−1.300.433−1.243−0.204

609

1369.8645TGTCAATGGCCATTGTTTAA78959.38−1.30−0.144−1.243−0.562

610

605.8646GTCAATGGCCATTGTTTAAC79059.99−1.30−0.055−1.243−0.506

611

933.2647TCAATGGCCATTGTTTAACT79158.93−1.30−0.211−1.243−0.603

612

441.8648CAATGGCCATTGTTTAACTT79257.97−0.90−0.352−0.895−0.558

613

545.6649AATGGCCATTGTTTAACTTT79357.070.90−0.4830.670−0.045

614

781.4650ATGGCCATTGTTTAACTTTT79459.310.90−0.1560.6700.158

615

1027.3651TGGCCATTGTTTAACTTTTG79559.240.90−0.1650.6700.152

616

1102.5652GGCCATTGTTTAACTTTTGG79661.840.300.2160.1490.190

617

935.7653GCCATTGTTTAACTTTTGGG79761.84−0.100.216−0.1990.058

618

403.7654CCATTGTTTAACTTTTGGGC79861.840.300.2160.1490.190

619

269.3655CATTGTTTAACTTTTGGGCC79961.840.900.2160.6700.389

620

296.8656ATTGTTTAACTTTTGGGCCA80061.840.900.2160.6700.389

621

449.4657TTGTTTAACTTTTGGGCCAT80161.840.900.2160.6700.389

622

448.1658TGTTTAACTTTTGGGCCATC80262.910.900.3730.6700.486

623

584.9659GTTTAACTTTTGGGCCATCC80366.730.400.9340.2360.669

624

1032.4660TTTAACTTTTGGGCCATCCA80464.79−0.700.649−0.7210.128

625

737.8661TTAACTTTTGGGCCATCCAT80564.44−1.200.598−1.156−0.069

626

950.2662TAACTTTTGGGCCATCCATT80664.44−1.200.598−1.156−0.069

627

1308.0663AACTTTTGGGCCATCCATTC80766.42−1.200.888−1.1560.111

628

2360.1664ACTTTTGGGCCATCCATTCC80872.21−1.201.738−1.1560.638

629

4946.0665CTTTTGGGCCATCCATTCCT80973.53−1.201.930−1.1560.758

630

6789.2666TTTTGGGCCATCCATTCCTG81071.49−1.201.632−1.1560.573

631

8150.6667TTTGGGCCATCCATTCCTGG81173.62−1.201.945−1.1560.766

632

7589.0668TTGGGCCATCCATTCCTGGC81277.43−2.802.504−2.5470.584

633

13914.0669TGGGCCATCCATTCCTGGCT81378.94−3.502.725−3.1560.490

634

17513.0670GGGCCATCCATTCCTGGCTT81479.51−3.502.809−3.1560.542

635

19883.0671GGCCATCCATTCCTGGCTTT81577.37−3.502.494−3.1560.347

636

20103.0672GCCATCCATTCCTGGCTTTA81674.28−3.102.040−2.8080.198

637

18622.0673CCATCCATTCCTGGCTTTAA81767.92−1.301.109−1.2430.215

638

16915.0674CATCCATTCCTGGCTTTAAT81864.36−1.300.585−1.243−0.109

639

13910.0675ATCCATTCCTGGCTTTAATT81963.53−1.300.464−1.243−0.185

640

12524.0676TCCATTCCTGGCTTTAATTT82063.88−1.300.516−1.243−0.152

641

11890.0677CCATTCCTGGCTTTAATTTT82162.81−0.900.359−0.895−0.118

642

12839.0678CATTCCTGGCTTTAATTTTA82258.550.90−0.2660.6700.090

643

9726.8679ATTCCTGGCTTTAATTTTAC82357.841.50−0.3711.1920.223

644

8499.7680TTCCTGGCTTTAATTTTACT82459.781.90−0.0861.5400.532

645

6800.4681TCCTGGCTTTAATTTTACTG82559.371.90−0.1461.5400.494

646

5445.6682CCTGGCTTTAATTTTACTGG82660.531.900.0241.5400.600

647

2901.6683CTGGCTTTAATTTTACTGGT82759.771.90−0.0871.5400.531

648

1174.2684TGGCTTTAATTTTACTGGTA82857.251.90−0.4581.5400.301

649

521.3685GGCTTTAATTTTACTGGTAC82957.861.90−0.3681.5400.357

650

611.1686GCTTTAATTTTACTGGTACA83056.551.80−0.5601.4530.205

651

287.6687CTTTAATTTTACTGGTACAG83152.660.40−1.1300.236−0.611

652

109.5688TTTAATTTTACTGGTACAGT83253.62−0.80−0.989−0.808−0.920

653

59.5689TTAATTTTACTGGTACAGTC83354.59−1.00−0.847−0.982−0.898

654

62.1690TAATTTTACTGGTACAGTCT83456.28−1.00−0.599−0.982−0.745

655

59.4691AATTTTACTGGTACAGTCTC83558.27−1.00−0.308−0.982−0.564

656

68.0692ATTTTACTGGTACAGTCTCA83661.78−1.000.207−0.982−0.245

657

72.9693TTTTACTGGTACAGTCTCAA83759.61−1.00−0.111−0.982−0.442

658

62.2694TTTACTGGTACAGTCTCAAT83859.25−1.00−0.164−0.982−0.475

659

64.5695TTACTGGTACAGTCTCAATA83958.30−1.00−0.303−0.982−0.561

660

53.5696TACTGGTACAGTCTCAATAG84058.15−1.00−0.326−0.982−0.575

661

57.8697ACTGGTACAGTCTCAATAGG84161.44−0.800.157−0.808−0.210

662

341.0698CTGGTACAGTCTCAATAGGG84263.550.100.467−0.0250.280

663

54.8699TGGTACAGTCTCAATAGGGC84365.891.100.8100.8440.823

664

47.1700GGTACAGTCTCAATAGGGCT84468.080.901.1310.6700.956

665

59.7701GTACAGTCTCAATAGGGCTA84564.730.700.6400.4960.586

666

47.0702TACAGTCTCAATAGGGCTAA84659.350.70−0.1490.4960.096

667

49.3703ACAGTCTCAATAGGGCTAAT84759.910.70−0.0670.4960.147

668

55.0704CAGTCTCAATAGGGCTAATG84859.290.70−0.1580.4960.091

669

49.0705AGTCTCAATAGGGCTAATGG84960.620.900.0370.6700.278

670

45.7706GTCTCAATAGGGCTAATGGG85063.001.100.3860.8440.560

671

115.6707TCTCAATAGGGCTAATGGGA85161.220.400.1250.2360.167

672

50.6708CTCAATAGGGCTAATGGGAA85257.971.40−0.3521.1050.202

673

48.0709TCAATAGGGCTAATGGGAAA85354.391.40−0.8771.105−0.124

674

50.5710CAATAGGGCTAATGGGAAAA85451.641.80−1.2811.453−0.242

675

44.1711AATAGGGCTAATGGGAAAAT85550.451.90−1.4541.540−0.316

676

43.1712ATAGGGCTAATGGGAAAATT85652.341.00−1.1780.757−0.442

677

45.2713TAGGGCTAATGGGAAAATTT85752.630.50−1.1350.323−0.581

678

47.4714AGGGCTAATGGGAAAATTTA85852.630.50−1.1350.323−0.581

679

50.0715GGGCTAATGGGAAAATTTAA85950.890.50−1.3900.323−0.739−0.86747.8716GGCTAATGGGAAAATTTAAA86047.140.50−1.9400.323−1.080−1.02250.2717GCTAATGGGAAAATTTAAAG86145.000.50−2.2540.323−1.275−1.09643.0718CTAATGGGAAAATTTAAAGT86243.950.50−2.4080.323−1.371−1.08857.0719TAATGGGAAAATTTAAAGTG86342.270.50−2.6550.323−1.524−1.07258.7720AATGGGAAAATTTAAAGTGC86446.180.70−2.0810.496−1.102−1.011183.6721ATGGGAAAATTTAAAGTGCA86548.901.70−1.6821.366−0.524−0.924303.4722TGGGAAAATTTAAAGTGCAA86647.391.80−1.9031.453−0.628−0.837135.7723GGGAAAATTTAAAGTGCAAC86747.841.60−1.8381.279−0.653−0.766241.7724GGAAAATTTAAAGTGCAACC86849.121.20−1.6490.931−0.669−0.737132.5725GAAAATTTAAAGTGCAACCA86948.091.20−1.8010.931−0.763−0.758128.8726AAAATTTAAAGTGCAACCAA87045.571.10−2.1710.844−1.025

680

141.0727AAATTTAAAGTGCAACCAAT87146.971.10−1.9650.844−0.897

681

282.0728AATTTAAAGTGCAACCAATC87249.461.10−1.5990.844−0.671

682

948.6729ATTTAAAGTGCAACCAATCT87352.841.10−1.1040.844−0.363

683

1815.1730TTTAAAGTGCAACCAATCTG87452.811.10−1.1090.844−0.366

684

3188.2731TTAAAGTGCAACCAATCTGA87553.711.00−0.9760.757−0.317

685

3566.1732TAAAGTGCAACCAATCTGAG87653.561.00−0.9990.757−0.331

686

2925.1733AAAGTGCAACCAATCTGAGT87756.811.00−0.5220.757−0.036

687

3233.2734AAGTGCAACCAATCTGAGTC87859.991.00−0.0550.7570.254

688

3615.6735AGTGCAACCAATCTGAGTCA87963.251.000.4220.7570.550

689

3994.8736GTGCAACCAATCTGAGTCAA88061.001.000.0930.7570.345

690

4033.0737TGCAACCAATCTGAGTCAAC88158.621.00−0.2570.7570.128

691

3380.2738GCAACCAATCTGAGTCAACA88259.871.00−0.0730.7570.242

692

4288.7739CAACCAATCTGAGTCAACAG88356.22−0.30−0.608−0.373−0.519

693

744.1740AACCAATCTGAGTCAACAGA88456.24−1.60−0.605−1.504−0.946−0.757392.2741ACCAATCTGAGTCAACAGAT88558.10−2.30−0.332−2.112−1.009−1.030158.1742CCAATCTGAGTCAACAGATT88657.90−3.30−0.362−2.982−1.357−1.21970.8743CAATCTGAGTCAACAGATTT88754.41−3.80−0.874−3.417−1.840−1.262190.0744AATCTGAGTCAACAGATTTC88854.37−3.60−0.880−3.243−1.778−1.16887.7745ATCTGAGTCAACAGATTTCT88958.37−2.60−0.293−2.373−1.084−1.017152.7746TCTGAGTCAACAGATTTCTT89058.73−1.90−0.241−1.764−0.820−0.797270.5747CTGAGTCAACAGATTTCTTC89158.73−0.30−0.241−0.373−0.291

694

498.7748TGAGTCAACAGATTTCTTCC89260.700.200.0490.0620.054

695

891.0749GAGTCAACAGATTTCTTCCA89362.060.200.2480.0620.177

696

1509.8750AGTCAACAGATTTCTTCCAA89458.660.20−0.2500.062−0.132

697

1009.3751GTCAACAGATTTCTTCCAAT89558.470.20−0.2790.062−0.149

698

1198.0752TCAACAGATTTCTTCCAATT89655.860.20−0.6610.062−0.387

699

680.5753CAACAGATTTCTTCCAATTA89754.080.20−0.9220.062−0.548

700

762.5754AACAGATTTCTTCCAATTAT89852.820.20−1.1070.062−0.663

701

689.8755ACAGATTTCTTCCAATTATG89954.580.20−0.8490.062−0.503

702

715.1756CAGATTTCTTCCAATTATGT90056.990.20−0.4960.062−0.284

703

833.8757AGATTTCTTCCAATTATGTT90156.020.20−0.6380.062−0.372

704

1067.7758GATTTCTTCCAATTATGTTG90255.800.30−0.6700.149−0.359

705

1225.9759ATTTCTTCCAATTATGTTGA90355.80−0.10−0.670−0.199−0.491

706

1028.7760TTTCTTCCAATTATGTTGAC90456.34−0.10−0.591−0.199−0.442

707

1419.0761TTCTTCCAATTATGTTGACA90557.29−0.10−0.452−0.199−0.356

708

1437.4762TCTTCCAATTATGTTGACAG90657.14−0.10−0.474−0.199−0.369

709

1518.3763CTTCCAATTATGTTGACAGG90758.36−0.10−0.295−0.199−0.259

710

1560.3764TTCCAATTATGTTGACAGGT90859.43−0.10−0.138−0.199−0.161

711

1100.0765TCCAATTATGTTGACAGGTG90959.02−0.10−0.198−0.199−0.198

712

1096.4766CCAATTATGTTGACAGGTGT91060.68−0.100.046−0.199−0.047

713

1103.4767CAATTATGTTGACAGGTGTA91156.240.30−0.6050.149−0.319

714

738.1768AATTATGTTGACAGGTGTAG91255.091.10−0.7740.844−0.159

715

596.7769ATTATGTTGACAGGTGTAGG91359.831.10−0.0790.8440.272

716

548.1770TTATGTTGACAGGTGTAGGT91463.161.100.4090.8440.575

717

701.1771TATGTTGACAGGTGTAGGTC91564.38−0.200.588−0.2860.256

718

724.7772ATGTTGACAGGTGTAGGTCC91669.08−0.601.278−0.6340.551

719

1129.8773TGTTGACAGGTGTAGGTCCT91771.21−0.601.591−0.6340.745

720

1214.0774GTTGACAGGTGTAGGTCCTA91870.75−0.601.523−0.6340.703

721

1425.4775TTGACAGGTGTAGGTCCTAC91967.83−0.601.095−0.6340.438

722

838.8776TGACAGGTGTAGGTCCTACT92069.52−0.901.343−0.8950.493

723

1173.1777GACAGGTGTAGGTCCTACTA92169.06−0.901.275−0.8950.450

724

1367.0778ACAGGTGTAGGTCCTACTAA92265.30−0.900.723−0.8950.108

725

872.0779CAGGTGTAGGTCCTACTAAT92364.69−0.900.634−0.8950.053

726

897.6780AGGTGTAGGTCCTACTAATA92462.84−0.900.362−0.895−0.115

727

962.2781GGTGTAGGTCCTACTAATAC92563.19−0.900.414−0.895−0.083

728

1382.6782GTGTAGGTCCTACTAATACT92662.53−0.900.317−0.895−0.143

729

1132.9783TGTAGGTCCTACTAATACTG92759.27−0.90−0.160−0.895−0.439

730

1180.7784GTAGGTCCTACTAATACTGT92862.53−0.500.317−0.547−0.011

731

1932.9785TAGGTCCTACTAATACTGTA92958.770.70−0.2340.4960.043

732

1634.4786AGGTCCTACTAATACTGTAC93059.910.50−0.0670.3230.081

733

2488.1787GGTCCTACTAATACTGTACC93163.540.500.4660.3230.411

734

3560.9788GTCCTACTAATACTGTACCT93262.910.500.3730.3230.354

735

3850.1789TCCTACTAATACTGTACCTA93359.310.50−0.1550.3230.026

736

1879.0790CCTACTAATACTGTACCTAT93457.990.50−0.3480.323−0.093

737

1920.4791CTACTAATACTGTACCTATA93553.680.50−0.9810.323−0.486

738

1131.2792TACTAATACTGTACCTATAG93651.920.70−1.2400.496−0.580

739

756.5793ACTAATACTGTACCTATAGC93756.451.20−0.5740.931−0.002

740

1881.3794CTAATACTGTACCTATAGCT93857.851.20−0.3690.9310.125

741

2033.6795TAATACTGTACCTATAGCTT93956.251.20−0.6040.931−0.021

742

1853.9796AATACTGTACCTATAGCTTT94057.141.20−0.4730.9310.060

743

2462.6797ATACTGTACCTATAGCTTTA94158.551.20−0.2660.9310.189

744

2436.8798TACTGTACCTATAGCTTTAT94258.551.20−0.2660.9310.189

745

1865.2799ACTGTACCTATAGCTTTATG94359.061.20−0.1920.9310.235

746

1682.1800CTGTACCTATAGCTTTATGT94461.641.300.1871.0180.503

747

1551.3801TGTACCTATAGCTTTATGTC94561.081.100.1050.8440.386

748

1600.1802GTACCTATAGCTTTATGTCC94665.161.100.7030.8440.757

749

4094.6803TACCTATAGCTTTATGTCCA94763.161.100.4090.8440.575

750

2794.2804ACCTATAGCTTTATGTCCAC94864.301.300.5771.0180.745

751

4754.9805CCTATAGCTTTATGTCCACA94964.941.300.6711.0180.803

752

4185.4806CTATAGCTTTATGTCCACAG95061.341.100.1430.8440.409

753

3284.3807TATAGCTTTATGTCCACAGA95160.701.100.0480.8440.351

754

2819.7808ATAGCTTTATGTCCACAGAT95261.270.600.1320.4100.238

755

3545.1809TAGCTTTATGTCCACAGATT95361.630.600.1860.4100.271

756

4232.6810AGCTTTATGTCCACAGATTT95462.570.600.3240.4100.356

757

5252.8811GCTTTATGTCCACAGATTTC95563.850.600.5110.4100.472

758

6823.9812CTTTATGTCCACAGATTTCT95661.560.600.1760.4100.265

759

4829.8813TTTATGTCCACAGATTTCTA95758.970.60−0.2050.4100.029

760

4333.7814TTATGTCCACAGATTTCTAT95858.620.60−0.2570.410−0.004

761

3801.0815TATGTCCACAGATTTCTATG95958.200.60−0.3180.410−0.041

762

3528.2816ATGTCCACAGATTTCTATGA96060.120.60−0.0360.4100.134

763

2080.0817TGTCCACAGATTTCTATGAG96160.340.60−0.0040.4100.153

764

913.8818GTCCACAGATTTCTATGAGT96263.680.600.4860.4100.457

765

1228.3819TCCACAGATTTCTATGAGTA96359.830.80−0.0780.5830.173

766

238.1820CCACAGATTTCTATGAGTAT96458.431.10−0.2850.8440.144

767

219.4821CACAGATTTCTATGAGTATC96555.780.90−0.6730.670−0.162

768

138.6822ACAGATTTCTATGAGTATCT96656.48−0.10−0.571−0.199−0.430

769

112.7823CAGATTTCTATGAGTATCTG96755.85−1.30−0.663−1.243−0.883

770

133.8824AGATTTCTATGAGTATCTGA96855.87−0.10−0.659−0.199−0.485

771

296.8825GATTTCTATGAGTATCTGAT96955.690.60−0.6860.410−0.270

772

279.7826ATTTCTATGAGTATCTGATC97055.670.80−0.6890.583−0.206

773

484.4827TTTCTATGAGTATCTGATCA97157.060.20−0.4850.062−0.277

774

502.0828TTCTATGAGTATCTGATCAT97256.70−0.50−0.538−0.547−0.541

775

637.3829TCTATGAGTATCTGATCATA97355.75−1.10−0.678−1.069−0.826

776

489.0830CTATGAGTATCTGATCATAC97454.95−1.30−0.794−1.243−0.965

777

808.7831TATGAGTATCTGATCATACT97554.95−1.10−0.794−1.069−0.899−0.738903.2832ATGAGTATCTGATCATACTG97655.49−1.20−0.715−1.156−0.883

778

1709.3833TGAGTATCTGATCATACTGT97758.64−1.20−0.254−1.156−0.597

779

2103.9834GAGTATCTGATCATACTGTC97860.20−1.20−0.025−1.156−0.455

780

3973.4835AGTATCTGATCATACTGTCT97960.88−1.000.076−0.982−0.326

781

6462.3836GTATCTGATCATACTGTCTT98061.03−0.300.097−0.373−0.081

782

9749.0837TATCTGATCATACTGTCTTA98157.160.90−0.4700.670−0.037

783

7817.2838ATCTGATCATACTGTCTTAC98258.340.90−0.2980.6700.070

784

9683.1839TCTGATCATACTGTCTTACT98360.420.900.0080.6700.259

785

8089.0840CTGATCATACTGTCTTACTT98459.320.90−0.1540.6700.159

786

8696.8841TGATCATACTGTCTTACTTT98557.630.90−0.4010.6700.006

787

6880.5842GATCATACTGTCTTACTTTG98657.630.90−0.4010.6700.006

788

7033.7843ATCATACTGTCTTACTTTGA98757.630.90−0.4010.6700.006

789

5406.5844TCATACTGTCTTACTTTGAT98857.630.70−0.4010.496−0.060

790

4239.4845CATACTGTCTTACTTTGATA98955.680.70−0.6880.496−0.238

791

3727.4846ATACTGTCTTACTTTGATAA99052.440.70−1.1630.496−0.533

792

2665.5847TACTGTCTTACTTTCATAAA99150.650.70−1.4260.496−0.696

793

1817.8848ACTGTCTTACTTTGATAAAA99249.49−0.30−1.595−0.373−1.131−0.8091335.9849CTGTCTTACTTTGATAAAAC99349.49−0.50−1.595−0.547−1.197−0.9161526.2850TGTCTTACTTTGATAAAACC99451.45−0.50−1.309−0.547−1.019−0.949822.7851GTCTTACTTTGATAAAACCT99553.32−0.50−1.034−0.547−0.849−0.9661227.4852TCTTACTTTGATAAAACCTC99651.75−0.50−1.264−0.547−0.991−0.946503.0853CTTACTTTGATAAAACCTCC99754.28−0.50−0.894−0.547−0.762−0.9101174.3854TTACTTTGATAAAACCTCCA99853.70−0.50−0.978−0.547−0.814−0.901885.5855TACTTTGATAAAACCTCCAA99951.79−0.50−1.259−0.547−0.988−0.916650.6856ACTTTGATAAAACCTCCAAT100052.29−0.50−1.185−0.547−0.943−0.826615.4857CTTTGATAAAACCTCCAATT100152.11−0.50−1.212−0.547−0.959

794

563.4858TTTGATAAAACCTCCAATTC100251.46−0.30−1.307−0.373−0.952

795

420.9859TTGATAAAACCTCCAATTCC100354.680.60−0.8340.410−0.362

796

536.6860TGATAAAACCTCCAATTCCC100457.790.60−0.3780.410−0.079

797

1417.8861GATAAAACCTCCAATTCCCC100561.151.000.1140.7570.359

798

4351.2862ATAAAACCTCCAATTCCCCC100663.241.900.4211.5400.846

799

7738.7863TAAAACCTCCAATTCCCCCT100764.881.900.6631.5400.996

800

11136.0864AAAACCTCCAATTCCCCCTA100864.881.900.6631.5400.9961.07414811.0865AAACCTCCAATTCCCCCTAT100966.731.900.9331.5401.1641.26115751.0866AACCTCCAATTCCCCCTATC101070.071.801.4241.4531.4351.33019661.0867ACCTCCAATTCCCCCTATCA101173.211.801.8831.4531.7201.33520301.0868CCTCCAATTCCCCCTATCAT101272.641.801.8011.4531.6691.32719376.0869CTCCAATTCCCCCTATCATT101369.661.601.3641.2791.3321.25417642.0870TCCAATTCCCCCTATCATTT101468.211.101.1500.8441.0341.09313751.0871CCAATTCCCCCTATCATTTT101567.121.100.9910.8440.9350.93112669.0872CAATTCCCCCTATCATTTTT101664.021.100.5360.8440.653

801

9255.9873AATTCCCCCTATCATTTTTG101762.800.400.3570.2360.311

802

8929.1874ATTCCCCCTATCATTTTTGG101867.280.001.014−0.1120.586

803

6148.2875TTCCCCCTATCATTTTTGGT101970.460.001.480−0.1120.875

804

5468.0876TCCCCCTATCATTTTTGGTT102070.460.001.480−0.1120.875

805

5803.7877CCCCCTATCATTTTTGGTTT102169.270.001.307−0.1120.768

806

5192.0878CCCCTATCATTTTTGGTTTC102267.180.001.000−0.1120.577

807

3557.4879CCCTATCATTTTTGGTTTCC102367.180.001.000−0.1120.577

808

5274.3880CCTATCATTTTTGGTTTCCA102464.630.000.625−0.1120.345

809

3787.9881CTATCATTTTTGGTTTCCAT102560.77−0.500.059−0.547−0.171

810

2726.8882TATCATTTTTGGTTTCCATC102660.20−0.50−0.025−0.547−0.223

811

3249.9883ATCATTTTTGGTTTCCATCT102762.83−0.500.361−0.5470.016

812

5548.9884TCATTTTTGGTTTCCATCTT102863.21−0.500.416−0.5470.050

813

5290.0885CATTTTTGGTTTCCATCTTC102963.21−0.500.416−0.5470.050

814

7451.0886ATTTTTGGTTTCCATCTTCC103065.88−0.500.809−0.5470.293

815

11578.0887TTTTTGGTTTCCATCTTCCT103167.93−0.501.109−0.5470.480

816

13722.0888TTTTGGTTTCCATCTTCCTG103267.42−0.501.035−0.5470.434

817

15064.0889TTTGGTTTCCATCTTCCTGG103369.71−0.901.370−0.8950.509

818

10869.0890TTGGTTTCCATCTTCCTGGC103473.74−1.301.962−1.2430.744

819

16035.0891TGGTTTCCATCTTCCTGGCA103574.48−1.302.071−1.2430.812

820

16304.0892GGTTTCCATCTTCCTGGCAA103672.21−1.301.737−1.2430.605

821

14885.0893GTTTCCATCTTCCTGGCAAA103767.37−1.301.027−1.2430.165

822

11910.0894TTTCCATCTTCCTGGCAAAC103864.82−1.300.653−1.243−0.067

823

11929.0895TTCCATCTTCCTGGCAAACT103966.34−1.300.877−1.2430.071

824

11517.0896TCCATCTTCCTGGCAAACTC104067.47−1.301.042−1.2430.174

825

11822.0897CCATCTTCCTGGCAAACTCA104167.12−0.900.991−0.8950.274

826

11710.0898CATCTTCCTGGCAAACTCAT104263.550.900.4660.6700.544

827

7635.3899ATCTTCCTGGCAAACTCATT104362.711.000.3430.7570.501

828

8378.2900TCTTCCTGGCAAACTCATTT104463.060.900.3950.6700.500

829

6321.4901CTTCCTGGCAAACTCATTTC104563.060.700.3950.4960.434

830

7659.0902TTCCTGGCAAACTCATTTCT104663.060.700.3950.4960.434

831

11621.0903TCCTGGCAAACTCATTTCTT104763.060.700.3950.4960.434

832

3389.0904CCTGGCAAACTCATTTCTTC104863.060.700.3950.4960.434

833

3870.6905CTGGCAAACTCATTTCTTCT104961.240.700.1270.4960.268

834

1992.7906TGGCAAACTCATTTCTTCTA105058.740.70−0.2390.4960.040

835

698.3907GGCAAACTCATTTCTTCTAA105156.860.70−0.5140.496−0.130

836

718.3908GCAAACTCATTTCTTCTAAT105254.360.70−0.8820.496−0.358

837

372.3909CAAACTCATTTCTTCTAATA105349.930.60−1.5300.410−0.793

838

180.6910AAACTCATTTCTTCTAATAC105449.110.60−1.6510.410−0.868

839

430.0911AACTCATTTCTTCTAATACT105552.790.60−1.1110.410−0.533

840

904.3912ACTCATTTCTTCTAATACTG105654.630.60−0.8420.410−0.366

841

1663.5913CTCATTTCTTCTAATACTGT105757.140.60−0.4740.410−0.138

842

2694.2914TCATTTCTTCTAATACTGTA105854.510.60−0.8590.410−0.377

843

3222.9915CATTTCTTCTAATACTGTAT105953.210.60−1.0490.410−0.495

844

3142.8916ATTTCTTCTAATACTGTATC106053.130.80−1.0610.583−0.436

845

5867.0917TTTCTTCTAATACTGTATCA106154.511.20−0.8590.931−0.179

846

6641.4918TTCTTCTAATACTGTATCAT106254.171.30−0.9081.018−0.176

847

7151.9919TCTTCTAATACTGTATCATC106355.171.30−0.7621.018−0.086

848

8134.9920CTTCTAATACTGTATCATCT106455.861.30−0.6611.018−0.023

849

8551.4921TTCTAATACTGTATCATCTG106553.801.30−0.9641.018−0.211

850

5741.7922TCTAATACTGTATCATCTGC106657.651.30−0.3981.0180.140

851

8575.9923CTAATACTGTATCATCTGCT106758.281.30−0.3071.0180.197

852

8980.3924TAATACTGTATCATCTGCTC106857.651.30−0.3981.0180.140

853

10762.0925AATACTGTATCATCTGCTCC106962.191.300.2681.0180.553

854

17037.0926ATACTGTATCATCTGCTCCT107066.431.300.8891.0180.938

855

20970.0927TACTGTATCATCTGCTCCTG107166.321.300.8741.0180.929

856

23084.0928ACTGTATCATCTGCTCCTGT107270.360.601.4660.4101.0650.87524474.0929CTGTATCATCTGCTCCTGTA107369.130.601.2860.4100.9530.91022217.0930TGTATCATCTGCTCCTGTAT107467.040.600.9790.4100.7630.89019829.0931GTATCATCTGCTCCTGTATC107568.850.601.2440.4100.9270.84223548.0932TATCATCTGCTCCTGTATCT107667.440.601.0370.4100.799

857

21759.0933ATCATCTGCTCCTGTATCTA107767.440.601.0370.4100.799

858

22711.0934TCATCTGCTCCTGTATCTAA107865.130.600.6990.4100.589

859

18134.0935CATCTGCTCCTGTATCTAAT107963.601.000.4750.7570.582

860

17772.0936ATCTGCTCCTGTATCTAATA108061.771.600.2071.2790.614

861

17134.0937TCTGCTCCTGTATCTAATAG108162.011.600.2411.2790.635

862

10969.0938CTGCTCCTGTATCTAATAGA108261.900.500.2250.3230.262

863

9556.3939TGCTCCTGTATCTAATAGAG108360.120.30−0.0360.1490.034

864

3739.9940GCTCCTGTATCTAATAGAGC108464.50−1.000.607−0.9820.003

865

4088.3941CTCCTGTATCTAATAGAGCT108562.210.300.2710.1490.224

866

2263.0942TCCTGTATCTAATAGAGCTT108660.560.300.0280.1490.074

867

1018.0943CCTGTATCTAATAGAGCTTC108760.560.300.0280.1490.074

868

1319.1944CTGTATCTAATAGAGCTTCC108860.560.300.0280.1490.074

869

2347.8945TGTATCTAATAGAGCTTCCT108960.560.300.0280.1490.074

870

1871.6946GTATCTAATAGAGCTTCCTT109061.000.300.0920.1490.114

871

3469.1947TATCTAATAGAGCTTCCTTT109158.200.30−0.3180.149−0.141

872

1114.6948ATCTAATAGAGCTTCCTTTA109258.200.30−0.3180.149−0.141

873

1358.4949TCTAATAGAGCTTCCTTTAG109358.390.30−0.2890.149−0.123

874

665.4950CTAATAGAGCTTCCTTTAGT109460.120.00−0.036−0.112−0.065

875

807.4951TAATAGAGCTTCCTTTAGTT109558.460.30−0.2800.149−0.117

876

608.7952AATAGAGCTTCCTTTAGTTG109658.970.30−0.2050.149−0.070

877

623.8953ATAGAGCTTCCTTTAGTTGC109765.530.300.7580.1490.526

878

674.5954TAGAGCTTCCTTTAGTTGCC109869.500.301.3400.1490.8870.841814.3955AGAGCTTCCTTTAGTTGCCC109973.890.301.9830.1491.2861.1571183.8956GAGCTTCCTTTAGTTGCCCC110077.200.302.4700.1491.5881.4542219.4957AGCTTCCTTTAGTTGCCCCC110179.380.302.7890.1491.7851.6504642.2958GCTTCCTTTAGTTGCCCCCC110282.410.403.2340.2362.0951.7658804.8959CTTCCTTTAGTTGCCCCCCT110380.060.802.8890.5832.0131.82311331.0960TTCCTTTAGTTGCCCCCCTA110477.671.102.5390.8441.8951.81812976.0961TCCTTTAGTTGCCCCCCTAT110577.270.602.4800.4101.6931.76512369.0962CCTTTAGTTGCCCCCCTATC110677.270.602.4800.4101.6931.66915090.0963CTTTAGTTGCCCCCCTATCT110775.740.602.2550.4101.5541.58116130.0964TTTAGTTGCCCCCCTATCTT110874.230.602.0330.4101.4161.54515304.0965TTAGTTGCCCCCCTATCTTT110974.230.602.0330.4101.4161.53914829.0966TAGTTGCCCCCCTATCTTTA111073.310.801.8990.5831.3991.49015309.0967AGTTGCCCCCCTATCTTTAT111173.831.401.9761.1051.6451.49815205.0968GTTGCCCCCCTATCTTTATT111273.911.401.9861.1051.6521.52414192.0969TTGCCCCCCTATCTTTATTG111370.591.401.5001.1051.3501.5158699.5970TGCCCCCCTATCTTTATTGT111473.391.401.9111.1051.6051.4617786.6971GCCCCCCTATCTTTATTGTG111573.391.401.9111.1051.6051.3286709.1972CCCCCCTATCTTTATTGTGA111670.611.401.5021.1051.3511.1656198.4973CCCCCTATCTTTATTGTGAC111767.661.201.0700.9311.0170.9994910.2974CCCCTATCTTTATTGTGACG111864.371.200.5870.9310.718

879

850.0975CCCTATCTTTATTGTGACGA111962.051.200.2480.9310.507

880

404.9976CCTATCTTTATTGTGACGAG112058.561.20−0.2650.9310.190

881

166.6977CTATCTTTATTGTGACGAGG112157.281.20−0.4520.9310.073

882

126.9978TATCTTTATTGTGACGAGGG112257.911.20−0.3610.9310.130

883

92.6979ATCTTTATTGTGACGAGGGG112361.031.200.0970.9310.414

884

97.9980TCTTTATTGTGACGAGGGGT112464.180.900.5590.6700.601

885

122.3981CTTTATTGTGACGAGGGGTC112564.18−0.800.559−0.8080.039

886

267.0982TTTATTGTGACGAGGGGTCG112662.63−1.200.332−1.156−0.233

887

396.0983TTATTGTGACGAGGGGTCGT112765.37−2.300.734−2.112−0.348

888

446.0984TATTGTGACGAGGGGTCGTT112865.37−2.800.734−2.547−0.513

889

661.9985ATTGTGACGAGGGGTCGTTG112965.82−2.800.800−2.547−0.472

890

864.5986TTGTGACGAGGGGTCGTTGC113070.01−2.801.414−2.547−0.091

891

1465.7987TGTGACGAGGGGTCGTTGCC113173.21−2.801.884−2.5470.200

892

2836.9988GTGACGAGGGGTCGTTGCCA113274.44−2.802.065−2.5470.312

893

3589.7989TGACGAGGGGTCGTTGCCAA113369.05−2.801.274−2.547−0.178

894

2100.4990GACGAGGGGTCGTTGCCAAA113467.10−2.800.988−2.547−0.355

895

1948.7991ACGAGGGGTCGTTGCCAAAG113566.13−2.600.845−2.373−0.378

896

1384.3992CGAGGGGTCGTTGCCAAAGA113666.81−1.400.945−1.3300.081

897

1192.0993GAGGGGTCGTTGCCAAAGAG113766.840.200.9500.0620.612

898

1221.0994AGGGGTCGTTGCCAAAGAGT113868.700.201.2230.0620.782

899

953.2995GGGGTCGTTGCCAAAGAGTG113968.320.201.1670.0620.747

900

988.6996GGGTCGTTGCCAAAGAGTGA114067.110.200.9890.0620.636

901

937.8997GGTCGTTGCCAAAGAGTGAT114164.590.500.6200.3230.507

902

852.1998GTCGTTGCCAAAGAGTGATC114263.510.000.461−0.1120.243

903

1189.4999TCGTTGCCAAAGAGTGATCT114362.35−1.000.291−0.982−0.192

904

1501.71000CGTTGCCAAAGAGTGATCTG114460.92−1.200.081−1.156−0.389

905

1360.91001GTTGCCAAAGAGTGATCTGA114561.71−1.200.198−1.156−0.317

906

1112.91002TTGCCAAAGAGTGATCTGAG114658.90−1.20−0.215−1.156−0.572

907

468.31003TGCCAAAGAGTGATCTGAGG114761.08−1.200.104−1.156−0.375

908

400.11004GCCAAAGAGTGATCTGAGGG114863.68−1.500.485−1.417−0.237

909

401.61005CCAAAGAGTGATCTGAGGGA114960.94−1.200.084−1.156−0.387

910

199.91006CAAAGAGTGATCTGAGGGAA115055.32−1.20−0.741−1.156−0.899

911

202.11007AAAGAGTGATCTGAGGGAAG115154.21−1.20−0.903−1.156−0.999

912

258.71008AAGAGTGATCTGAGGGAAGT115259.12−1.20−0.183−1.156−0.552

913

274.71009AGAGTGATCTGAGGGAAGTT115361.60−1.000.181−0.982−0.261

914

297.21010GAGTGATCTGAGGGAAGTTA115460.78−0.300.061−0.373−0.104

915

250.61011AGTGATCTGAGGGAAGTTAA115557.350.60−0.4430.410−0.119

916

231.31012GTGATCTGAGGGAAGTTAAA115655.250.60−0.7510.410−0.310

917

214.51013TGATCTGAGGGAAGTTAAAG115752.550.60−1.1470.410−0.556

918

102.31014GATCTGAGGGAAGTTAAAGG115855.090.60−0.7740.410−0.324

919

102.31015ATCTGAGGGAAGTTAAAGGA115955.090.60−0.7740.410−0.324

920

49.41016TCTGAGGGAAGTTAAAGGAT116055.090.60−0.7740.410−0.324

921

104.31017CTGAGGGAAGTTAAAGGATA116153.321.00−1.0340.757−0.353

922

46.31018TGAGGGAAGTTAAAGGATAC116251.951.30−1.2351.018−0.378

923

50.91019GAGGGAAGTTAAAGGATACA116353.260.90−1.0430.670−0.39258.21020AGGGAAGTTAAAGGATACAG116452.140.90−1.2070.670−0.49450.51021GGGAAGTTAAAGGATACAGT116554.810.90−0.8150.670−0.25153.1

Example 3

[0239] Synopsis: The method of the present invention is particularly useful as a guide to the iterative refinement of probes. One of the specific predictions made for rabbit β-globin in Example 1 is used to provide an example of such a refinement.

[0240] Materials and Methods: The contig spanning positions 5-11 of a portion of the rabbit β-globin gene (Example 1, Table 3) was analyzed, using the experimentally measured data to simulate the results of successive experimental measurements. The iterative refinement was performed using a rule-based algorithm, outlined below. This algorithm is used by way of example only; other algorithms for efficiently finding local maxima are well known to the art and could be employed to perform this task.

[0241] Given experimental data for probes from the 1st quartile, median and 3rd quartile of a contig, as well as a user-set signal threshold for further consideration of a probe,

[0242] 1) If all 3 measurements are below the user-specified signal threshold, discard the prediction.

[0243] 2) If at least one of the measurements is above the user-specified threshold, determine which point yields the maximum signal.

[0244] a) If the maximum point is the 1st quartile probe, then make three new measurements for probes with the same spacing as that used in the preceding iteration, but displaced so that the third probe is identical to the original 1st quartile probe. In other words, repeat the search with the same pattern and spacing, but displace the pattern in the direction of increasing signal found in the first experiment.

[0245] b) If the maximum point is the 3rd quartile probe, then make three new measurements for probes with the same spacing as that used in the preceding iteration, but displaced so that the first probe is identical to the original 3rd quartile probe. In other words, repeat the search with the same pattern and spacing, but displace the pattern in the direction of increasing signal found in the first experiment.

[0246] c) If the maximum point is the median probe, then repeat the experiment, keeping the median point the same, but shrinking the spacing between probes by a factor of 2.

[0247] 3) Continue iteration until a maximum is found, or the user judges the signal level observed to be acceptable. Use the experimental value measured for the probe duplicated in successive iterations to tie together the successive data sets, via a simple normalization procedure, described below. Where appropriate, consider all of the data (i.e. all of the iterations) when deciding how to proceed, or whether the peak hybridization intensity has been found.

[0248] Results: Iterative refinement of the contig spanning positions 5-11 in Table 3 proceeds as follows:

[0249] Iteration 1: Probes are synthesized at positions 6, 8 and 10, yielding the experimental hybridization intensities 180, 220 and 310, respectively.

[0250] Iteration 2: Following rule 2b), probes are synthesized at positions 10, 12 and 14. Note that the redundant measurement at position 10 serves as a bridge between experiments, and allows comparison of the two sets by normalizing the intensities by multiplying the second iteration measurements by the ratio of the intensity observed for the probe at position 10 in the first iteration to the value observed in the second iteration. In the simplest case, the ratio is 1; in any case, the second iteration yields the normalized values 310, 390, 240 for probe positions 10, 12 and 14, respectively.

[0251] Iteration 3: By rule 2c), measurements are performed for probes at positions 11, 12 and 13; after normalization, these yield the normalized hybridization intensities 320, 390 and 410, respectively. Combination of these results with the results from iteration 2, probe position 14, yields the conclusion that the best probe for this intensity peak is the probe that starts at sequence position 13.

[0252] The overall result is that iterative improvement converges in three iterations, and requires the synthesis of seven test probes, one of which is the local optimal probe. In addition, the first and second iterations yield probes that exhibit 75% and 95% of the local maximum hybridization intensities, respectively. In many applications, either of these probes would be considered acceptable.

[0253] The above examples 1 and 2 demonstrate that two different implementations of the method of the present invention are capable of efficiently predicting regions of high hybridization efficiency in a variety of polynucleotide targets. Many of the predictions yield acceptable probe sequences on the first design iteration, and all would yield optimized probe sets after 2-4 rounds of iterative refinement, as demonstrated in Example 3. The performance demonstrated in these examples greatly exceeds the performance of current methods. Finally, the examples demonstrate that the predictions can be performed by a software application that has been implemented and installed on a Pentium®-based computer workstation.

[0254] All publications and patent applications cited in this specification are herein incorporated by reference as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference.

[0255] Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be readily apparent to those of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

	Number	Date	Country
Parent	09021701	Feb 1998	US
Child	09784674	Feb 2001	US

Methods for evaluating oligonucleotide probe sequences

Information

Publication Number

Date Filed

Date Published

Inventors

CPC

US Classifications

International Classifications

Abstract

Description

Claims

Continuations (1)