Patent Application
20110280466
This invention relates to genetic imaging, and more particularly to systems and methods for making genetic images, starting with raw biological sequence data.
Advances in sequencing technology have contributed to a rapid accumulation of a vast amount of genetic information from genomes and their transcribed molecules (RNAs) of a variety of species, which are subjected to biological investigations. One of the key biomedical applications of the genomic sequence data is to identify genetic polymorphisms associated with a vast range of disease processes by alignment analysis against a reference. The alignment analysis of genetic sequence information is rather cumbersome especially when the size of the sequences to be compared is large, and this requires a certain level of training in molecular biology and genomics.
Recent focus on the personalized genome project suggests that the genetic sequence data from individuals, and presumably from animals and plants as well, can be used as a tool for specific identification for medical as well as administrative purposes. However, most genetic sequence data are simply too bulky to be used as a tool for rapid daily identification purposes.
The invention is based, at least in part, on the discovery that genetic sequence data, e.g., nucleic acid or amino acid sequences, can be represented in new, so-called Genetic Images, that provide a compact, portable image that can be analyzed electronically (e.g., by computer) or optically, e.g., visually or by optical scanning devices. In the new methods, genetic sequence data for a given sequence is first converted into a numeric data set, which is, in turn, encoded to form a Genetic Image. The Genetic Image can be traced backwards to determine the original genetic sequence data.
In one aspect, the invention features computer-implemented methods of forming a numeric data set that represents a nucleotide sequence. These methods include receiving electronic information representing a nucleotide sequence comprising a contiguous series of nucleotides; obtaining an electronic set of Genetic Analyzers, wherein each Genetic Analyzer comprises “n” nucleotides; wherein the set comprises all possible combinations of “X” different nucleotides present in the nucleotide sequences at each of “n” positions of a Genetic Analyzer in the set; wherein the set has a known order of Genetic Analyzers; wherein Xn is the number of Genetic Analyzers in the set; and wherein each Genetic Analyzer has a unique sequence that provides a cut site within the nucleotide sequence at a specified site within or at an end of each segment of “n” nucleotides that is identical to a given Genetic Analyzer; converting the nucleotide sequence with the ordered set of Genetic Analyzers into numeric data that comprises a series of groups of numbers, wherein a group of numbers is generated for each unique Genetic Analyzer of the set of Genetic Analyzers, with each number in the group comprising a total number of nucleotides between successive cut sites in the nucleotide sequence provided by the given unique Genetic Analyzer, and wherein the groups of numbers in the numeric data set are organized in the known order of the set of Genetic Analyzers; and generating a numeric data set that comprises, in order, the first n−1 nucleotides of a 5′ end of the nucleotide sequence, the numeric data, and a 3′ nucleotide of the nucleotide sequence.
These methods can further include encoding the numeric data set into an electronic representation of a genetic image; and storing the electronic representation of the Genetic Image in a machine-readable storage device. These methods can also further include displaying the electronic representation on a display device to provide a visible genetic image and/or providing the electronic representation to a printer and printing a visible genetic image on a substrate.
In another aspect, the invention features tangible machine-readable storage devices that include a digital representation of an ordered set of Genetic Analyzers, wherein the set of Genetic Analyzers includes a digital representation of a series of nucleotide sequences; wherein each Genetic Analyzer includes “n” nucleotides; wherein the set includes all possible combinations of “X” different nucleotides present in the nucleotide sequences at each of “n” positions of a Genetic Analyzer in the set; wherein the set has a known order of Genetic Analyzers; wherein Xn is the number of Genetic Analyzers in the set; and wherein each Genetic Analyzer has a unique sequence that provides a cut site within a nucleotide sequence at a specified site within or at an end of each segment of “n” nucleotides within the nucleotide sequence that is identical to a given Genetic Analyzer.
In these storage devices, the order of the Genetic Analyzers within the set can be, for example, alphabetical. In certain embodiments of these storage devices, n=4 and X=4. In various embodiments, the storage device can be a memory within a computer or a portable and tangible machine-readable medium.
In another aspect, the invention also includes articles of manufacture that are or include a tangible object; and a Genetic Image displayed on the tangible object, wherein the Genetic Image comprises non-alphanumeric markings in machine-readable form, wherein the Genetic Image when read by a machine causes a processor to decode the Genetic Image into a numeric data set and convert the numeric data set into a specific genetic sequence, such as a nucleotide or amino acid sequence. The tangible objects in these articles of manufacture can be, for example, a container, piece of paper or plastic, or a label, or any other article upon which a Genetic Image can be represented, such as an electronic display device. In these Genetic Images, the image can be an array of colored pixels.
The invention also includes tangible machine-readable storage devices that include a numeric data set that when read by a machine can causes a processor to (a) encode the numeric data set into an electronic representation of a Genetic Image, wherein the Genetic Image comprises non-alphanumeric markings in machine-readable form, wherein the Genetic Image when read by a machine causes a processor to decode the genetic image to provide a specific genetic sequence; or (b) convert the numeric data set into a specific genetic sequence.
In these tangible storage devices, the storage device can be or include an electronic memory within a computer, a universal serial bus (USB) compatible memory, or a magnetic or optical disk.
The invention also includes methods of generating sets of Genetic Analyzers. These methods include selecting a length “n” of a sequence of characters in each Genetic Analyzers; selecting “X” as the number of different characters in each Genetic Analyzer; calculating all possible combinations of “X” different characters present in a sequence at each of “n” positions of a Genetic Analyzer to create a basic set of Xn Genetic Analyzers; arranging the basic set of Genetic Analyzers in a specific order to create an ordered set of Genetic Analyzers; and storing the ordered set of Genetic Analyzers in a machine-readable storage medium.
In these methods, the ordered set of Genetic Analyzers can include a digital representation of a series of nucleotide sequences; wherein each Genetic Analyzer includes “n” nucleotides; wherein the set comprises all possible combinations of “X” different nucleotides present in the nucleotide sequences at each of “n” positions of a Genetic Analyzer in the set; wherein the set has a known order of Genetic Analyzers; wherein Xn is the number of Genetic Analyzers in the set; and wherein each Genetic Analyzer has a unique sequence that provides a cut site within a nucleotide sequence at a specified site within or at an end of each segment of “n” nucleotides within the nucleotide sequence that is identical to a given Genetic Analyzer. For example, “n” can be 4, and the characters can be nucleic acids or amino acids.
In yet another aspect, the invention features methods of reading a Genetic Image that represents a nucleotide sequence. These methods include obtaining an article of manufacture that has one or more Genetic Images as described herein; scanning the article of manufacture to convert markings of the Genetic Image into electronic data; decoding the electronic data to obtain a numeric data set that represents at least one nucleotide sequence; and converting the numeric data set into a nucleotide sequence. For example, converting the numeric data set into a nucleotide sequence can include the use of a known ordered set of Genetic Analyzers, as described herein.
The invention also includes methods of comparing two or more nucleotide sequences by obtaining at least two articles of manufacture with Genetic Images as described herein representing first and second nucleotide sequences; scanning the articles of manufacture to convert markings of the respective Genetic Images into electronic data representing the first and second nucleotide sequences; comparing the electronic data representing the first and second nucleotide sequences to locate any differences; decoding the electronic data of any differences to obtain numeric data sets that represent the differences between the first and second nucleotide sequences; and converting the numeric data sets using an ordered set of Genetic Analyzers to provide a nucleotide sequence representing the differences between the first and second nucleotide sequences.
In another aspect, the invention also includes systems for generating Genetic Images that includes a processor; a machine-readable storage device; and an ordered set of Genetic Analyzers as described herein in the storage device; wherein the processor is programmed with a program that causes the processor to: receive electronic information representing a nucleotide sequence including a contiguous series of nucleotides; obtain the ordered set of Genetic Analyzers from the storage device; convert the nucleotide sequence with the ordered set of Genetic Analyzers into numeric data that comprises a series of groups of numbers, wherein a group of numbers is generated for each unique genetic analyzer of the set of Genetic Analyzers, with each number in the group comprising a total number of nucleotides between successive cut sites in the nucleotide sequence provided by the given unique Genetic Analyzer, and wherein the groups of numbers in the numeric data set are organized in the known order of the set of Genetic Analyzers; and generate a numeric data set that comprises, in order, the first n−1 nucleotides of a 5′ end of the nucleotide sequence, the numeric data, and a 3′ nucleotide of the nucleotide sequence.
In these systems, the processor can be further programmed to encode the numeric data set into an electronic representation of a Genetic Image; and store the electronic representation of the Genetic Image in a machine-readable storage device. These systems can further include a display device and the processor can be further programmed to display the electronic representation on the display device to provide a visible Genetic Image. These systems can further include a printer and the processor can be further programmed to provide the electronic representation to the printer and to cause the printer to print a visible Genetic Image on a substrate.
The invention also features systems for reading Genetic Images. These systems include a processor; a machine-readable storage device; a scanner that scans an image and converts the image into electronic data; and an ordered set of Genetic Analyzers as described herein in the storage device; wherein the processor is programmed with a program that causes the processor to: obtain the electronic data from the scanner; obtain the ordered set of Genetic Analyzers from the storage device; decode the electronic data to obtain a numeric data set that represents at least one nucleotide sequence, wherein the electronic data comprises a series of groups of numbers, and wherein a group of numbers is generated for each unique Genetic Analyzer of the set of Genetic Analyzers, with each number in the group comprising a total number of nucleotides between successive cut sites in the nucleotide sequence provided by the given unique Genetic Analyzer, and wherein the groups of numbers in the numeric data set are organized in the known order of the set of Genetic Analyzers; and convert the numeric data set into a nucleotide sequence with the ordered set of Genetic Analyzers.
As used herein, a “Genetic Image” is a representation, e.g., a marking on a tangible, physical object, or an image on a screen or monitor, or an electronic representation stored on a machine-readable medium, of genetic sequence data that has been converted into a machine-readable numeric data set and then encoded to form the Genetic Image. The genetic sequence data represents at least one biopolymer sequence, such as a nucleic acid sequence, e.g., DNA or RNA, or an amino acid sequence.
Genetic sequence data is first converted into a numeric data set, and then that numeric data set is encoded to form the Genetic Image that is machine readable. Such a Genetic Image is machine readable, in that an automated optical or non-optical (e.g., electronic) process can be employed to input or “read” the encoded sequence data for analysis and/or further processing. In some embodiments, a human can visually read the Genetic Image. In various embodiments, encoded sequence data can include alphanumeric data, or can be incorporated into a form such as a radiofrequency identification (RFID) element, hologram, a solid state memory element, a magnetic element, a magneto-optical element, an optical disc element, an image format such as a Joint Photographics Experts Group (JPEG) image or Portable Network Graphics (PNG) image, or the like. In some embodiments, the sequence data is encoded as a PNG.
As used herein, a biopolymer is a molecule that comprises a plurality of biologically derived monomer units bonded in a particular sequence. Typical examples include nucleic acid sequences, such as DNA, RNA, and the like, and amino acid sequences, such as polypeptides and proteins. Thus, the monomer units can include ribonucleotides, ribonucleosides, deoxyribonucleotides, deoxyribonucleosides, amino acids, and the like. The monomer units can also include unnatural or synthetic amino acids, nucleotides, or nucleosides, or unnatural or synthetic compounds employed to mimic, substitute, or replace natural amino acids, nucleotides, or nucleosides. Accordingly, the biopolymer can include natural and unnatural peptides, proteins, enzymes, antibodies, polynucleotides or polynucleotides such as single or multiple stranded DNA or RNA, messenger RNA (e.g., messenger RNA derived from primary blood mononuclear cells), peptide nucleic acids, and the like. Note, therefore, that the term “genetic” in “Genetic Image” is illustrative and is not intended to limit the sequence data to DNA or RNA sequences from a natural genome, or peptide, proteins, etc. that correspond to a natural genome.
As used herein, genetic sequence data is information that describes at least a portion of the sequence of a biopolymer. Typical examples include genomic sequence data, such as the sequence of a genome, a chromosome, a gene, a transposon, retrotransposon, endogenous retroviral element, retrovirus genome, retrovirus protein, or portion thereof, or the like. In various embodiments, the sequence data can represent a continuous portion of the biopolymer; a full sequence of the biopolymer; a polymorphic sequence; a restriction fragment length polymorphism (RFLP) profile, or a single nucleotide polymorphism (SNP) profile, or the like.
As used herein, “non-sequence” data is any data of interest other than the sequence data. Typical examples of non-sequence data can describe one or more aspects of a subject, a phylogenetic classification, an organism, a cell, a sample, an experiment, a data origin, a name, a chromosome, a gene, a transposon, a retrovirus, a trademark or other commercial mark, an identifier such as a license or permit number, a government regulatory stamp or approval code, or the like. The non-sequence data can be human readable and/or can be encoded in a machine-readable format. In various embodiments, the non-sequence data can be encoded in a format compatible with Automatic Identification and Data Capture (AIDC). In some embodiments, the sequence data and the non-sequence data can each be independently encoded in alphanumeric data, or into a form such as a barcode, a hologram, a radiofrequency identification (RFID) element, a solid state memory element, a magnetic element, a magneto-optical element, an optical disc element, an image format such as PNG or JPEG, or the like. In particular embodiments, at least a portion of the non-sequence data can be in a human-readable format, and at least a portion of the sequence data can be encoded in a non-human-readable, machine-readable format, typically an encrypted machine-readable format. Such an embodiment can, for example, permit users to read identifying, non-confidential non-sequence data from a Genetic Image label, while sensitive sequence data, being encoded in the form of the Genetic Image (or optionally encrypted as well), can be held confidential, with access limited to users in possession of a corresponding cryptographic key. In some embodiments, the sequence data and the non-sequence data are each independently encoded in the Genetic Image, such as a PNG image. In various embodiments, at least one of the sequence data and the non-sequence data is encrypted. In certain embodiments, the sequence data and the non-sequence data are encrypted with different encryption keys.
As used herein, a polymorphic sequence is a sequence which is nominally conserved in a population, but which contains two or more distinct particular sequences in that population. Thus, in various embodiments, polymorphic sequence data corresponds to an individual species, subject, cell type, disease state, gene, chromosome, retrovirus, endogenous retroviral element, for example, as compared to other such species, subject, cell type, disease state, gene, chromosome, retrovirus, or endogenous retroviral element.
As used herein, a restriction fragment length polymorphism (RFLP) is a variation in the sequence of a genome that can be detected by digesting the sequence into fragments with restriction enzymes and analyzing the size of the resulting fragments, e.g., by gel electrophoresis. As used herein, a restriction fragment length polymorphism (RFLP) profile includes data that describes a collection of subsequence fragments generated by operation of a restriction enzyme on one or more copies of a parent sequence, such as a DNA or RNA sequence. An RFLP profile typically includes data such as the number of unique fragments, the size of each unique fragment (e.g., as determined by electrophoresis), and/or the number or intensity of each unique fragment, or the like. Typically, an RFLP profile can correspond to sequence data that relates to an individual species, subject, cell type, disease state, gene, chromosome, retrovirus, or endogenous retroviral element, thereby identifying the source of the sequence data.
As used herein, a single nucleotide polymorphism (SNP) is a single nucleotide variation in a genomic nucleic acid sequence, e.g., that differs between different individuals of the same species. Known SNPs or SNP patterns have been shown to correspond to a particular species, individual, cell type, disease state, gene, chromosome, retrovirus, or endogenous retroviral element and can be detected using the methods described herein.
As used herein, a restriction enzyme or restriction endonuclease is a biological protein (enzyme) that recognizes a specific nucleic acid sequence and cuts double-stranded or single-stranded DNA or RNA at a particular location within that specific nucleotide sequence (known as a restriction site).
As used herein, a Genetic Analyzer is a software algorithm that recognizes, in silico, a predefined sequence within a longer sequence, and “cuts” (separates the longer sequence in silico) at a predefined location within or after that predefined sequence. A specific Genetic Analyzer can be referred to by the length of the sequence it recognizes, such as a “four-nucleotide Genetic Analyzer,” which indicates a Genetic Analyzer that recognizes a sequence that is four nucleotides long. A Genetic Analyzer can cut the recognized sequence at the end of that sequence, e.g., just after the fourth of four nucleotides when using a four-nucleotide Genetic Analyzer, or it can cut at some other predefined location within the recognized sequence. Thus, the Genetic Analyzer is not a physical restriction enzyme (it is not a biological protein), but acts like one in silico. As described herein, defined sets of multiple Genetic Analyzers are used to cut long genetic sequence in silico to generate a set of unique fragments that are then recorded, along with additional information, to generate a numeric data set.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.
Other features and advantages of the invention will be apparent from the following detailed description, and from the claims.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
The disclosed invention generally relates to Genetic Images, methods of making Genetic Images, and methods of using Genetic Images to store, retrieve, and compare genetic sequence information. The invention includes new protocols to convert any genetic sequence (DNA and RNA), or an amino acid sequence, into a numeric data set that is then encoded to generate a Genetic Image. The Genetic Image can be traced backwards to determine the original genetic sequence information.
1. General Overview of Genetic Images
A Genetic Image is a representation of genetic sequence information, e.g., DNA or RNA, that can be analyzed, e.g., visually or by machine. The Genetic Image is a compressed and encoded form of a genetic sequence that takes far less storage space than the original sequence information, and can be easily analyzed and compared with other Genetic Images to easily detect differences between two different genetic sequences.
In various embodiments, the numeric data set that represents a specific genetic sequence (e.g., a sequence that contains a large amount of genetic information) can be encoded to form a Genetic Image that is represented in an image format such as JPEG, JPS (JPEG Stereo), PNG, or PNS (PNG Stereo).
In other embodiments, the Genetic Image can be in the form of a hologram, a radio frequency identification (RFID) element, a solid-state memory element, a magnetic element, a magneto-optical element, an optical disc element, or the like. In general, the GA analysis of the sequence creates a dataset that is then processed to form a visualization of that data, or the Genetic Image. This is similar to any image, so you can store it on a flash drive or some other electronic media as well as print it on paper or other media. The image formats can also be represented electronically on a monitor or screen, such as on a computer monitor, a mobile telephone screen, or on a personal digital assistant (PDA) screen. In each case, the representation permits visual or optical analysis and comparison, e.g., with a laser scanner or image capture device, such as a charge-coupled device (CCD). Images on paper or other non-electronic media can be scanned, e.g., digitally, and then compared by machine. For example, these images can then be compared using standard pattern recognition software, such as fingerprint matching or facial recognition programs. Alternatively, the Genetic Images can also be analyzed and compared by computer in digital, electrical form without the need for a tangible printout or image represented on a computer or other screen or monitor.
In some embodiments, the sequence data can be encrypted. As used herein, “encrypted” sequence data has been transformed by a cipher algorithm so that the sequence data typically cannot be read or interpreted unless first decrypted with a corresponding cryptographic key. Some examples of encryption formats include, but are not limited to AES-256, RSA-256, and the like. However, the process described herein to create the Genetic Images already provides a very secure system, because the length and the cut location within the Genetic Analyzers, and the order of the Genetic Analyzer set used are all, in effect, “keys” that are required to read the Genetic Image. Also, the non-sequence data that might be stored together with the Genetic Image can also be encrypted using any standard encryption format.
The Genetic Images described herein may typically be used to indicate the correspondence of the data encoded thereon to some other object or subject, such as a patient file, a sample container, a patient ID bracelet, a tag that can be affixed to a test animal or the animal's cage, a shipping or customs label, a license, a permit, a security badge, a passkey, an entry ticket, a particular location or address, and the like. When the Genetic Image is represented on a label, it can be in the form of a pattern printed on or embedded in the surface of a sample container, an implanted tag on a person or an animal, and the like. The label can be an inert substrate that incorporates the sequence data as a pattern, e.g., as a printed code on adhesive backed paper, cloth, plastic, metal, or the like. The label can be a machine-rewriteable substrate, such as a magnetic strip or disk, a writeable digital video disc, or a radio frequency identification (RFID) tag. The label can also be a temporary physical embodiment of the encoded, machine-readable data, for example, as an image embodied in activated pixel elements, e.g., polarized liquid crystal pixels, light emitting diode pixels, electronic paper pixels, or the like, for example, as in a cell phone display or on a computer or other monitor. Sequence data can thereby be stored by incorporating the sequence data into the Genetic Image, and can be retrieved by reading and decoding the Genetic Image, for example, with a corresponding machine reader. Also, sequence data can be compared by, for example, visually comparing the encoded data, or by reading the encoded data into a corresponding machine reader and therein automatically comparing the data. In some embodiments, the encoded non-sequence data can be visually compared by a person while still leaving the sequence data encoded therein in non-human readable form. For example, sequence data can be encoded in an image that does not facilitate human readability of the sequence, but nevertheless, two images corresponding to same or different sequences may appear visually the same or distinct to a person viewing the two images.
2. General Overview of Methods of Generating Genetic Images with Genetic Analyzers
As shown in the flowchart of
If the “sequence” is a non-genetic sequence, such as a sequence of letters, numbers, and/or symbols rather than nucleic acid or amino acid sequences, the Genetic Analyzers would then similarly include letters, numbers, or symbols, and not be to be limited to nucleic acid bases (ACGT) or amino acids. Note that each unique Genetic Analyzer in a set of Genetic Analyzers “cuts” the nucleotide sequence immediately after a segment of nucleotides that is identical to the sequence of the given Genetic Analyzer. Thus, a Genetic Analyzer AGG will be said to “cut” the nucleotide sequence, e.g., after every occurrence of the AGG segment within the nucleotide sequence. Of course, the cut site does not have to occur at the end of the Genetic Analyzer, but at any pre-specified location within its sequence. For example, the Genetic Analyzer could be defined to cut after each first nucleotide, so the Genetic Analyzer AGG would “cut” between the “A” and “G” at every occurrence of the AGG segment.
Once the numeric data set is created, it can be converted, using other software programs, into a Genetic Image, e.g., as shown schematically in
As discussed briefly above, in one example, a set of Genetic Analyzers is a group of all possible combinations of the corresponding nucleotides (A, C, G, and T/U) at each position of a certain Genetic Analyzer nucleotide sequence length (or amino acids at each position of a Genetic Analyzer of a certain length of amino acids). In principle, the Genetic Analyzer sequence length can range from one to infinity, but in practice, the length of a Genetic Analyzer typically ranges from two to a length of interest, for example, a length that results in a computationally useful number of Genetic Analyzers given the computer resources available and the length of the sequences to be converted into a Genetic Image. Thus, Genetic Analyzers for nucleotide sequences are typically 2, 3, 4, 5, 6, 7, 8, 9, or 10 nucleotides in length. One would use a shorter Genetic Analyzer, e.g., 3, 4, 5, or 6 nucleotides in length, to cut a shorter genetic sequence, such as up to about a thousand nucleotide bases in length; whereas one would use a longer Genetic Analyzer, e.g., 7 or 8 nucleotides in length, to cut a longer genetic sequence, e.g., up to about a million nucleotide bases in length.
For example, a complete set of in silico Genetic Analyzers for a nucleotide sequence length of one is A, C, G, and T (for DNA) and A, C, G, U (for RNA). Likewise, a complete set of in silico Genetic Analyzers for a DNA nucleotide sequence length of two includes each of the 16 possible two-base sequences based on the four bases A, C, G, T (for DNA) or A, C, G, U (for RNA). A complete set of Genetic Analyzers having a length of three nucleotides contains 64 Genetic Analyzers. Thus, in general, a complete set of in silico Genetic Analyzers includes a number of Genetic Analyzers equal to the number (X) of different units, e.g., nucleotide bases or amino acids (which is four for nucleotides and 20 for coded amino acids) raised to the power of the sequence length (n) of the Genetic Analyzers, e.g., Xn.
As an example, this equation would be 43 for a set of Genetic Analyzers of 4 different nucleotide bases that are three nucleotides long=64 total Genetic Analyzers in the set (starting with AAA, AAC, . . . , and ending with TTT as shown in
In another example, the equation would be 204 for a set of Genetic Analyzers of 20 different amino acids, where each Analyzer is four amino acids long=160,000 total Genetic Analyzers in the set. Note that the length of the Genetic Analyzers can impact the size of the final dataset. Furthermore, the total number of fragment sizes generated may have the greatest effect on the Genetic Image size.
“Cutting” a sequence with a full set of Genetic Analyzers in silico converts the sequence into an ordered and unique set of numbers, which is referred to herein as a numeric data set. Since the analysis is performed in silico, any nucleotides or amino acids can be used in the Genetic Analyzers, and epigenetic information can be captured as well. Thus, the genetic sequence information, including any polymorphisms, such as single nucleotide differences or epigenetic differences, can be converted into a numeric data set. Epigenetic information refers to factors besides DNA sequence that can influence the development of an organism. For example, in methylation, a methyl group is added to the carbon-5 position of cytosine, which usually occurs in CpG (cytosine followed by guanine) dinucleotides. This methylation subtly affects an organism in many ways, such as by stabilizing gene expression or suppressing viral genes. One method of discovering these methylation sites is to treat isolated DNA with bisulfite, which converts unmethylated cytosine residues into uracil residues, but leaves methylated cytosine residues unchanged. When the bisulfite treated DNA is sequenced, these basepair changes can be detected by comparison to non-bisulfite treated sequences. The two images (pre and post bisulfite treatment) can be compared to find the methylation sites. These methylation sites can then be noted on the sequence file and detected and/or analyzed using the Genetic Analyzers. For example, the Genetic Analyzers can capture the methylation status by including a new “methylated” base, so instead of only the bases of ACTG, there could be the new base “X” (which can be any letter or symbol), which represents a methylated cytosine residue.
The conversion of nucleotide sequence information into a numeric data set enables the use of high-resolution graphics programs (using available graphics formats, such as PNG, JPEG, or the like) to encode the numeric data set to create a Genetic Image, which is a compact, portable, scannable, and traceable format. The Genetic Images can be scanned, e.g., to identify polymorphisms among different genetic sequences from humans and other species including microorganisms and plants. Due to the ordered characteristics of the numeric data points in the Genetic Image, the genetic polymorphisms identified during the analysis, e.g., optical scanning, are traceable to the original nucleotide sequence data. This protocol, involving the numeric conversion of genetic sequences using the Genetic Analyzers and the generation of a Genetic Image, is an efficient tool to store any genetic information in a compact and portable format, as well as to compare and trace polymorphisms at the genome and expression levels.
3. Methods of Generating Genetic Analyzers
As noted, the Genetic Analyzers are part of a software program and can be thought of as DNA restriction enzymes in silico. However, there are differences compared to actual DNA restriction enzymes used in vitro. First, in contrast to the limited number of available in vitro DNA restriction enzymes and corresponding recognition sites, the unique design of the Genetic Analyzers allows recognition of all possible combinations of nucleotide sequences for the sequence length of interest. Second, the Genetic Analyzers can recognize RNA nucleotide sequences without conversion into a cDNA format. Third, the Genetic Analyzers can capture epigenetic information, e.g., based on methylation of cytosine. For example, as noted above, the Genetic Analyzers can detect the methylation status by including a new “methylated” base, represented by a new base “X,” which stands for the methylated cytosine. Fourth, the actual cut site on the genetic sequence corresponding to the individual Genetic Analyzers is typically at the end of the defined sequence of the Genetic Analyzer, e.g., after the fourth nucleotide in a four-nucleotide long Genetic Analyzer, or at some other specified point corresponding to a location between two nucleotides within the Genetic Analyzers.
To synthesize a set of Genetic Analyzers with a defined nucleotide sequence length, all potential combinations of four nucleotides (A, C, G, T/U) at each position are calculated using an algorithm, e.g., a macro program designed within the Microsoft® Excel® Visual Basic program. This implementation is computationally tractable on contemporary desktop computers for Genetic Analyzer lengths up to 10 nucleotides. To facilitate the creation of sets of Genetic Analyzers that have a longer sequence length, e.g., 11, 12, 13, 14, 15, or more nucleotides in length, the same algorithm can be implemented more efficiently in another program, such as Mathematica® or MatLab®, or directly in a language such as C/CC+, Java, or the like. Table 1 below shows an exemplary Microsoft® Excel® macro program for synthesizing Genetic Analyzer sets, e.g., having 7 nucleotides in each member of the Genetic Analyzer set.
Once the entire set of possible combinations of Genetic Analyzers is calculated, they are put into a desired order, and the order is stored in memory or a machine-readable storage device. The order can be, e.g., alphabetical (see, e.g.,
4. Converting Genetic Sequences into Numeric Data Sets
Once the set of Genetic Analyzers has been generated, they are applied as a cutting device in silico to a specific target genetic sequence to generate a unique profile of cut fragments (in the form of a set of numeric data indicating their position and size of each cut) for the individual target sequence. The Genetic Analyzers can be generated anew each time, or they can be generated once and stored in memory and used as needed. Note that the order of the Genetic Analyzers in a set can change, and so different orders may be used at different times (and the exact order must be known to read the corresponding Genetic Image). Exactly how this information is stored and where will depend on the software design and the specific type of analysis. The resulting numeric data set, which is composed of cut fragments from the target sequence, is unique and enables the generation of a high-resolution Genetic Image for clear and rapid identification of any genetic polymorphisms among the sequences being analyzed.
An entire nucleotide sequence (DNA or RNA), which is subjected to a conversion analysis, is cut with one full set of Genetic Analyzers (e.g., a set of three-nucleotide Genetic Analyzers with 64 members, or a set of four-nucleotide Genetic Analyzers with 256 members). The Genetic Analyzers may be organized, for example, in an order of four different groups during the cut process depending on their recognition specificity for the nucleotide (A, C, G, or T/U) in the last position. For example,
The nucleotide sequence is cut with each Genetic Analyzer and the resulting cut fragments are recorded as a number (size of fragments) in the order of their positions from the 5′-end of the sequence. To convert the entire nucleotide sequence information into a numeric data set, all Genetic Analyzers in a set are utilized individually to cut the sequence. The numeric data set acquired from this conversion process (cutting) now contains information regarding the position and identity of every nucleotide in the sequence except for the few nucleotides on the 5′- and/or 3′-ends, depending on the set of Genetic Analyzers used.
The numeric data from each Genetic Analyzer, composed of ordered cut fragments, can be collected as a series of numbers in the order of the Genetic Analyzers utilized in this conversion process. The set and order of Genetic Analyzers is fixed during a cutting analysis of a sequence or group of sequences. The data set does need to be in a predetermined order so it can be analyzed or traced, but the actual Genetic Analyzer order can be altered from application to application, providing another level of security. The numbers are ordered because each set of Genetic Analyzers creates a set of ordered fragment sizes, or a list of fragment sizes in the order of appearance. Each group of fragment sizes is then ordered by the predetermined order of the set of Genetic Analyzers, which can be varied, but must be known to read the resulting Genetic Image.
To account for the 5′-end nucleotides, which are not recognized in a given set of Genetic Analyzers (e.g., the first three nucleotides if using 4-nucleotide set), their nucleotide identity (A, C, G, or T/U) can be entered at the beginning of the numeric data set without any additional conversion. In addition, the last nucleotide at the 3′-end, which is recognized by a Genetic Analyzer, but does not contribute to the generation of a relevant cut fragment (numeric data) due to its end location, can be attached to the end of the numeric data set. Thus, the final numerically converted sequence data set consists of: a few 5′-end nucleotides (variable depending on Genetic Analyzer set utilized)+a series of numbers (=size of cut fragments in the order of cut occurrence and Genetic Analyzers used)+one 3′-end nucleotide.
In the version of software described herein, there is only one end nucleotide that needs to be known, because when a sequence is cut with a Genetic Analyzer, that final fragment size will always be the length from the last cut site to the end of the sequence. For all the other fragments, you always know the last nucleotide of that fragment. It will be the same as the sequence of the Genetic Analyzer used. However, the end sequence of that last piece is unknown, because the end of it is not created by a cut. This will be true for all the last fragments for all Genetic Analyzers. However, there will always be a Genetic Analyzer that cuts at one base pair from the end of the sequence, creating a last fragment size of 1, so one can trace back all the other bases except that last one. To account for this, that last base and other important unchangeable information (the beginning n−1 bases, the GA size, and the GA order) need to be encoded directly into the data set to trace the Genetic Image back to the original sequence. Other variations of the software can eliminate the need for including the n−1 and last base data.
Alternatively, the cut fragment data from all Genetic Analyzers may be combined and reorganized as a number of cut fragments with same size. As a result, the numeric data set becomes more compact and still maintains the unique characteristics of the original nucleotide sequence for the generation of Genetic Image. In this embodiment, the information is ordered in a manner similar to a RFLP. Changes in the sequence are visible, because the total number of a certain fragment size(s) should change when cut with a full set of Genetic Analyzers. In this way, one can rapidly determine changes in sequence, and identify which sequences need to be studied or compared in more detail.
Genetic Analyzer AC (GA(2)-2) is represented once in the target sequence and so generates a cut just after its appearance in the target sequence, i.e., only after location 5. This creates two fragments, one that is five nucleotides long and the other that is ten nucleotides long. This creates two numbers “5” and “10” associated with this second Genetic Analyzer.
Most of the Genetic Analyzers cut once, in this example. Only Genetic Analyzers CC (GA(2)-6) and TG (GA(2)-16) cut twice. For example, the Genetic Analyzer TG cuts after location 2, and after location 9, thus creating three fragments that are two, seven, and six nucleotides long, respectively. Thus, this last Genetic Analyzer in the set, creates three numbers “2,” “7,” and “6” associated with this particular Genetic Analyzer.
Each recognition site creates an in-silico “cut” to generate a number representing the nucleotide length of the fragment created from individual Genetic Analyzers within the set. The numbers generated from these cut events (each associated with their specific Genetic Analyzers) are presented in a graphical presentation (
Numbers on the left vertical side of
The GA(3)-01 is colored blue, which indicates that this Genetic Analyzer ends in the letter T. To decode the sequence, there should then be a T at positions 12, 43, 91, 92, 93, and 105. The last fragment (at position 246) is not a fragment created by a cut, but by reaching the end of the nucleotide sequence and therefore is not used in reconstructing the original sequence. As shown along the right side of
The fragment information in
In general, the Genetic Analyzers are applied to a given genetic sequence using a sequence cutter tool software program, referred to herein as the “cutEvolution.” The cutEvolution tool is a program that reads amplified nucleotide sequence files and generates the numeric data set, which is a list of fragment sizes and/or total number of fragments generated for a given Genetic Analyzer. The location and name of the sequence files, the Genetic Analyzers to be used, and the output location and output type for the data are all defined in the cutEvolution project file.
The cutEvolution software 20 includes one or more sets of Genetic Analyzers (for example, in
The amplified nucleotide sequences and the Genetic Analyzers are read by the cutEvolution Input Processor module 26. Small specific sequences of DNA (Primer Set) matching the ends of a DNA sequence of interest can be used for PCR amplification of that region. However, in other applications, obtaining the sequence to be analyzed by a set of Genetic Analyzers does not have to be done by using primer sets and PCR. The following process is applied for all amplified nucleotide sequences input into the application:
1. The sequence is loaded and scanned for occurrences for each Genetic Analyzer in the list (64 Genetic Analyzers for 3 cutters, 256 Genetic Analyzers for 4 cutters, etc.).
2. For each match the fragment size is calculated as follows:
([Current Cutting Position]+[Size of Genetic Analyzer])−[Previous Cutting Position]
Exceptions are as follows:
1. At the beginning of each sequence scan, the [Previous Cutting Position] is set to 0.
2. If no match is found the fragment size is set to the sequence length of the original sequence.
3. The remainder of the sequence after the last match is the last fragment size.
The fragment sizes are written out in a specified serial order for each Genetic Analyzer and the order of the Genetic Analyzers are kept constant through the analysis for the selected sequence file.
In a specific embodiment, the output format can be comma separated values (csv), which can be easily imported to spreadsheets and other programs. In this embodiment, the output is organized in columns that represent the sequence ID (such as the subject ID, primer set ID, clone #) and rows that represent the Genetic Analyzers. In general, the data output can be organized in various arrangements, such as having the columns represent the sequence ID, and the rows representing the Genetic Analyzer set.
In this numeric data set, the first three letters (TGG) represent the first three nucleotides not cut by any four-nucleotide Genetic Analyzer, then a series of numbers (which each indicate the fragment sizes for a given Genetic Analyzer, e.g., AAAA cuts at fragment sizes (which relate to the cut position), which are in this example 27, 587, 1, 194, etc.), and then ends with C, which is a single nucleotide at the end of the original genetic sequence.
5. Encoding a Numeric Data Set to Generate a Genetic Image
The genetic sequence information, entirely converted into numeric data using a set of Genetic Analyzers as described above, can then be encoded to generate a unique Genetic Image. The numeric data set is encoded as a graphic image in the order of the cut events/fragments for each Genetic Analyzer to ensure the uniqueness of cut profiles for each sequence analyzed. Thus, the Genetic Images are encrypted, compressed versions of the numeric data sets.
Alternatively, reorganized data made by combining the cut fragment profiles from all Genetic Analyzers may be encoded to form a Genetic Image. In addition, encoding multiple versions of the numeric data set (created by using different sets of Genetic Analyzers) from the same nucleotide sequence may enhance the accuracy of the scanning results. The Genetic Image is compact for storage and presentation, portable, and can be tangibly incorporated into a label, etc. as discussed herein. The individual numeric data points in the Genetic Image are scannable for comparison analysis and tracing of the original sequence information.
The numeric conversion of the nucleotide sequence information enables the use of a high-resolution graphics program to present the complex sequence information in a compact and portable format. The numeric sequence information is encoded to a scannable and traceable Genetic Image using a program, e.g., as described in further detail below. A Genetic Image can be created in any of a variety of available formats, e.g., JPEG/PNG/GIF or the like. For example, a Genetic Image can be generated as a heat diagram in a PNG format (see, e.g., the World Wide Web at libpng.org).
Two exemplary types of Genetic Images can be generated from the fragment data of nucleotide sequences, which are calculated using the cutEvolution software tool. In both types of images, only one set of Genetic Analyzers are used. Multiple Genetic Images can be grouped together to create a larger image with more information, if necessary.
1. Fragment Blocks Image (FBI)—In this type of image, only information about the total number of generated fragments for multiple sequences are color-coded. These images use two colors: one to identify the sequence and the other to identify the total number of generated fragments by a specific Genetic Analyzer. The FBI uses the two-dimensional (X and Y) axis for organization, with the sequences listed on one axis and the Genetic Analyzer on the other.
2. Fragment Row Image (FRI)—In this type of image, information about the size and order of each generated fragment for one sequence is color-coded. This image also uses two colors: one to identify the sequence and the other to identify the fragment size. The FRI uses the two dimensional (X and Y) axis for organization, with the Genetic Analyzer listed on one axis and the cut/fragment number on the other.
Both the FBI and FRI images can be implemented in standard Portable Network Graphics (PNG) files. Programming libraries are used to create the Genetic Image by utilizing the Genetic Analyzer dataset to determine the correct color blocks and positions within the Genetic Image, and verifying the color from a predefined color map to guarantee consistency. The color data assignment, the block size, and/or the data organization within the Genetic Image can be modified to include other information, depending on the type of data to be stored.
To store a large amount of data and still be able to rebuild the original sequence, the data should be compressed, such as in a compressed binary storage media. The cutEvolution tool includes an Output Processor module to generate images, e.g., in the PNG format. The Output Processor Image module of the cutEvolution creates images that satisfy the following requirements:
1. The sequence data must be compressed so that comparisons between such large data sets can be done efficiently.
2. The Genetic Image must enable one to trace back to a specific location in the original sequence from any position in the image. This allows one to trace back to the original sequence when comparing two images.
3. The Genetic Image must also enable one to reconstruct the entire original sequence from Genetic Image.
Genetic Images are created based on the order of the Genetic Analyzers used in the cutting process discussed above. For example, in a simple FBI PNG-based image, each column represents the sequence and each row a specific Genetic Analyzer. With this type of alignment, any data point (represented, e.g., as x and y coordinates, and color) in the Genetic Image can be tracked back to the sequence and the Genetic Analyzer. This simple alignment organization can be modified depending on the complexity and purpose of the Genetic Image. The color of the data point is used to encode detail information, such as the Primer ID, Clone number, Genetic Analyzer used and Fragment information.
The creation of a FBI is shown in
The RGB color scheme uses a mixture of Red/Green/Blue in which each color allows 256 shade combinations. RGB provides a total of 2563 combinations of colors, which equals 16,777,216 unique colors. The data generated by the cutter algorithm needs to be mapped into numerical values that do not exceed the maximum combination of RGB color variations. Because the data for a subject is large and most likely creates hundreds of primers and sequence combinations, the 2563 combinations are typically not enough to store the information adequately. For this reason each data point can be represented in two colors using the data alignment (max values in boxes) shown in
In
As shown in
6. Comparison and Decoding of Genetic Images
The basic methods of decoding and reading a Genetic Image, e.g., on a label, card, or electronic screen, include the steps of providing a Genetic Image, reading and decoding the Genetic Image to generate the corresponding numeric data set, and applying a known set of Genetic Analyzers to obtain the original corresponding genetic sequence. The same basic steps are used if the Genetic Image is represented on an electronic screen, e.g., of a mobile telephone, PDA, or similar device. The decoding step is generally a reversal of the encoding step described herein.
In addition, two or more of the Genetic Images generated from two or more different nucleotide sequences can be compared to identify differences, e.g., polymorphisms, by scanning and overlaying the images on a computer or other monitor, or on other tangible objects, such as labels, paper, or plastic media. The Genetic Images, which are generated using a standard image format such as PNG or JPEG, can be scanned optically using any high resolution graphics or image scanner, e.g., a flatbed scanner or passport scanner. By overlaying the Genetic Images derived from different sequences, any mismatches/polymorphisms are highlighted and subsequently the relevant code(s) derived from the numeric data point(s) can be easily identified.
The mismatches/polymorphisms present in different Genetic Images are directly linked to differences or polymorphisms in the sequence data. For example,
Each Genetic Image can be a tangible label that incorporates a machine-readable, encoded numeric data set (that corresponds to the genetic sequence data of a first specific biopolymer). In some embodiments, the Genetic Images can be configured so that the corresponding similarity or difference between the first and second sequences can be identified visually, e.g., by a human operator, or alternatively by machine. For example, in some embodiments, differences in the high-resolution Genetic Images can be discernable by human visual examination when there are colors and patterns within the images that are visible to the human eye. To facilitate such comparison, for example, Genetic Images can be incorporated into a semi-transparent material, allowing overlaid images to be compared to discern areas of overlap or difference. In addition, multiple analyses of data images of a single nucleotide sequence created using different sets of Genetic Analyzers can also assure the robustness of the scanned data. However, in practice it is far more practical to compare different Genetic Images by machine, because the differences between sets of data are typically too difficult to visualize by the human eye.
The following two factors can help trace the polymorphisms identified during the comparison of different Genetic Images to the original nucleotide sequences. First, the numeric sequence data generated by cutting with an entire set of Genetic Analyzers are capable of accounting for every single nucleotide on the original sequence by design. Second, the encoding system, which is used to create an ordered numeric data set of cut fragments to generate a Genetic Image, is designed to preserve the uniqueness/identification of the original nucleotide sequences analyzed.
The Genetic Images (or the underlying numeric data sets) can also be analyzed and compared within a computer, e.g., by analyzing the Genetic Images without ever printing or applying them to a tangible medium, or otherwise representing the Genetic Images on a monitor or screen. Thus, a plurality of data files representing Genetic Images can be compared by computer without the need for human visualization, though the images can be compared by computer while also being represented on a computer monitor.
As noted above,
For example,
As a result, amplification of a single nucleotide polymorphism into a number of changes in numeric data points should contribute to enhanced visual readability as well as accuracy of such Genetic Image comparisons. Subsequently, a brief survey of the profile of cut fragments surrounding the highlighted/mismatch fragments and respective Genetic Analyzers identifies the mismatch nucleotide(s) precisely, including any major deletion and/or addition. If confirmation of the polymorphisms identified during this tracing process is needed, a selective segment of nucleotide sequences encompassing the polymorphic locus can be subjected to an alignment analysis.
An image analysis program can be created that can scan the coded data and track the polymorphisms. Since the Genetic Image can be a physical representation of the sequence data (RFLP or full sequence), any polymorphisms can be rendered visible as a change to the image pattern; a program to track and analyze the changes can be created or adapted from existing technologies. Even if the sequence data is encrypted, pattern changes can still be analyzable, even human-viewable, allowing researchers to conduct blind studies. An application of this image analysis program in genomics would be the ability to scan and detect single nucleotide polymorphisms (SNPs) within a number of large sequences which are encoded into the Genetic Images. Since the images would be relatively small (compared to the complete sequence listing), many sequences can be compared quickly and accurately, without the need to download or store large sequence files for analysis.
7. Physical and Electronic Genetic Images and Uses Thereof
As noted above, the new Genetic Images can take physical form on any number of substrates including paper, cardboard, plastic sheeting and films, metal, ceramic, and other materials. The Genetic Image can be printed, engraved, e.g., by laser, embossed, or otherwise applied, without limitation, to the substrate. In addition the nature of the substrate onto which the Genetic Image is applied can take many shapes, and be in the form or any number of different objects. For example, the substrate can be part of, or take the form of, a small plastic card, such as a credit card or driver's license. The substrate can be the wall of a container, or a label attached to a container, e.g., a medicine vial. The substrate can be part of a surface of, or a label attached to, any object that needs a specific identification.
The Genetic Images can also be represented electronically and/or optically, e.g., on a computer monitor or on the screen of a television, a mobile telephone, or a personal digital assistant (PDA), or any other similar device that includes a screen that can exhibit the Genetic Images. These electronic/optical representations of the Genetic Images can be presented temporarily, while they are being analyzed, scanned, and/or compared with other Genetic Images, and can then be deleted from the monitor or screen. Of course, a Genetic Image can be stored in a machine-readable form, e.g., as the numeric data set or as the Genetic Image itself, e.g., as a PDF.
Thus, the new Genetic Images can be placed on personal identification cards, e.g., along with name, address, and/or other information. In other words, the new Genetic Images can be used as a “Universal ID” code, in which each Genetic Image represents a unique genomic sequence data, e.g., based on individual subject's genetic material. Typically, subjects may be randomly assigned with identification numbers for various reasons, such as a social security number, a driver's license number, a patient ID number, and the like. A patient can even accumulate multiple ID numbers within a single medical network, such as one when he visits his regular physician and another if he is rushed to the emergency room for immediate care. If the patient transfers to a different medical network, he can be assigned even more ID numbers. On the other hand, a “Universal ID” can be, first of all, unique and specific, and can be valid no matter where the person may be located. Further, since the “Universal ID” can be based on encrypted sequence data, privacy of the patient's genomic data can be maintained. Similarly, such a “Universal ID” code can be established for forensic purposes, phylogenetic studies, animal experiments, regulatory or safety monitoring of foods, organisms, and other biological products, monitoring of endangered species, monitoring of synthetic sequence data or DNA identification tags, or the like.
The Genetic Image when used as a “Universal ID” can also be represented on the screen of a mobile telephone or PDA or other similar device, whenever needed, e.g., to gain access to a building (such a court house or school), pass through an identification checkpoint, enter an airplane or other secure vehicle or location, make a purchase with a credit card that requires the identification of the cardholder (e.g., at automated gasoline pumps and other automated payment systems).
The new Genetic Images can be used in any situation in which an identification of a person, animal, plant, or micro-organism is required. For example, the Genetic Images can be used in commerce, e.g., on foodstuffs (packaging) and agricultural products, e.g., to confirm that a particular vegetable, fruit (e.g., grapes, apples, or oranges), fish (e.g., tuna for sushi), meat (e.g., Japanese Kobe beef), or processed food or beverage (such as a cheese or a wine) is in fact what it is alleged to be.
8. Error Checking of Genetic Images
The application of a second set of Genetic Analyzers to the same target genetic sequence can be used as an elegant method of error checking of a resulting numeric data set and of the encoded Genetic Images. If the second set of Genetic Analyzers provides a numeric data set (and Genetic Image) that can be reconstructed to provide the same original genetic sequence, then one can be assured that the system has worked properly.
9. Hardware and Software Implementations
The memory 1020 stores information within the system 1000. In some implementations, the memory 1020 is a computer-readable medium. The memory 1020 can include volatile memory and/or non-volatile memory.
The storage device 1030 is capable of providing mass storage for the system 1000. In one implementation, the storage device 1030 is a computer-readable medium. In various different implementations, the storage device 1030 may be a disk device, e.g., a hard disk device or an optical disk device, or a tape device.
The input/output device 1040 provides input/output operations for the system 1000. In some implementations, the input/output device 1040 includes a keyboard and/or pointing device. In some implementations, the input/output device 1040 includes a display device for displaying graphical user interfaces.
The features described can be implemented in digital electronic circuitry, or in computer hardware, software, firmware, or in combinations of them. The features can be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device, for execution by a programmable processor; and features can be performed by a programmable processor executing a program of instructions to perform functions of the described implementations by operating on input data and generating output. The described features can be implemented in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. A computer program includes a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, and the sole processor or one of multiple processors of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. Computers include a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, ASICs (application-specific integrated circuits).
To provide for interaction with a user, the features can be implemented on a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user and a keyboard and a pointing device such as a mouse or a trackball by which the user can provide input to the computer.
The features can be implemented in a computer system that includes a back-end component, such as a data server, or that includes a middleware component, such as an application server or an Internet server, or that includes a front-end component, such as a client computer having a graphical user interface or an Internet browser, or any combination of them. The components of the system can be connected by any form or medium of digital data communication such as a communication network. Examples of communication networks include, e.g., a LAN, a WAN, and computers and networks that form the Internet.
The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a network, such as the described one. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
The processor 1010 carries out instructions related to a computer program. The processor 1010 may include hardware such as logic gates, adders, multipliers and counters. The processor 1010 may further include a separate arithmetic logic unit (ALU) that performs arithmetic and logical operations.
A number of embodiments of the invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. Accordingly, other embodiments are within the scope of the following claims.
The inventions described herein were made, at least in part, with government support under a grant from the National Institute of General Medical Sciences (NIGMS R01GM071360). The Government has certain rights to the invention.
