Patent Application
20130103368
This invention relates generally to automated experimental design and more particularly to automated experimental design of a modification of a target DNA strand.
Scientists today rely on many tools and methods such as Basic Local Alignment Search Tool (BLAST), Nearest Neighbor Thermodynamics, primary and secondary structure prediction of nucleotide polymers, restriction enzymes, and knowledge of organism specific homologous recombination rates are just a few examples of techniques that are used in genetic experimentation.
Science is deeply rooted in experimentation. Experiments are to be tested and confirmed before products can be brought to market. For example, insulin is commonly produced by growing a biological precursor to insulin in bacteria. In addition, scientists will conduct many experiments before a process is developed, peer-reviewed, and perfected.
Genetic engineering is no exception to this procedure: whether it is new drug development, curing diseases with gene therapy, modifying enzymes to destroy pollutants, or genetically modifying rice to reduce vitamin A-deficiency in the developing world, genetic engineering generally follows this path:
Step 2 of this process is traditionally a tedious, heuristically driven, and error-prone process. A scientist is faced with trillions of combinations when creating an experimental design and often uses heuristics, best guesses, or intuition to create an experimental design. The end result can be a failed experiment, low yield, lost time, and/or lost money. This need not be the case, as there is a large amount of experimental understanding behind the process. However, not only is it very difficult for a human to account for most of the design parameters, it can be cost prohibitive for a scientist to master this niche field of knowledge. The scientist needs to work at a higher level in the process.
The heart of experimental design from step 2 above, lies in the selection of six “primers.” A primer is a small piece of genetic material that corresponds to an underlying genetic region but which also may have added sequences corresponding to restriction enzyme sequences or other alterations. Primers act to amplify specific regions of DNA and restriction enzymes act to cut specific regions of DNA. Combining specific amplified regions of DNA, carefully selected restriction enzymes, and other enzymes such as ligases, these things can act together as molecular scissors and glue for inserting and/or removing genetic material in vitro as well as in vivo.
A scientist will select from thousands of possible enzymes, thousands of possible individual primer solutions, and millions of individual primer-pair solutions when designing an experiment. Each of these primer choices is further subject to various experimental parameters (DNA concentration, salt concentration, melting temperature range, annealing properties, PCR programs, polymerases, etc.). However, there can be are literally trillions of possible six-primer solutions. Manually creating a simple error-free solution is difficult, tedious work. Manually selecting the best possible solution out of trillions of possibilities is very trying.
In addition to designing a six-primer solution, the scientist can design a reaction to make copies of the modified DNA. This is achieved today through the use of the Polymerase Chain Reaction (PCR). In order to amplify a region of DNA, a scientist needs some amount of that DNA which has the region of interest somewhere in that DNA (which is called the template) and two pieces of short strips of DNA called primers. The reaction occurs in a solution of buffers and enzymes.
A thermocycler holds small tubes where the reaction takes place, and is programmed to cycle rapidly and accurately through a series of timed temperature changes. The times and temperatures of the cycles are dependent upon properties of the template, primers, and reaction reagents and concentrations. A PCR program not optimized for a particular reaction could lead to a failed attempt at amplifying the desired region, or amplifying many unwanted regions.
A method and apparatus of a device that generates a primer pair design to amplify a template in a DNA strand is described. The device calculates a first and second plurality of primers, where each primer in the first plurality of primers is from a different region of the DNA template than each primer in the second plurality of primers. The device further calculates a set of primer pairs, where each of primer pairs include one primer from the first plurality of primers and one primer from the second plurality of primers, and each of the first plurality of primer pairs is calculated based on a penalty of combination between the two primers in that primer pair.
In another embodiment, a method and apparatus of a device that performs automated experimental design is described. The device receives a primer parameter input that is used to perform the automated experimental design. In addition, the device determines a plurality of possible primers. The device further calculates a set of six or more from the plurality of possible primers by calculating individual primer penalties for each primer in the set of six or more and inter-primer penalties between pairs of primers in the set of six or more using the primer input. In this embodiment, the set of six or more are designed to amplify a target in the DNA sequence.
In a further embodiment, a method and apparatus of a device that calculates a primer-enzyme combination is described. In one embodiment, the device receives primer input for the primer. In addition, the device receives an enzyme input sequence, wherein the enzyme input sequence is a sequence of nucleotide symbols and at least one of the nucleotide symbols is an ambiguity code. The device further calculates the primer using the primer input. Furthermore, the device calculates the enzyme that corresponds to the primer using the enzyme input sequence and the primer. In this embodiment, the calculated enzyme is the nucleotide sequence of the enzyme input with the ambiguity code is replaced by a non-ambiguous code.
Other methods and apparatuses are also described.
The present invention is illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate similar elements.
A method and apparatus of a device that generates a primer pair design to amplify a template in a DNA strand is described. In the following description, numerous specific details are set forth to provide a thorough explanation of embodiments of the present invention. It will be apparent, however, to one skilled in the art, that embodiments of the present invention may be practiced without these specific details. In other instances, well-known components, structures, and techniques have not been shown in detail in order not to obscure the understanding of this description.
Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification do not necessarily all refer to the same embodiment.
In the following description and claims, the terms “coupled” and “connected,” along with their derivatives, may be used. It should be understood that these terms are not intended as synonyms for each other. “Coupled” is used to indicate that two or more elements, which may or may not be in direct physical or electrical contact with each other, co-operate or interact with each other. “Connected” is used to indicate the establishment of communication between two or more elements that are coupled with each other.
The processes depicted in the figures that follow, are performed by processing logic that comprises hardware (e.g., circuitry, dedicated logic, etc.), software (such as is run on a general-purpose computer system or a dedicated machine), or a combination of both. Although the processes are described below in terms of some sequential operations, it should be appreciated that some of the operations described may be performed in different order. Moreover, some operations may be performed in parallel rather than sequentially.
The terms “server,” “client,” and “device” are intended to refer generally to data processing systems rather than specifically to a particular form factor for the server, client, and/or device.
A method and apparatus of a device that generates a primer pair design to amplify a template in a DNA strand is described. In one embodiment, a scientist uses the device to design a set of primers that can be used to amplify a region of interest in a DNA strand. In one embodiment, a scientist may use this automated design process to further tune the results for yield, price, on-hand consumable inventory, or other possible consumable solutions by specifying additional information a priority or applying this filtering information once the invention has returned a series of possible solutions.
For example and in one embodiment, a scientist uses the automated experimental design process to use a specific enzyme in an experiment but avoid low yields due to secondary binding. Sending this preference to the invention will result in a set of six or more that have the lowest possible hairpin, homodimer, heterodimer, and/or other types of secondary binding, while still ensuring the experiment can be run with the chosen enzyme.
As another example and embodiment, a scientist may use enzymes that are on hand due to tight budget, a tight deadline, or other constraint. The automated experimental design process returns results for possible experimental design solutions for scientist's specified enzyme inventory.
In a further example and embodiment, a scientist may request a list of the inexpensive solutions or another scientist may request the best possible solution out of all known possible enzymes in the world. As with the previous examples, the automated experimental design process can filter these results based on yield, price, or other factors after the invention has been run.
Listed below is a set of definition for terms used in the present specification:
Deoxyribonucleic Acid (DNA): A polymer present in living organisms, which carries genetic information.
Genetic Experiment (Hereafter referred to as Experiment): A gene targeting experiment is a six-step scientific process for constructing a novel genetic sequence in an organism. For example, modifying Bovine DNA to produce insulin-like proteins in their milk.
Experiment Design: The process of designing an Experiment Solution for a Genetic Experiment.
Experiment Solution: A series of optimized materials and methods for constructing a novel genetic sequence. Materials include but are not limited to primers, enzymes, buffers, and solutions. Methods include but are not limited to protocols for PCR, ligations, digestions, etc.
PCR Protocol: Includes a PCR protocol and reaction mixture concentrations (salt, buffers, cofactors, DNA and primer concentrations, etc.). The PCR protocol is a series of times and temperatures used in conjunction with a pair of primers to amplify a specific region of a DNA template.
PCR (Polymerase Chain Reaction): A scientific technique used in molecular biology to make many copies (millions or billions) of a particular DNA region.
Nucleotide: Nucleotides are molecules that, when joined together, make up the structural units of DNA. Nucleotides can be purine bases or pyrimidines. In DNA, the purine bases are adenine (A) and guanine (G), while the pyrimidines are thymine (T) and cytosine (C).
Primer: A short, specific sequence of consecutive nucleotides. Primers are usually between 12-35 nucleotides long.
Primer Pair: Two primers, a forward and reverse primer that are used in PCR to amplify a specific region of a DNA template.
Forward Primer: A primer that occurs on the sense strand of DNA.
Reverse Primer: A primer that occurs on the anti-sense strand of DNA.
Sense Strand: There are two strands to a DNA double-helix. One strand is designated as the sense strand and the other strand is designated as the anti-sense strand. The sense strand is what encodes a gene.
Anti-Sense Strand: There are two strands to a DNA double-helix. One strand is designated as the sense strand and the other strand is designated as the anti-sense strand.
Restriction Enzyme (Hereafter referred to as Enzyme): A restriction enzyme (also known as a restriction endonuclease) is a type II restriction enzyme that cuts double-stranded DNA at a specific DNA sequence in a specific way. For example, the EcoRI restriction enzyme cuts the nucleotide sequence GAATTC between the G and the A.
Template: A region of DNA that a scientist wants to amplify with PCR.
Region of Interest: A specific region of DNA that a scientist wants to study or modify.
Flanking Region: The regions immediately before and after the region of interest.
Insert: A specific DNA sequence used to replace a region of interest. The Insert is usually contained within a plasmid.
Plasmid: A circular piece of DNA used by scientists to store novel DNA strands, one of which is the insert.
Construct: The intermediate DNA product of a genetic experiment. The construct is formed by ligating the insert with the flanking regions of the region of interest.
Sequence: Nucleotides arranged in a specific order.
Binding: The Watson-Crick pairing of one or more nucleotides.
Base Pair: An instance of one nucleotide binding to its Watson-Crick complement.
Nucleotide: Nucleotides are molecules that, when joined together, make up the structural units of RNA and DNA.
Secondary Structure: A set of base pairings in one or more single strands of DNA or RNA that results in the strand(s) forming complicated structures.
Hairpin: A secondary structure formed by the binding of a single strand of DNA to itself.
Homodimer: A secondary structure formed by the binding of a single strand of DNA to a copy of itself.
Heterodimer: A secondary structure formed by the binding of a single strand of DNA to a different strand of DNA.
Secondary Binding: During PCR, the binding of a primer to a region of the template DNA not intended by the scientist.
Dynamic Programming: Dynamic programming is a method of solving complex problems by breaking them down into simpler subproblems and combing the results of these subproblems into an overall solution. Dynamic programing makes it possible to solve exponentially complex problems in a realistic amount of time.
In one embodiment, the method and apparatus automatically analyzes genetic experiment design solutions and produces one or more sets of six primer solutions for a given series of genetic material, a region of interest in this genetic material, a command to either insert or remove a piece of this genetic material, a list of enzymes to consider for this experiment, and a set of parameters for such things as DNA concentration, salt concentration, oligonucleotide length limits, melting temperature limits, GC % range, GC Clamp, self-dimer limits, cross-dimer limits, secondary binding limits, bulge options, stem/loop formation limits, 3′ annealing penalties, and other parameters typically associated with experimental design.
As described above, a scientist typically considers these parameters for each primer in a six-primer solution in designing the primer set. In addition, the scientist further considers interactions between the primers themselves. This can lead to trillions of possible combinations and an optimization problem that no human could ever hope to manually solve.
In one embodiment, template 102 is a sequence of nucleotides. In this embodiment, a scientist will typically want to modify some genetic material of the DNA system 100 as part of an experiment. The part of the genetic material that is to be modified is the target 110. The start 106 of the template 102 is the first nucleotide of the sequence and the end 108 is the last nucleotide in the sequence.
In one embodiment, the start 106 of the template 102 is called the 5′ side and the end 108 of the template 102 the 3′ side. A position in the template 102 that is closer to the start 106 of the template 102 is called “upstream” and a position that is closer to the end 108 of the sequence is called “downstream”.
In one embodiment, a scientist will modify a portion of the template 102 called the target 110. For example and in one embodiment, the scientist replaces the target 110 with some other genetic material called the insert 112. In another embodiment, the scientist removes the target 110.
In one embodiment, a scientist uses two enzymes and six primers to design a construct to remove the target 110. In this embodiment, both the enzymes and primers refer to specific DNA sequences. In one embodiment, part of the sequence of DNA is found on the template 110, and part of the other enzymes and primers are added. In one embodiment, enzymes sequences are 4 to 8 base pairs long (or longer), and overall primer sequences are generally between 12 and 35 base pairs long. As is known in the art, a base pair are two nucleotides on opposite complementary DNA or RNA strands that are connected via hydrogen bonds. For example and in one embodiment, in a DNA base pairing, adenine (A) forms a base pair with thymine (T) and guanine (G) forms a base pair with cytosine (C). In RNA, thymine is replaced by uracil (U). In one embodiment, the enzymes and primers are used as molecular scissors and glue to create a new genetic construct, which can be used to coerce the organism into replacing the target with the insert. In one embodiment, the enzyme sequences and/or primers sequences can be longer or shorter.
In one embodiment, each of the six primers 104A-F is located in separate “zones” in the diagram. For example and in one embodiment, P1104A is an abbreviation that stands for the range that primer 1 can be located. Similarly, P2104B, P3104C, P4104D, P5104E, and P6104F indicate areas of the template that the respective primer can be located in the template 102. In one embodiment, the P1104A and P6104F are relatively large and can be up to 500 nucleotide positions or long. On the other hand, the P2104B, P3104C, P4104D, and P5104E regions are relatively smaller and being in the range of 50 nucleotide positions or smaller.
In one embodiment, an enzyme sequence is a sequence of nucleotides that flank or are close to one of primers P2-P5132B-E. In one embodiment, these enzyme sequences 134A and 134C are used to cuts double-stranded DNA at a specific DNA sequence in a specific way. In this embodiment, enzyme sequences 134A and 134C are enzymes as defined above. For example and in one embodiment, one or more of the enzyme sequences 132B-C can be the EcoRI restriction enzyme cuts the nucleotide sequence GAATTC between the G and the A. In one embodiment, the enzyme sequence-primer pairs (e.g., P2132B—enzyme sequence E1134A) flank the target 130 are used to cut the target 130 at a specific point. While in one embodiment, enzyme sequences 134A and 134B are a first enzyme sequence and enzyme sequences 134C-D are a second enzyme sequence, in alternate embodiments, these enzyme sequences 134A-D can be all the same, all different, and/or another combination.
In addition to the DNA Organism of Interest 120, the DNA system 140 also includes the plasmid 122 that is used to deliver the insert 136A to the DNA organism of interest 120. In one embodiment, the plasmid 122 includes a sense strand 138A and an anti-sense strand 138B. As described above, the sense strand 138A is the strand that encodes a gene and the anti-sense strand 138B binds to the sense strand, as well as allowing for replication of the genetic material and chemical protection. Similar to the DNA organism of interest 120, the plasmid 122 includes primer-enzyme pairs on different strands. In one embodiment, the plasmid 122 includes enzyme sequence E1134B and primer P3132C on the sense strand 136A. In one embodiment, the primer P3132C is part of the insert 136A and the corresponding enzyme, enzyme sequence E1134B is attached to the primer P3132C. In another embodiment, the enzyme sequence E2134D and primer P4136A is part of the anti-sense strand 138B. In this embodiment, the primer P4132D is located on the anti-sense strand 138B in a location that is opposite to the end of the insert 136A. Furthermore, enzyme sequence E2134D is attached to primer P4136A and is in a location that is outside of the insert 136A.
In one embodiment, the enzyme sequences 134A-D and primers 132A-F of
In one embodiment, the P1132A and P6132F primers are known as “floating” primers and can be located in a much larger range. In one embodiment, the range is normally less than 1000 nucleotides in length and is at least 500 nucleotides upstream from the start of the target and roughly the same distance upstream (P1) or downstream (P6) from the end of the target. In another embodiment, the floating primers can be of a shorter or longer number of nucleotides.
In one embodiment, a scientist uses various tools to find a primer in these ranges. For example and in one embodiment, the scientist can manually ensure that each primer is within a specific melting temperature range, the primer has a certain GC %, does not end with certain nucleotide combinations, and the primer does not fold on itself (hairpins), bind to another copy of itself (homo-dimerization), bind to other primers in the same test tube (hetero-dimerization), and doesn't bind to too many other areas of the template (secondary-binding). Furthermore, the scientist also considers several other primer parameters as he attempts to locate a sequence of DNA that is “ideal.” This is typically accomplished by visually inspecting the primer ranges for a “good primer” while using several different tools to calculate melting temperature, hairpin analysis, dimer analysis, etc.
Once a scientist has selected what looks like a good primer, the scientist repeats this process for each of the remaining five primers. In one embodiment, the primers are separated into pairs. The P1/P2 primers 132A-B are the upstream primers, the P3/P4132C-D primers are the construct primers, and the P5/P6132E-F primers are the downstream primers. In this embodiment, each of these primer pairs will be in a separate test tube so the scientist ensures that the melting temperature of each of the primer pairs is within a specific number of degrees of tolerance. In addition, the scientist ensures that these two primers will not bind to each other instead of the template (the hetero-dimerization previously mentioned).
If any of the primers or primer pairs fail to meet these criteria, the scientist must choose another primer and look for a better solution. If no valid primers are available, the scientist will choose a different enzyme and repeat the entire process.
This entire manual process, however, is error-prone and time consuming and is very easy to select a primer that initially looks good but results in a poor yield. Many scientists do not know if their design is bad or if their experiment was contaminated. Furthermore, it can sometimes take months or many failed experiments before reaching a good yield.
It would be useful to a scientist to automate the primer selection process so as to minimize the experimental difficulties involved with the manual determination of primer as described above in FIG. 1AB.
In one embodiment, the computer 154 includes experiment module 158 and Polymerase Chain Reaction (PCR) protocol generation module 162. In one embodiment, the experiment module 158 is a module that is used to perform automated experimental design to design a set of primers that can be used to replace a target with another nucleotide sequence. For example and in one embodiment, the experiment module 158 receives input parameters from a user, such as the DNA strand, target, insert, and primer parameters (specific melting temperature range, GC %, certain excluded nucleotide combinations, enzyme input, etc.). The experiment module 158 outputs a set of primers based on the input parameters. Furthermore, the experiment module 158 calculates the set of primers such that one or more primers do not fold on itself (hairpins), bind to itself (homo-dimerization), bind to other primers in the same test tube (hetero-dimerization), and does not bind to too many other areas of the template (secondary-binding). Furthermore, the process that calculates the set of primers used by the experiment module 158 is further described in
In one embodiment, the PCR protocol generation module 162 receives the primers calculated by the experiment module 158 and a template for each reaction needed (three total in our Automated Experimental Design, four if a scientist chooses verification) and designs a PCR Program (set of instructions for a thermocycler) that optimizes the chance of getting good and specific yield. The PCR protocol generation module 162 is further described in
At block 204, process 200 validates the input data. In one embodiment, process 200 determines if the data is within a range that corresponds to the different input parameters. For example and in one embodiment, process 200 checks for illegal characters and/or characters that are outside the range expected for the corresponding input. Process 200 performs automated experimental design with the validated input data at block 206. In one embodiment, process 200 performs automated experimental design by optimizing the individual primers and primer sets based on the input data. Automated experimental design is further described in
Process 200 validates and ranks the results of the automated experimental design at block 208. In one embodiment, process 200 filters the results and ranks the results. In one embodiment, the automated experiment design of block 206 can take minutes or hours to complete depending on the scientist's parameters. Real-time filtering and ranking lets a scientist eliminate possible solutions and view results in near-time. For example and in one embodiment, process 200 performs real-time filtering and ranking by receiving different penalty weights via a user interface, such as the penalty weights panel 1808 as illustrated in
For example and in one embodiment, suppose a scientist forgot to include a GC clamp when submitting a request. Instead of resubmitting the requests, and waiting for the result, the scientist could simply turn on a GC-clamp filter and view the valid solutions.
In one embodiment, a scientist may also want to change solution ranking by assigning weight(s) to one or more input parameters. For example and in one embodiment, a scientist may decide that melting temperature is more important than GC percentage and assign a higher weight to the melting temperature penalty. Process 200 recalculate the penalties in real-time without having to recalculate a new automated experimental design.
If a wildcard enzyme optimization is selected, process 300 performs a wildcard enzyme optimization at block 308. In one embodiment, process 300 performs a wildcard enzyme optimization by computing prefix and suffix penalties for primers. For example and in one embodiment, an enzyme with an “N” as part of it will “match” any A, C, G, or T nucleotide. In one embodiment, wildcard enzymes increase the number of possible solutions and a special Wildcard Enzyme Optimization is used for this situation. Wildcard enzymes are enzymes that contain one or more of the International Union of Pure and Applied Chemistry (IUPAC) ambiguity codes. While the IUPAC codes for A, C, G, T, and U are for specific nucleotides, the codes M, R, W, S, Y, K, V, H, D, B, and N can refer to multiple nucleotides as known in the art. For example, the W code means A or T, the S code means G or C, and the N code means A, C, G, or T.
In one embodiment, a sequence with ambiguity codes can increase the number of possible primer-pair combinations to consider. For example and in one embodiment, the Sfi I enzyme contains 5 of the N ambiguity codes (GGCCNNNNNGGCC) resulting in 1024 possible combinations for this enzyme (each of the N codes could be an A, C, G, or T resulting in 45 combinations). Referring to our A and B primer list example from the primer pair optimizer section, above, process 300 can have 102.5 million primer pair combinations as opposed to 100,000 primer-pair combinations.
In one embodiment, many of these combinations would result in poor individual primers and primer pairs. For example, any five nucleotide sequence is undesirable (AAAAA, GGGGG, etc.) as is any palindromic enzyme sequence (e.g., GAATTC).
In one embodiment, the wildcard enzyme optimizer eliminates many of these combinations by computing prefix and suffix penalties for primers. For example and in one embodiment, process 300 may eliminate restriction enzymes that fail melting temperature criteria, GC percentage, GC clamp, and/or other constraints. In this embodiment, it is possible for process 300 to pre-compute the melting temperature, GC percentage, and annealing penalties of these enzyme prefixes or suffixes to reduce computation time. The wildcard enzyme optimization performs both this pruning and pre-calculation to substantially reduce computation time.
If a multi-enzyme optimization is selected, process 300 performs a multi-enzyme optimization at block 310. In one embodiment, process 300 performs a multi-enzyme optimization by iterating over a list of inputted enzymes. For example and in one embodiment, a scientist may specify a list of enzymes to consider for either the upstream or downstream enzyme. In this case, process 300 solves for each of the possible enzymes (static or wildcard) and identifying the best results. In one embodiment, to reduce time, a simple baseline is created after the first solution to weed out poor results. In one embodiment, a primer and primer pair must be better than the “worst’ solution or it is not worth considering.
In one embodiment, the multi-enzyme optimization is the process of iterating the automated experiment design across a range of enzymes. For example, a scientist may not know which enzyme will produce the best results for an experiment and will request that the automated experiment design be run across all enzymes in his inventory. Similarly, a scientist conducting research in a specific area of DNA may want to learn the best possible restriction enzymes for a region and can request that some or all known enzymes should be considered as part of the automated experiment design. At block 312, process 300 returns the results.
Process 400 generates a set of primers for each primer region 412A-F using a best of the worst process at blocks 404A-F, respectively. In one embodiment, a best of the worst process is an adaptive process for generating a minimum (and maximum) number of acceptable solutions for a primer range. For example and in one embodiment, each of these primers is given a scoring penalty and sorted before being returned as part of a primer list. The best of the worst process is further described in
Process 400 optimizes pairs of primers at blocks 406A-C. In one embodiment, process 400 optimizes pairs of primers for P1/P2 pairs 414A at block 406A, P3/P4 pairs 414B at block 406B, and P5/P6 pairs 414C at block 406C. In one embodiment, each of these primer lists generated from the best of the worst processes 404A-F are joined into primer pairs (upstream pairs (P1/P2 pairs 414A), construct pairs (P3/P4 pairs 414B), and downstream pairs (P5/P6 pairs 414C)). Furthermore, a similar ranking and sorting process takes place for the primer pairs based on primer-pair annealing and temperature variance.
In one embodiment, primer pair optimization occurs by breadth-first search. Given two lists of primers, the primer pair optimizer produces a list of the optimal primer-pair combinations. A primer pair is considered optimal if each of the primers has a low annealing penalty (e.g., using the annealing penalizer described below in
For example and in one embodiment, suppose that process 400 generates a list of 100 primers for P1 at block 404A (list A) and another list of 1000 primers for P2 at block 404B (list 8). In this example, process 400 would process 100,000 different possible primer pairs. In one embodiment, process 400 returns a list of the 100 best primer-pairs out of a possible 100,000 primer-pair combinations. In one embodiment, the number of the primer-pair results to be returned in an input parameter received at block 402.
In one embodiment, process 400 reduces the amount of computation times for the primer pair optimization by performing a breadth-first search and using this information as a baseline. In one embodiment, a breadth-first search is a search that initially sets a bound and determines if primer pairs fall within the bound. For example and in one embodiment, suppose process 400 receives the A and B primer lists above, the scientists has requested the top 100 results, and each of the primer lists are both sorted by penalty (the best primers being first in the list). In this example, process 400 at block 406A begins by taking the first ten items from each of the primer lists (the square root of the desired number of results) and establishing a penalty baseline. In this embodiment, individual primers penalties that are better than the primer-pair penalty baseline are considered as a potential primer-pair.
In one embodiment, process 400 calculates a penalty for each primer based on different penalties. While in one embodiment, the penalties are based on primer design parameters input by the scientist (self-annealing, nucleotide repeats, deviation from ideal melting point temperature, deviation from ideal GC percentage, etc.), in alternate embodiments, other factor could be used to calculate penalties (ΔG dissimilarity penalty, etc.). For example and in one embodiment, the penalty for a primer is calculated based on Equation (1):
P=a
sa
p
sa
+a
rep
p
rep
+a
Tm
p
Tm
+a
GC%
p
GC% (1)
where psa, prep, pTm, and pGC% are the calculated penalties for individual primer penalties for self-annealing, nucleotide repeat, deviation from ideal melting point temperature and deviation from ideal GC percentage and asa, arep, aTm, and aGC% are the respective weights. In one embodiment, process 400 calculates each of the individual penalties and use the inputted weights to determines the overall penalty for a primer with Equation (1).
For example and in one embodiment, if a primer in list A has a penalty that is worse than the 100th primer pair combination, process 400 knows that the penalty of the combination pair is going to be worse than the 100th item and process 400 can eliminate 1000 primer pair combination tests. In other words, if the penalty for A[i] primer is greater than the 100th primer-pair penalty, process 400 can forgo computing the primer-pair penalties of {A[i ], B[1 . . . N]}.
In this example, process 400 has three lists of primer pairs that can be considered for an overall six-primer solution. If process 400 averages 1000 candidates for each floating primer and 20 candidates for each fixed primer, process 400 will have 20,000 potential primer pair candidates to process. At block 408, process 400 assembles combinations of six primers and ranks these combinations. In one embodiment, process 400 assembles the combinations of six primers by creating some or all possible combinations of six primer pairs from the P1/P2414A, P3/P4414B, and P5/P6414C. In this embodiment, the three primer-pair lists of 20,000 candidates would yields eight trillion possible combinations. In practice, the majority of these combinations are could be poor choices. These poor combinations could be weeded out through the use of a “survivor” algorithm. In one embodiment, process 400 weeds out primer pairs by determining if a primer pair has a combined penalty that is greater than a threshold, other combinations of primer pairs using this one primer par are excluded, as these combinations will have a penalties that are above the threshold. For example and in one embodiment, if a P1/P2 primer pair has a penalty above a threshold, other combinations of P3/P4 primer pair with this P1/P2 primer pair would be weeded out from consideration by process 400 as the threshold of the P1/P2 primer pair and with other P3/P4 primer pairs would have a penalty greater than the threshold.
At block 410, process outputs the ranked list of six primer solutions. In one embodiment, process 400 returns the ranked list to the computations module that invoked process 400. In one embodiment, the ranked list is displayed in a user interface.
Process 500 begins by receiving the BOW input parameters. In one embodiment, process 500 receives inputs for input primer range, melting temperature range, GC Range, and GC Clamp, etc. At block 504, process 500 generates the primers for the input range and other parameters. In one embodiment, process 500 generates the primers by grabbing sequences from the DNA template within the length and position restraints and adds all possible additions (such as the enzyme sequence). Generating the primers is further described in
At block 506, process 500 checks the results of the primer generation. If there are too many generated primers, process 500 tightens the parameters at block 510. Execution proceeds to block 504 above with the tightened parameters. If there are too few generated primers at block 506, process 500 loosens the parameters at block 512. Execution proceeds to block 504 above with the loosened parameters.
In one embodiment, loosening and tightening of the input parameters are accomplished by increasing or decreasing various parameter ranges. For example and in one embodiment, process 500 can loosen or tighten melting temperature range, GC Range, GC Clamp, AG dissimilarity parameter, etc. to allow a minimum number of results or limit an exceptional number of valid primer results. For example and in one embodiment, process 500 starts with the parameters as specified by a scientist. If a satisfactory number of results are not obtained process 500 adjusts parameters accordingly. In one embodiment, process 500 initially adjusts the GC range, by expanding or loosening the range by 1% for each loosening/tightening round. For example and in one embodiment, process 500 can adjust the parameters for 6 rounds (the number is adjustable) before resetting back to normal and attempting to adjust the temperature range.
In another embodiment, process 500 can similarly adjusts the temperature range by 1 degree Celsius for six rounds. The adjustment value and number of rounds are also adjustable. Primer length, GC clamp, and other options can be adjusted in similar ways until a desired number of results is achieved.
Once a desired number of results has been achieved, at block 508, process 500 calculates annealing penalties for each primer through the Annealing Penalizer. These primer results are sorted by penalty and returned at block 514. In one embodiment, an annealing penalty is a measure of the desirability of an individual primer. In one embodiment, an annealing penalty is calculated by comparing the melting temperature or ΔG of a primer's secondary structure to the melting temperature or ΔG of the desired primer-to-template binding. The smaller the difference between these two values, the larger the penalty. Conversely, the larger the difference between these two values, the smaller the penalty.
In one embodiment, the annealing penalizer is a process that simulates the annealing of a primer to itself or to another primer. Annealing is the strength at which a sequence of nucleotides binds to another sequence of nucleotides. The higher the annealing between two primers, the less desirable these primers are for an experiment.
In one embodiment, the input to the annealing penalizer is the template DNA and primer and the annealing penalizer outputs a percentage of wanted binding/ratio of wanted to unwanted binding. In one embodiment, a primer binds to the template at the desired location with a given ΔG. Process 500 determines the AG using one of the ways as known in the art (e.g., nearest neighbor thermodynamics, predicted secondary structure, etc.).
In one embodiment, process 500 uses a heuristic to determine where along the template DNA significant parts of the primer might bind. In this embodiment, both the sense and anti-sense strands are checked. In another embodiment, one of the sense and anti-sense strands area checked. For example and in one embodiment, process 500 checks the template using a BLAST, Smith-Waterman, secondary structure prediction, or other known algorithm known in the art for determining binding affinity. In another embodiment, other algorithms as known in the art are used to determine binding affinity. In one embodiment, process 500 saves primer areas that have a similarity amount above a threshold.
For the primer areas that have a close enough similarity, along with flanking nucleotides on the template to ensure the segment is longer than the primer, process 500 checks these primer areas against the primer for pair binding (heterodimer), and a ΔG is calculated based on these methods. For example and in one embodiment, process 500 uses an algorithm as known in the art to determine ΔG for pair binding (e.g., secondary prediction algorithm that is based on nearest neighbor thermodynamics). If the pair ends up with a positive or 0 ΔG, it is discarded. If the ΔG is negative, process 500 records the ΔG and saves this primer for later.
Given a set of primer to template segment bindings and their associated ΔGs, process 500 determines the percentage of wanted binding and unwanted binding at the annealing temperature. in one embodiment, process 500 does this by assuming the thermodynamic product of reaction from single strands to double strands at a given temperature. This gives a ratio of wanted to unwanted binding as well. In addition, process 500 can give an overall secondary binding score to each primer, which is already normalized. Process 500 further compares the different primers based on this secondary binding score, and process 500 can weigh the importance of this parameter relative to the other primer parameters however the experimenter wishes (this is done later, outside of the secondary binding determination).
In one embodiment, process 500 includes the ability to distinguish whether a single strong secondary binding is solely responsible for removing X % of wanted binding or whether it is many unwanted weak secondary bindings. In this embodiment, this can lead to a better secondary binding score, because strong unwanted binding can be worse than many weak unwanted bindings. In another embodiment, process 500 determines if the unwanted bindings are close to a reverse primer (wanted or unwanted), thus creating small amplicons (amplified/copied regions of DNA during PCR) that will compete heavily with the desired region of amplification during the exponential copying stages of PCR. In a further embodiment, process 500 determines the sizes of any unwanted amplicons to see if these amplicons differ enough from the desired region to be separated during gel electrophoresis (this would only be desired if the scientist absolutely could not use any other primers and was stuck with some really bad secondary binding).
For example and in one embodiment, the types of intra-primer, inter-primer, and primer-template interactions that can occur are:
In one embodiment, the experimental design aims to develop sets of primers and primers-enzymes that reduce the negative impact of hairpins, homodimers, heterodimers, and secondary binding. In this embodiment, by reducing the negative impact of the above secondary structures, the desired binding is increased, which is a scientist's goal.
Many annealing penalizers models known in the art are based on the ΔG of the folded sequence. While in one embodiment, secondary structure prediction is a technique known in the art for determining a sequence's ΔG, in alternate embodiments different techniques can be used (tertiary structure prediction, heuristics, etc.).
Some example techniques that can be employed are:
Heuristic-based models: These techniques tend to rely on rules of thumb, instead of numerically calculating a ΔG. For example and in one embodiment, a process can penalize sequence(s) that are above or below a specific GC % range, melting temperature range, looking for a GC clamp at the end of a sequence, not allowing more than N-binds in a row when using a sliding window approach, etc.
Other heuristic models can use temperature heuristics. Some of the most basic temperature penalizing examples are the Wallace rule (Td=2° C.(A+T)+4° C.(G+C)) and the Howley formula (Tm=81.5+16.6 log M+41(XG+XC)−500/L−0.62F). Melting temperatures that are close to the desired binding melting temperature are penalized more than those that are far away from the desired binding melting temperature.
Another technique is base pair maximization. In this technique, the base pair binding of the desired binding is compared to the base pair binding of the undesired bindings and penalized in a similar fashion to temperature.
In alternative embodiment, a combination of heuristic models can be used. For example and in one embodiment, a combination of a temperature penalty, a GC penalty, an individual bind-penalty, and a penalty for stems that were longer than a fixed length can be employed.
Minimum Free Energy Models: A minimum free energy model is another technique known in the art. In these techniques, the closer the ΔG to the desired binding ΔG, the higher the penalty. An alternative minimum free energy model is to use an equilibrium partition function to predict the structure with the minimum free energy. There are several variations of this approach as known in the art. A further approach is to use minimum free energy model and existing sequence alignments (homology-based-prediction) to aid in a minimum free energy determination.
Maximum Expected Accuracy (MEA): MEA-based approaches are driven by statistical learning on a given data set as opposed to thermodynamic or probabilistic models. Some various approaches with references follow. These techniques are used to describe the secondary structure and the ΔG of this structure is computed and used as a penalty. Other MEA-based approaches that could be employed are: Stochastic Context-Free Grammar (SCFGs), Conditional Log-Linear Models (CLLMs).
Machine Learning Models: As known in the art, machine learning approaches rely on data set training that can give accurate results, which depend on the training set to train the model. Two techniques that have been used in secondary structure prediction are support vector machines and neural networks: Support Vector Machines and Neural Networks.
Another type of secondary structure is the bulge 550B. In one embodiment, the bulge 550B results from one or more nucleotides in a primer not binding to a nucleotide in the other primer. For example and in one embodiment, in bulge 550B, each nucleotide in the top primer sequence binds to a corresponding nucleotide in the bottom primer sequence, except for the C nucleotide that constitutes the bulge 554. In this embodiment, this bulge 554 results because the cytosine (C) nucleotide does not bind to the thymine (T) nucleotides that are opposite from the C nucleotide in the bulge 554. While in this embodiment, the bulge 554 include one nucleotide, in alternate embodiments, the bulge 554 can be more than one nucleotide and/or include the same or different types of nucleotides.
The next two secondary structures are interior loops formed by a single primer sequence or multiple primer sequences binding to each other, which are the symmetric interior loop 550C and the asymmetric interior loop 550D. The symmetric interior loop 550C is formed from two primer sequences, where a loop of equal number of nucleotides in each primer sequence do not bind to each other. In one embodiment, the symmetric interior loop 550C includes a loop 556A-B, where each of the top 556A and bottom 556B segments of the loop each includes four nucleotides. In this embodiment, this loop results from the nucleotides in the top segment 556A and the nucleotides in the bottom segment 556B not binding to each other. For example and in one embodiment, the TCAA segment 556A does not bind to the TAAA 556B segment because T does not bind to another T, C does not bind to A, A does not bind to itself A. While in one embodiment, the top and bottom segments 556A-B of the loop include four nucleotides, in alternate embodiments, the top and bottom segments can have greater or lesser number of nucleotides and/or include the same or different types of nucleotides.
The asymmetric interior loop 550D is similar to the symmetric interior loop 550C except that the top and bottom segments 558A-B of the loop each have different numbers of the nucleotides. For example and in one embodiment, the TCAA segment 558A does not bind to the TA 558B segment because T does not bind to another T or A, C does not bind to T or A, A does not bind to itself (A). While in one embodiment, the top and bottom segments 558A-B of the loop include four nucleotides, in alternate embodiments, the top and bottom segments can have greater or lesser number of nucleotides and/or include the same or different types of nucleotides.
The multi-branch loop 550E is a primer sequence where there are multiple branches 560A-C that branch off a loop 562 of nucleotides. In one embodiment, the multi-branch loop 550E can be a single primer sequence or two or more primer sequences. As with the loops 550C-D described above, the loop 562 results from nucleotides that do not bind to a neighboring nucleotide. For example and in one embodiment, the loop 562 includes segments TTG, ATTTTAT, and GCT that do not bind to a neighboring nucleotide.
In addition, the multi-branch loop 550E, includes the primer sequences that bind to each other form the branches 560A-C. For example and in one embodiment, branch 560A includes ten base pairs, branch 560B three base pairs, branch 560C includes four base pairs. Furthermore, multi-branch loop 550E can include bulges 564A-B where the primer sequence folds back onto itself. For example and in one embodiment, branch 564A include an AAA bulge 564A that allows the formation of branch 560C. In addition, in this embodiment, branch 560B include another AAA bulge 564B that allows the formation of branch 560B. While in one embodiment, the multi-branch loop 550E include three branches 560A-C, a loop 562, and two bulges 564A-B, in alternate embodiments, the multi-branch loop 550E can includes a greater or lesser number of branches, loops, and/or bulges.
At block 604, process 600 determines if the fixed point generator is requested. In one embodiment, an input parameter specifies whether the primer generator is working on a fixed or floating primer range. If working on a fixed primer range, process 600 generates primers at block 606 with a fixed point generator. In one embodiment, process 600 appends the sequence range to the enzyme and “walks” this range. Process 600 generates primer statistics for the generated primer at block 608. Execution proceeds to block 614.
If the floating point generator was requested, process 600 generates primer using the floating point generator. In one embodiment, process 600 walks the supplied floating primer range. In this embodiment, walking a primer consists of generating statistics and passing these statistics to the primer filter at block 612. In one embodiment, the statistics are generated by “walking” a minimum and maximum length primer across an entire sequence range. For example and in one embodiment, the CG content is computed by adding the number of Cs and Gs in the primer segment and dividing this number by the total number of nucleotides (As, Cs, Ts, and Gs). The computed number is converted into a percentage by multiplying by 100.
In another example and another embodiment, process 600 computes a melting temperature using one or more ways to calculate a melting temperature as known in the art. As is known in the art, there are many variations on how to calculate melting temperature. These variations differ due to updated data sets from empirical determination of parameters, or from describing a different approach to the problem. For example and in one embodiment, process 600 calculates a melting temperature of a primer using a Nearest Neighbor Thermodynamics approach that uses the thermodynamic tables. In this embodiment, the basic equation (2) for determining melting temperature in Celsius is:
dH*1000/(dS+R*ln(Ct/x))−273.15+[salt correction] (2)
where dH is the sum of nearest neighbor enthalpy parameters, dS is the sum of nearest neighbor entropy parameters, R is the molar gas constant, Ct is the molar concentration of DNA, and x is a parameter whose value depends on the palindromic quality of the primer. There are also corrections for salt concentrations in the mixtures.
For example and in one embodiment, if process 600 is to walk a sequence of 1000 nucleotides across a primer length range of 25-35 nucleotides, process 600 will start at the first nucleotide, take the first 25 nucleotides, compute primer statistics, and pass this information to the primer filter. Process 600 increases the primer length by 1 and repeats this process. Once process 600 has reached the maximum length primer, process 600 will advance the primer start to the second nucleotide and repeat this minimum/maximum primer length process. In this embodiment, process 600 will continue to advance the start of the primer until it reaches the smallest possible primer at the end of the floating primer range. In this case, since the minimum primer length is 25, the last primer start would be at the 975th nucleotide in the range and the last primer end would be at the 1000th nucleotide. Process 600 thus walks a primer length range across a sequence.
At block 614, process 600 filters the generated primer. In one embodiment, process 600 decides to accept or reject the primer based on the primer statistics generated by the primer generator and the primer options specified by a scientist. Primer filtering is further described in
At block 702, process 700 receives the wildcard enzyme optimization input. In one embodiment, process 700 receives the list of primer regions, gene of interest (GOI), P1R start and end, P6 start and end, P2/P3 and P4/P5 enzymes (where one or more of the enzymes include wildcard codes), target sequence, construct sequence, primary criteria, primer heuristics, primer quality parameters, southern options, etc.). For example and in one embodiment, process 700 receives the wildcard enzyme such as the Sfi I enzyme that includes ambiguity codes for one of the input primer criteria along with the other primer inputs.
Process 700 computes the different primers for primers P1-P6 at blocks 704A-F. At blocks 704A, F, process 700 build floating primers. In one embodiment, a floating primer is a primer that is not tied down to a particular location in the DNA of interest 120. For example and in one embodiment, primers P1 and P6 are floating primers, such as primers P1132A and P2132F as illustrated in
At blocks 704B-E, process 700 computes primer candidates for the degenerate primers. In one embodiment, the degenerate primers are the primers that are either attached or close in proximity to the target or insert as described above with reference to
In one embodiment, process 700 computes the primer candidates P2-P5 with a wildcard enzyme by eliminating invalid wildcard prefixes and suffixes as described further in
At block 706, process 700 receives the primer candidates for P2-P5 (714A-C) and performs a degenerate quad optimization. In one embodiment, process 700 performs the degenerate optimization to generate one or more P2-P3-P4-P5 solutions. In this embodiment, because the degenerate primers P2-P5 are fixed, there relatively few possible solutions for each of the degenerate primers, because the primer ranges on the sense or anti-sense strand are relatively fixed. In one embodiment, one end of the degenerate primers P2-P5 is fixed at the boundary of either the target or insert. For example and in one embodiment, and with Reference to
In one embodiment, process 700 determines the degenerate quad solution using a dynamic programming techniques to optimize pairs of the degenerate primers and successively build larger sets of primers. In one embodiment, dynamic programming is a method of solving complex problems by breaking them down into simpler subproblems and combing the results of these subproblems into an overall solution. Dynamic programing makes it possible to solve exponentially complex problems in a realistic amount of time.
In one embodiment, process 700 optimizes a P4/P5 pair of primers. Using these optimized P4/P5 primer pairs, process 700 optimizes a set of P3/P4/P5 solutions using results of the P4/P5 optimization. In this embodiment, by using the optimized sets of P4/P5 primers, process 700 has reduced the number of computations needed to arrive at the P3/P4/P5 solutions. Furthermore, process 700 uses the results of the P3/P4/P5 optimization to optimize a set of P2/P3/P4/P5 solutions. Similar to the P3/P4/P5 optimization, by using the optimized P3/P4/P5 set of solutions, process 700 reduces the number of computations to optimize the set of P2/P3/P4/P5 solutions. In this embodiment, the set of P2/P3/P4/P5 solutions is the degenerate quad solution. Optimizing the degenerate quad solution is further described in
In the embodiment, described above, the degenerate quad solution was computed by solutions in the order P4/P5 ->P3/P4/P5 ->P2/P3/P4/P5. In other embodiments, the order in arriving at the quad degenerate solutions can be different. In another embodiment, process 700 starts the optimization process by optimizing the P2/P3 set of solutions and using this set of solutions to optimize a P2/P3/P4 set of solutions, and using the P2/P3/P4 set of solutions to optimize a P2/P3/P4/P5 set of solutions. In an alternate embodiment, process 700 initially optimizes a P3/P4 set of solutions and uses this optimized set of solutions to optimize either a P2/P3/P4 or P3/P4/P5 sets of solutions. In this embodiment, process 700 can use either P2/P3/P4 or P3/P4/P5 sets of solutions to optimize the degenerate quad set of solutions, P2/P3/P4/P5.
In one embodiment, the possible primers that can be used to calculate are primer sets 804A-F (P1 floating primers 804A, P2 degenerate primers 804B, P3 degenerate primers 804C, P4 degenerate primers 804D, P5 degenerate primers 804E, and P6 floating primers 804F). In one embodiment, because the floating primers can be calculated from a large range on the sense (P1) or anti-sense strand (P6), there can be a relatively large number of possible floating primers. On the other hand because, in one embodiment, one of end of the degenerate primers is relatively fixed, the possible number of degenerate primers for each of P2-P5 is relatively small.
In one embodiment, the approach 800 computes a quad degenerate solution set 808. In this embodiment, the approach 800 is to calculate one or more paths from P2->P3->P4->P5 to arrive at the quad degenerate primer solution set. Using this quad primer solution set 808, the approach calculates paths from P5->P6 and P1->P2 to arrive at a six primer solution set(s).
For example and in one embodiment, the approach 800 calculates different paths 806A-E using primers 802A-F. The approach 800 calculates an optimal P4->P5 path 806D using primers P4802D and P5802E and uses this path to calculate an optimal P3->P4->P5 path 806C with primer P3802C. The approach uses path 806C and primer 802D to calculate the quad degenerate primer solution 808, which is path P2->P3->P4->P5806B. Using the quad degenerate primer solution 808, the approach 800 calculates the paths that include the optimal floating primers P1802A for path P1->P2->P3->P4->P5806A and P6802F for path P1->P2->P3->P4->P5->P6806F. This dynamic programming approach is further described in FIGS. 7 and 9-12A.
In addition, approach 800 determines that the optimal primers for primer P4B is PSC and the optimal primer for P4C is PSA. In this embodiment, the approach determines a set of paths 858A-C between the primers in the P4 primer candidate set 850C and the P5 primer candidate set P5850D. This set of paths is P4A-P5B 858A, P4B-P5C 858B, and P4C-P5A 858C. While in this embodiment, the approach has determined a set of three paths between primer candidate sets that each have three primer candidates, in alternate embodiments, the number of paths that are determined and the number of primers in each or both primer candidate sets can be the same, greater or smaller. In one embodiment, the determining of paths between two sets of primer candidate sets for P4 and P5 creates the P4/P5 degenerate mesh as described in
Furthermore, this approach 800 builds a larger degenerate mesh from a smaller degenerate mesh by determining new primer paths between a primer candidate set and an input degenerate mesh. In one embodiment, approach 800 determines a path between one of the primers in the primer candidate set and one of the primer paths in the input degenerate mesh. For example and in one embodiment, approach 800 determines paths between the P3 primer candidate set 850B and the paths 858A-C of the P4/P5 degenerate mesh. In this embodiment, the approach determines that the optimal primer for P3A is P4A, resulting in the primer path P3A-P4A-P5B 856A. Similarly, approach determines the paths P3B-P4C-P5A 856B and P3C-P4A-P5A 856C. In addition, the paths 856A-C determined by approach 800 is the P3/P4 degenerate mesh as described in
In addition, the approach 800 determines the four primer paths 854A-D for the P2/P3 degenerate mesh. In one embodiment, the P2/P3 degenerate mesh includes paths P2A-P3C-P4A-P5B 854A, P2B-P3A-P4A-P5B 854B, and P2C-P3C-P4A-P5B 854B 854C. In one embodiment, this set of four primer paths is the quad degenerate solution that is computed in
Returning to
At block 710, process 700 optimizes the P6 primer for P5 primer using the degenerate quad solution as optimized in block 706. In one embodiment, process 700 optimizes a primer pair using a breadth-first search process as described above in
At block 712, process 700 determines if each or the one or more six primer solutions produced at block 710 above meets the criteria input by the scientist. In one embodiment, the input criteria is melting temperature, GC percentage, primer length, etc. and/or other criteria input by the scientist. For each six primer solution that meets the input criteria, at block 714, process outputs the six primer solution(s) that meet the input criteria. For each six primer solution that does not meet the input criteria, at block 716, process outputs the six primer solution(s) that do not meet the input criteria.
At block 906, process 900 builds a degenerate mesh for P3/P4 using the results of the P4/P5 degenerate mesh from blocks 904 above. In this embodiment, the degenerate mesh is a mesh of solutions for primers P3 and the P4/P5 degenerate mesh. For example and in one embodiment, the P3/P4 degenerate mesh is the best path from P3→P4→P5. In another embodiments, the degenerate mesh is one or more suitable paths from P3→P4→P5. Building the degenerate mesh for P3/P4 is further described in
At block 908, process 900 builds a degenerate mesh for P2/P3 using the results of the P3/P4 degenerate mesh from blocks 906 above. In this embodiment, the degenerate mesh is a mesh of solutions for primers P2 and the P3/P4 degenerate mesh. For example and in one embodiment, the P2/P3 degenerate mesh is the best path from P2→P3→P4→P5. In another embodiments, the degenerate mesh is one or more suitable paths from P2→P3→P4→P5. Building the degenerate mesh for P2/P3 is further described in
While in the embodiment illustrated above, the degenerate mesh for the P2/P3/P4/P5 primers was calculated starting from a P4/P5 degenerate mesh and a P3/P4 degenerate mesh, in alternate embodiments, the P2/P3/P4/P5 degenerate mesh can be calculated in different ways. For example and in one embodiment, the P2/P3/P4/P5 degenerate mesh can be calculated starting with a P2/P3 degenerate mesh and a P3/P4 degenerate mesh. In another embodiment, the P2/P3/P4/P5 degenerate mesh can be calculated starting with a P3/P4 degenerate mesh and either a P2/P3 or P4/P5 degenerate mesh.
In another embodiment, process 1000 receives a set of primer solutions and another degenerate mesh. In this embodiment, process 1000 uses the set of primer solutions to extend the inputted degenerate mesh. For example and in one embodiment, process 1000 receives a set of primer solution for the P3 primer and the P4/P5 degenerate mesh as described above in
Process 1000 executes a processing loop (blocks 1004-1016) to find optimal layer 2 primers for each layer 1 primer. In one embodiment, the layer 1 primers are from the first primer solution set and the layer 2 primers are from the second primer solution set or the degenerate mesh that were received at block 1002 above. For example and in one embodiment, the layer 1 primers are from the P4 primer solution set and the layer 2 primers are from the P5 primer solution set. In another embodiment, the layer 1 primers are from the P3 primer solution set and the layer 2 primers are from the P4/P5 degenerate mesh. In a further embodiment, the layer 1 primers are from the P2 primer solution set and the layer 2 primers are from the P3/P4 degenerate mesh. Furthermore, as described above, the layer 1 and 2 primer can be from alternative combinations of primer solution sets and degenerate meshes.
For each primer in the layer 1 primers, process 1000 find compatible layer 2 primers for that layer 1 primer. In one embodiment, process 1000 finds compatible layer 2 primers for the particular layer 1 primer by producing a list of the optimal primer-pair combinations. A primer pair is considered optimal if each of the primers has a low annealing penalty (e.g., using the annealing penalizer described below in
In one embodiment, process 1000 finds compatible layer 1 and 2 primers by computing a penalty between prospective primers. As is known in the art, there are many different ways known to compute a penalty between possible primers. In one embodiment, process 1000 computes a penalty based on the positive or negative interactions that can occur between the possible primer pairs. For example and in one embodiment, process 1000 computes an inter-primer pair penalty using the annealing penalizer as described above with to
In one embodiment, process 1000 computes a primer pair penalty for primer pair consisting of primer i and j using Equation (3):
P
ij
=P
i
+P
j
+a
inter
P(inter)ij (3)
where Pi and Pj are the penalties for primers i and j, respectively, P(inter)ij is the inter-primer penalty calculated between primers i and j, and ainter is the weight for the inter-penalty penalty. Inter i
At block 1008, process 1008 find an optimal layer 1 and layer 2 primer pair. In one embodiment, process 1000 performs this step for certain primer pair combinations. For example and in one embodiment, process 1000 finds the optimal layer 1 and layer 2 primers when the layer 1 and 2 primers are the P3 and P4 primers. In this embodiment, P3 and P4 primer would be in the same test tube, so process 1000 penalizes the P3/P4 if there is possible secondary structure formation that could occur. For example and in one embodiment, process 1000 penalizes layer 1 and 2 primers if these primer could form a bulge 550B, symmetric interior loop 550C, asymmetric interior loop 550C, and/or multi-branch loop 550E as described above with reference to
At block 1010, process 1000 computes the total penalty from block 1006 and, if present, block 1008 for the layer 1 and 2 primers. Process 1000 further determines if the computed penalty from block 1010 is smaller than a previous best penalty. In one embodiment, the best penalty is a smallest penalty determined. In this embodiment, the result of process 1000 is the best match layer 2 primer for the input layer 1 primer. Furthermore, in this embodiment, if process 1000 determines the computed penalty is greater than the best penalty, process 1000 updates the best penalty at block 1014. In another embodiment, the best penalty is a penalty that is smaller than a threshold penalty. In this embodiment, if the computed penalty is greater than the best penalty, process 1000 adds the primer pair to a list of potential primer pairs. Process 1000 ends the processing loop at block 1016.
In
Process 1100 builds the primers at block 1104. In one embodiment, process 1100 builds the primers using a fixed point generator best of the worst approach as described in FIG. 6, block 606 above. At block 1104, process 1100 adds the enzyme to the primer. In one embodiment, process 1100 adds the enzyme to the primer by eliminating invalid wildcard suffixes for each wildcard replacement. Adding the enzyme to the primer is further described in
Process 1100 filters the primer at block 1106. In one embodiment, process 1100 decides to accept or reject the primer based on the primer statistics generated by the primer generator and the primer options specified by a scientist. Primer filtering is further described in
In block 1104, process 1100 added the enzyme that includes ambiguity codes to the degenerate primer.
In one embodiment, the wild card enzyme includes a number of labels that could represent many different nucleotides.
Process 1200 executes a processing loop (blocks 1204-1218) to calculate an appropriate enzyme for the input primer for each wildcard nucleotide position in the enzyme. For example and in one embodiment, process 1200 would loop over wildcard range 1234, which includes five different positions corresponding to the “NNNNN.” At block 1206, process 1200 adds the smallest suffix to the enzyme. In one embodiment, the nucleotide with the smallest suffix is the nucleotide that will give the smallest contribution to the desired input parameter, such as melting temperature, GC content percentage, etc. For example and in one embodiment, process 1200 adds the largest suffix for the first N position in the wildcard range 1234 and would add a nucleotide that is A or T as this would decrease the GC percentage. In another example, process 1200 would add an GC pair that would increase the melting point temperature.
At block 1208, process 1200 determines if the nucleotide added above is greater than the largest parameter value. For example and in one embodiment, if the added A or T gives the enzyme+primer a GC content that is over the desired percentage, process 1200 would reject this enzyme+primer combination at block 1210. However, if the added nucleotide is below the desired largest parameter value, process 1200 proceeds to block 1212.
Process 1200 adds the suffix with the largest parameter value to the enzyme at block 1212. In one embodiment, the nucleotide with the largest suffix is the nucleotide that will give the smallest contribution to the desired input parameter, such as melting temperature, GC content percentage, etc. For example and in one embodiment, process 1200 adds the largest suffix for the first N position in the wildcard range 1234 and would add a nucleotide that is G or C as this would increase the GC percentage. In another example, process 1200 would add a AT pair would decrease the melting point temperature.
At block 1214, process determines if the nucleotide added above is smaller than the smallest parameter value. For example and in one embodiment, if the added G or C gives the enzyme+primer a GC content that is over the desired percentage, process 1200 would reject this enzyme+primer combination at block 1210. However, if the added nucleotide is above the desired smallest parameter value, process 1200 proceeds to block 1216, where process 1200 adds the enzyme to the return list. The loop ends at block 1218. Process 1200 returns that enzyme list at block 1218.
Process 1250 begins by receiving the primer filter input parameters at block 1252. In one embodiment, process 1250 receives the prospective primer and validity parameters that are used to compare to the prospective primer. For example and in one embodiment, the validity parameters are length criteria, enzyme length criteria, GC content, GC clamp, etc.
At block 1254, process 1250 checks the length of the primer to determine if the primer is within the primer length criteria. In one embodiment, a primer is discarded if it does not meet a length criterion. For example and in one embodiment, is the primer length range is between 25 and 35 base pairs and a primer has a length of less than 25 base pair or greater than 35 base pairs, process 1250 would reject this primer. If the primer is within the length criterion, process 1250 proceeds to block 1256. If not, process proceeds to block 1266 and returns an invalid status.
At block 1256, process 1250 checks if the primer is within an enzyme length criterion. In one embodiment, a floating primer will not have an enzyme but a fixed primer will have an enzyme as part of the sequence. In one embodiment, a fixed primer is discarded if the enzyme length is greater than half of the primer length. In a further embodiment, a floating primer may have an enzyme and process 1250 will check the enzyme length as per above. In another embodiment, process 1250 does not check floating primers against this criterion. If the primer is within the enzyme length criterion or the enzyme length criterion does not apply, process 1250 proceeds to block 1258. If not, process 1250 proceeds to block 1266 and returns an invalid status.
At block 1258, process 1250 checks if the primer fails to meet GC content criteria. In one embodiment, GC content is a percentage measure of the number of G or C nucleotides in a sequence. If the primer is within the GC content criteria, process 1250 proceeds to block 1260. If not, process 1250 proceeds to block 1266 and returns an invalid status.
At block 1260, process checks if the primer has a valid GC clamp criteria. In one embodiment, GC Clamp is a heuristic that specifies that the last N nucleotides of a primer must be a G or C nucleotide. If the primer is within the GC clamp criteria, process 1250 proceeds to block 1262. If not, process 1250 proceeds to block 1266 and returns an invalid status.
At block 1262, process 1250 determines is the melting temperature of the primer is within the input range. In one embodiment, process 1250 allows melting temperature values that are within a half degree Celsius of the range are accepted while values outside of this range are rejected. In one embodiment, the melting temperature of a primer is calculated based on the number of hydrogen bonds that can be formed in the primer. For example and in one embodiment, a C-G pair can form three hydrogen bonds and an A-T pair can form two hydrogen bonds. If the primer is within the melting temperature range, process 1250 proceeds to block 1264 and returns a valid status. If not, process 1250 proceeds to block 1266 and returns an invalid status.
As describe above, the processes illustrated in
In one embodiment, option panel 1304 is a series of panels that are used to input different input parameters for the experiment module (e.g. experiment module 158 of
In one embodiment, the target sequence panel 1306 is used to input the target DNA strand. While in one embodiment, the target DNA strand is a sequence of IUPAC nucleotide letter codes, in alternate embodiments, the target DNA strand is designated in other ways as known in the art (e.g., NCBI RefSeq, Fasta format, Entrez Gene ID, GenBank ID, or other known gene notations in the art). For example and in one embodiment, the target DNA strand is the strand that is to be modified. In one embodiment, the construct sequence panel 1308 is used to input the construct DNA sequence. For example and in one embodiment, the construct sequence is the sequence that is to be produced.
As described above, the options panel 1304 of
The solutions panel 1806 includes a set of primers that are a solution for the inputted data. For example and in one embodiment, the solutions panel 1806 includes proposed primers for P1-P6, where for each proposed primer a nucleotide sequence, range where the primer binds to the template, melting temperature, and GC percentage is displayed. In addition, the solutions panel 1806 includes a plot of an overview of primer stats relative to other primers in the pool. Furthermore, the solutions panel 1806 includes a slider that can be used to display different primer solution sets. For example and in one embodiment, the slider can be set to display the best primer solution set, the worst solution set, or one of the solution sets in between.
In one embodiment, the primary weights panel 1808 includes a set of sliders that can be used to set the weights that are used to calculate the primer solution sets. In one embodiment, changing the weights via the sliders can change the relative ranking of the primer solution sets. In this embodiment, a scientist uses these sliders for what the scientist feels is important to their setup. Furthermore, this would be a learned heuristics that may go away with our more accurate modeling of what is important. For example and in one embodiment, a primer solution set that is the best solution with a small weight for a self-annealing penalty, maybe a worse solution if the penalty for self-annealing is increased. In one embodiment, these weights are used to calculate the primer and inter-primer penalties as described in Equations (1) and (3) above. Changing these weights can change the rank order of the primer solution sets.
Once a scientist has a set of primers that can be used to amplify a region of DNA, a scientist would design a PCR program, which run on a thermocycler to amplify the area of DNA in order to generate the desired material. In one embodiment, the PCR Protocol Generation module 160 takes the primers and template for each reaction needed (e.g., three total in automated experimental design, four if a scientist chooses verification) and designs a PCR Program (set of instructions for a thermocycler) that optimizes the chance of getting good and specific yield for the desired DNA modification. In one embodiment, the resulting PCR program is optimized based on input reaction reagents that the scientist wishes to use. In another embodiment, the PCR Protocol Module 160 chooses the best reagents and desired concentrations (of all known to us or of a set such as the scientist's inventory) for the given primers and template.
At blocks 1904-1910, process 1900 performs a series of cycles 1914. In one embodiment, the cycle is a set of three steps, each step being perform at a specific temperature and time duration for that temperature. The steps are called denaturation (block 1904), annealing (block 1906), and elongation (block 1908). In one embodiment, the denaturation step (block 1904) consists of heating the reaction mixture to a specific temperature that melts the DNA strand. For example and in one embodiment, process 1900 heats the reaction mixture to a temperature of 94° C. and holding at this temperature for a period of minutes. In one embodiment, the annealing step (block 1906), process 1900 reduces the temperature below the melting temperature of the primers and holds this temperature for a period of time. For example and in one embodiment, process 1900 reduces the temperature to 50-65° C. and holding at this temperature for 20-40 seconds.
In one embodiment, the elongation step (block 1908) consists of increasing the temperature to the active temperature of the polymerase to elongate the target DNA strand. For example and in one embodiment, process 1900 increases the temperature to 70-80° C. and holding at this temperature for a period of minutes.
At block 1910, process 1900 determines if the cycle 1914 is to be repeated. In one embodiment, these three steps of a cycle 1914 are repeated a certain number of times. PCR programs can consist of multiple cycles, but a basic PCR program has just one cycle that repeats 25 to 30 times. If there are no further cycles, at block 1916, process 1900 can determine if other cycles using different parameters are to be performed. In one embodiment, process 1900 determines that a touchdown cycle, step down cycle, or other type of cycles are to be performed as known in the art. In this embodiment, a touchdown or step down cycle alters the annealing temperatures during the annealing steps. If there are other cycles to be performed, execution proceeds to block 1904 above with possible different PCR parameters. If there are no further cycles to be performed, at block 1918, process 1900 performs a final elongation. In one embodiment, the final elongation step is longer than the other elongation step performed. At block 1912, process 1900 performs a cool down. In one embodiment, the temperature of the mixture is cooled down to 4° Celsius using one of the ways known in the art.
In one embodiment, the main variable components of the PCR reaction solution are the buffer used, the polymerase (an enzyme that amplifies the DNA) used, the concentrations of the template DNA primer concentrations, salt and cofactor concentrations. Various polymerases have different active temperatures, different half-lives at certain temperatures, PCR Protocol Generation, different error rates, and a few other unique properties. Various buffers work better or worse for mixes of enzymes and can change the concentrations of various salts, which alter the optimal temperature of the annealing steps for the primers. The concentration of template DNA also can alter the optimum annealing temperature for the primers.
For a scientist to use the PCR program, a scientist need the PCR parameters for each of the steps of the PCR program.
At block 2004, process 2000 determines the parameters for the denaturation step. In one embodiment, the denaturation step is generally run at 94° C. and held at this temperature for 30 seconds. In one embodiment, various polymerases have different half-lives at this temperature and the number of effective total cycles can be determined from how long the enzymes spend at this temperature.
At block 2006, process 2000 determines the parameters for the annealing step. In one embodiment, process 2000 determines the annealing step parameters selecting a temperature lower than the lowest melting temperature of the two primers. In one embodiment, the annealing temperature is influenced by the buffers and salt concentrations to be used in the reaction. In one embodiment, process 2000 uses a primer melting temperature that is calculated from the automated experimental design as described in
Process 2000 determines the parameters the elongation step at block 2008. In one embodiment, the elongation step is determined by the active temperature of the polymerase used and the length of the region being amplified along with the effective speed of the polymerase used. For example, if the scientist was using Taq polymerase, the effective speed would be 1 Kb per 60 seconds, so if the region to be amplified (the region between and including the two primers) was 2000 base pairs, the time for the elongation step would be 120 seconds. The active temperature for Taq polymerase is 72 degrees, so that would be the temperature used. Polymerase properties would be stored locally in a database.
In another embodiment, if the polymerase used was a heat-start polymerase, an the optional preliminary step would be used, which would be a temperature heating around 94 degrees for between 1 and 10 minutes, depending on the properties of the polymerase. The final step is always a cool down to 4 degrees to slow any reactions and degradation of the DNA.
At block 1510, process 2000 offer alternatives to the determined parameters. In one embodiment, in addition to the basic PCR design, there are alterations, which could be used to get better yield, reduce unwanted byproducts, or attempt to get a failed PCR to work. In one embodiment, most of these alternations are not determined to be needed until after a PCR experiment has been run and shown to not work as hoped. In this embodiment, a scientist would attempt to alter the program in order to solve existing problems. Many of these problems are due to unwanted secondary binding to the template or other properties of the underlying DNA region. In one embodiment, process 2000 checks this before hand and offer alternative PCR programs to optimize the experiments success the first time. Process 2000 returns the determined and alternative PCR parameters at block 1512. In one embodiment, process 2000 determines different PCR parameters such as a touchdown or step down cycle, change of concentrations, change of buffer, etc. and/or other different parameters as known in the art.
As shown in
The mass storage 3211 is typically a magnetic hard drive or a magnetic optical drive or an optical drive or a DVD RAM or a flash memory or other types of memory systems, which maintain data (e.g. large amounts of data) even after power is removed from the system. Typically, the mass storage 3211 will also be a random access memory although this is not required. While
A display controller and display device 3309 provide a visual user interface for the user; this digital interface may include a graphical user interface which is similar to that shown on a Macintosh computer when running OS X operating system software, or Apple iPhone when running the iOS operating system, etc. The system 3300 also includes one or more wireless transceivers 3303 to communicate with another data processing system, such as the system 3300 of
The data processing system 3300 also includes one or more input devices 3313, which are provided to allow a user to provide input to the system. These input devices may be a keypad or a keyboard or a touch panel or a multi touch panel. The data processing system 3300 also includes an optional input/output device 3315 which may be a connector for a dock. It will be appreciated that one or more buses, not shown, may be used to interconnect the various components as is well known in the art. The data processing system shown in
At least certain embodiments of the inventions may be part of a digital media player, such as a portable music and/or video media player, which may include a media processing system to present the media, a storage device to store the media and may further include a radio frequency (RF) transceiver (e.g., an RF transceiver for a cellular telephone) coupled with an antenna system and the media processing system. In certain embodiments, media stored on a remote storage device may be transmitted to the media player through the RF transceiver. The media may be, for example, one or more of music or other audio, still pictures, or motion pictures.
The portable media player may include a media selection device, such as a click wheel input device on an iPod® or iPod Nano® media player from Apple, Inc. of Cupertino, Calif., a touch screen input device, pushbutton device, movable pointing input device or other input device. The media selection device may be used to select the media stored on the storage device and/or the remote storage device. The portable media player may, in at least certain embodiments, include a display device which is coupled to the media processing system to display titles or other indicators of media being selected through the input device and being presented, either through a speaker or earphone(s), or on the display device, or on both display device and a speaker or earphone(s). Examples of a portable media player are described in published U.S. Pat. No. 7,345,671 and U.S. published patent number 2004/0224638, both of which are incorporated herein by reference.
Portions of what was described above may be implemented with logic circuitry such as a dedicated logic circuit or with a microcontroller or other form of processing core that executes program code instructions. Thus processes taught by the discussion above may be performed with program code such as machine-executable instructions that cause a machine that executes these instructions to perform certain functions. In this context, a “machine” may be a machine that converts intermediate form (or “abstract”) instructions into processor specific instructions (e.g., an abstract execution environment such as a “virtual machine” (e.g., a Java Virtual Machine), an interpreter, a Common Language Runtime, a high-level language virtual machine, etc.), and/or, electronic circuitry disposed on a semiconductor chip (e.g., “logic circuitry” implemented with transistors) designed to execute instructions such as a general-purpose processor and/or a special-purpose processor. Processes taught by the discussion above may also be performed by (in the alternative to a machine or in combination with a machine) electronic circuitry designed to perform the processes (or a portion thereof) without the execution of program code.
The present invention also relates to an apparatus for performing the operations described herein. This apparatus may be specially constructed for the required purpose, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), RAMs, EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus.
A machine readable medium includes any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). For example, a machine readable medium includes read only memory (“ROM”); random access memory (“RAM”); magnetic disk storage media; optical storage media; flash memory devices; etc.
An article of manufacture may be used to store program code. An article of manufacture that stores program code may be embodied as, but is not limited to, one or more memories (e.g., one or more flash memories, random access memories (static, dynamic or other)), optical disks, CD-ROMs, DVD ROMs, EPROMs, EEPROMs, magnetic or optical cards or other type of machine-readable media suitable for storing electronic instructions. Program code may also be downloaded from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a propagation medium (e.g., via a communication link (e.g., a network connection)).
The preceding detailed descriptions are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the tools used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.
It should be kept in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as “determining,” “receiving,” “calculating,” “ranking,” “identifying,” “storing,” “inserting,” “modifying”, or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.
The processes and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the operations described. The required structure for a variety of these systems will be evident from the description below. In addition, the present invention is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the invention as described herein.
The foregoing discussion merely describes some exemplary embodiments of the present invention. One skilled in the art will readily recognize from such discussion, the accompanying drawings and the claims that various modifications can be made without departing from the spirit and scope of the invention.
