DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are currently pending and under examination herein.
Claims 1-20 are rejected.
Priority
The instant application claims priority to US provisional application 63367002 filed 24 June 2022.In this action, claims 1-20 are examined as though they had an effective filing date of 24 June 2022. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 29 February 2024, 25 April 2024, and 19 July 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed 23 June 2023 are objected to as the supplemental file containing the drawings was found to contain color drawings (Figures 11A, 11B, 11C, 11D, 12A, 12B, 12C, 12D).
Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. No petitions are shown to have been filed. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural law without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea or natural law (Step 2A, Prong 1). Claims 1-10 are directed to a method and Claims 11-20 are directed to systems. In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recites the limitations – identifying one or more nucleotide reads corresponding to a genomic region of a genomic sample; determining candidate split groups comprising fragment alignments corresponding to the one or more nucleotide reads; and selecting, for nucleobase calling of the genomic region, a predicted split group from the candidate split groups based on the split group scores. Based on the broadest reasonable interpretation, identifying, determining, and selecting data could practically be done by the human mind. This draws the limitation to a mental process, which classify the limitations as abstract ideas. The claim also recites generating split group scores for split alignments of the candidate split groups with a reference genome. Based on the broadest reasonable interpretation, generating scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 2 recites the limitations - determining a candidate split group of the candidate split groups by: grouping, into the candidate split group, one or more fragment alignments of a single-end nucleotide read; or grouping, into the candidate split group, one or more fragment alignments of a paired-end nucleotide read from a pair of paired-end nucleotide reads. Based on the broadest reasonable interpretation, grouping data could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 3 recites the limitation - generating fragment alignment scores for individual fragment alignments of a candidate split group with the reference genome; and generating a split group score for the candidate split group based on the fragment alignment scores. Based on the broadest reasonable interpretation, generating scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 4 recites the limitation - generating, for a candidate split group of the candidate split groups, a break penalty for relative geometries of a first fragment alignment and a second fragment alignment with respect to the reference genome; and generating a split group score for the candidate split group based on the break penalty. Based on the broadest reasonable interpretation, generating penalties and scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 5 recites the limitation - generate, for a candidate split group of the candidate split groups, an overlap penalty for an overlap within a nucleotide read between a first fragment alignment and a second fragment alignment; and generate a split group score for the candidate split group based on the overlap penalty. Based on the broadest reasonable interpretation, generating penalties and scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 6 recites the limitation - generating a split group score for a candidate split group of the candidate split groups by: generating fragment alignment scores, a break penalty, and an overlap penalty for fragment alignments of the candidate split group; and combining the fragment alignment scores and subtracting the break penalty and the overlap penalty from the combined fragment alignment scores. Based on the broadest reasonable interpretation, generating penalties and scores encompass equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 7 recites the limitation - determining the candidate split groups by iteratively grouping individual fragment alignments following an order of outermost fragment alignments to innermost fragment alignments of a nucleotide read. Based on the broadest reasonable interpretation, determining groups could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. The claim also recites generating the split group scores by iteratively scoring groupings of individual fragment alignments following the order in which the individual fragment alignments were grouped. Based on the broadest reasonable interpretation, generating scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 8 recites the limitation - identifying, from the candidate split groups, candidate pairs of split groups comprising different fragment alignments for mates of a paired-end nucleotide read; and selecting, for each mate of the paired-end nucleotide read, the predicted split group based further on the pair scores. Based on the broadest reasonable interpretation, identifying and selecting groups of data could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. Claim 8 also recites generating, for the candidate pairs of split groups, pair scores evaluating pair alignments of the candidate pairs of split groups with the reference genome. Based on the broadest reasonable interpretation, generating scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 9 recites the limitation - determining sums of split group scores for respective candidate pairs of split groups; generating pairing penalties based on an estimated insert size between innermost fragment alignments of the candidate pairs of split groups; and generating the pair scores for the candidate pairs of split groups based on the sums of split group scores and the pairing penalties. Based on the broadest reasonable interpretation, determining scores and penalties encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 10 recites the limitation - determining an alt-contig fragment alignment score for an inner fragment alignment and an outer fragment alignment corresponding to a nucleotide read with an alternate contiguous sequence within the reference genome; determining a split group score for the inner fragment alignment and the outer fragment alignment with a primary-assembly region of the reference genome; and selecting the alt-contig fragment alignment score as a replacement split group score based on determining that the alt-contig fragment alignment score exceeds the split group score. Based on the broadest reasonable interpretation, determining and comparing scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 11 recites the limitation - identify one or more nucleotide reads corresponding to a genomic region of a genomic sample; determine candidate split groups comprising fragment alignments corresponding to the one or more nucleotide reads; and select, for nucleobase calling of the genomic region, a predicted split group from the candidate split groups based on the split group scores. Based on the broadest reasonable interpretation, identifying, determining, and selecting data could practically be done by the human mind. This draws the limitation to a mental process, which classify the limitations as abstract ideas. The claim also recites generate split group scores for split alignments of the candidate split groups with a reference genome. Based on the broadest reasonable interpretation, generating scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 12 recites the limitation - determine nucleobase calls for the genomic region based on an alignment of the predicted split group with the reference genome. Based on the broadest reasonable interpretation, determining information could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 13 recites the limitation - determine that a fragment alignment score of a fragment alignment fails to satisfy a threshold fragment alignment score. Based on the broadest reasonable interpretation, determining scores encompass equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea. The claim also recites remove the fragment alignment from consideration in forming the candidate split groups. Based on the broadest reasonable interpretation, removing data from consideration could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 14 recites the limitation - determine that an alignment score for a candidate split group fails to satisfy a minimum alignment score; and refrain from reporting a split alignment of the candidate split group based on the alignment score failing to satisfy the minimum alignment score. Based on the broadest reasonable interpretation, comparing scores and information encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 15 recites the limitation - generate a split group score for a candidate split group of the candidate split groups by: generating fragment alignment scores, a break penalty, and an overlap penalty for fragment alignments of the candidate split group; and combining the fragment alignment scores and subtracting the break penalty and the overlap penalty from the combined fragment alignment scores. Based on the broadest reasonable interpretation, generating penalties and scores encompass equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 16 recites the limitation - determine the candidate split groups by iteratively grouping individual fragment alignments following an order of outermost fragment alignments to innermost fragment alignments of a nucleotide read. Based on the broadest reasonable interpretation, determining groups could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea. The claim also recites generate the split group scores by iteratively scoring groupings of individual fragment alignments following the order in which the individual fragment alignments were grouped. Based on the broadest reasonable interpretation, generating scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 17 recites the limitation - identify one or more nucleotide reads corresponding to a genomic region of a genomic sample; determine candidate split groups comprising fragment alignments corresponding to the one or more nucleotide reads; and select, for nucleobase calling of the genomic region, a predicted split group from the candidate split groups based on the split group scores. Based on the broadest reasonable interpretation, identifying, determining, and selecting data could practically be done by the human mind. This draws the limitation to a mental process, which classify the limitations as abstract ideas. The claim also recites generate split group scores for split alignments of the candidate split groups with a reference genome. Based on the broadest reasonable interpretation, generating scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 18 recites the limitation - determine a candidate split group of the candidate split groups by: grouping, into the candidate split group, one or more fragment alignments of a single-end nucleotide read; or grouping, into the candidate split group, one or more fragment alignments of a paired-end nucleotide read from a pair of paired-end nucleotide reads. Based on the broadest reasonable interpretation, grouping data could practically be done by the human mind. This draws the limitation to a mental process, which classifies the limitation as an abstract idea.
Claim 19 recites the limitation - generate fragment alignment scores for individual fragment alignments of a candidate split group with the reference genome; and generate a split group score for the candidate split group based on the fragment alignment scores. Based on the broadest reasonable interpretation, generating scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
Claim 20 recites the limitation - generate, for a candidate split group of the candidate split groups, a break penalty for relative geometries of a first fragment alignment and a second fragment alignment with respect to the reference genome; and generate a split group score for the candidate split group based on the break penalty. Based on the broadest reasonable interpretation, generating penalties and scores encompasses equations and could practically be done by the human mind. This draws the limitation to a mathematical concept and a mental process, which classifies the limitation as an abstract idea.
These limitations recite concepts of identifying, determining, selecting, and grouping information that are so generically recited that they can be practically performed in the human mind as claimed, which falls under the “Mental processes” and “Mathematical concepts” grouping of abstract ideas. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claims 1-20 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). These judicial exceptions are not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology (MPEP § 2106.04(d)(1)). Rather, the claims provide insignificant extra-solution activity (MPEP § 2106.05(g)) and provide mere instructions to apply a judicial exception (MPEP § 2106.05(f)). Specifically, the claims recite the following additional elements:
Claim 1 recites a computer.
Claim 11 recites at least one processor; and a non-transitory computer-readable medium comprising instructions.
Claim 14 recites an alignment file and a variant call file.
Claim 17 recites a non-transitory computer-readable medium comprising instructions and at least one processor.
There are no limitations that indicate that the claimed identifying, determining, selecting, and grouping information require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. There is no indication that these steps are affected by the judicial exception in any way and thus do not integrate the recited judicial exception into a practical application. As such, claims 1-20 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite conventional additional elements that equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment. The claims also recite conventional additional elements that represent insignificant extra-solution activities.
As discussed above, there are no additional limitations to indicate that the claimed determining, predicting, and identifying information and applying algorithms require anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea or natural law eligible. MPEP 2106.05(f) discloses that mere instructions to apply the judicial exception cannot provide an inventive concept to the claims. As specified in MPEP 2106.05(g), extra-solution activities can be understood as incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Insignificant extra-solution activities include mere data gathering, selecting a particular data source or type of data to be manipulated, and displaying information. Additionally, Hintzsche et al. (2016, International Journal of Genomics: 1-16) reviews that computing devices processing sequencing data, including alignment and variant call files, are well understood, routine, and conventional (Page 13, Column 1, Paragraph 5: Fastq2vcf can be used in a single or parallel computing environment on variety of sequencing data; also see Page 2, Figure 1 and Page 3, Figure 2).
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, Claims 1-20 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 11, 13, 14, 17, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Frith and Kawaguchi (2015, Genome Biology, Vol. 16, No. 116: 1-17).
The applicable claims include:
Claim 1. A computer-implemented method comprising: (Claim 1.i) identifying one or more nucleotide reads corresponding to a genomic region of a genomic sample; (Claim 1.ii) determining candidate split groups comprising fragment alignments corresponding to the one or more nucleotide reads; (Claim 1.iii) generating split group scores for split alignments of the candidate split groups with a reference genome; and (Claim 1.iv) selecting, for nucleobase calling of the genomic region, a predicted split group from the candidate split groups based on the split group scores.
Claim 3. The computer-implemented method of claim 1, further comprising: (Claim 3.i) generating fragment alignment scores for individual fragment alignments of a candidate split group with the reference genome; and (Claim 3.ii) generating a split group score for the candidate split group based on the fragment alignment scores.
Claim 11. A system comprising: at least one processor; and a non-transitory computer-readable medium comprising instructions that, when executed by the at least one processor, cause the system to:(Claim 11.i) identify one or more nucleotide reads corresponding to a genomic region of a genomic sample; (Claim 11.ii) determine candidate split groups comprising fragment alignments corresponding to the one or more nucleotide reads; (Claim 11.iii) generate split group scores for split alignments of the candidate split groups with a reference genome; and (Claim 11.iv) select, for nucleobase calling of the genomic region, a predicted split group from the candidate split groups based on the split group scores.
Claim 13. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to: determine that a fragment alignment score of a fragment alignment fails to satisfy a threshold fragment alignment score; and remove the fragment alignment from consideration in forming the candidate split groups.
Claim 14. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to: determine that an alignment score for a candidate split group fails to satisfy a minimum alignment score; and refrain from reporting a split alignment of the candidate split group in an alignment file or a variant call file based on the alignment score failing to satisfy the minimum alignment score.
Claim 17. A non-transitory computer-readable medium comprising instructions that, when executed by at least one processor, cause a computing device to: (Claim 17.i) identify one or more nucleotide reads corresponding to a genomic region of a genomic sample; (Claim 17.ii) determine candidate split groups comprising fragment alignments corresponding to the one or more nucleotide reads; (Claim 17.iii) generate split group scores for split alignments of the candidate split groups with a reference genome; and (Claim 17.iv) select, for nucleobase calling of the genomic region, a predicted split group from the candidate split groups based on the split group scores.
Claim 19. The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the at least one processor, cause the computing device to: (Claim 19.i) generate fragment alignment scores for individual fragment alignments of a candidate split group with the reference genome; and (Claim 19.ii) generate a split group score for the candidate split group based on the fragment alignment scores.
Regarding Claims 1, 11 and 17, Frith and Kawaguchi teach (Claim 1.i) identifying nucleotide reads corresponding to a genomic region of a genomic sample (Page 12, Column 1, Paragraph 3: The input is a set of local alignments between one query sequence and one genome). The set of input alignments is interpreted as a sample. Frith and Kawaguchi also teach (Claim 1.ii) determining candidate split groups comprising fragment alignments (Page 4, Column 2, Paragraph 3: Find local alignments between the two genomes, by seed-and-extend (many-to-many). Apply the repeated matches algorithm, constrained to the candidate alignments found in step 1. We refer to this constrained version of the repeated matches algorithm as “split-alignment”. Split-alignment guarantees to find a set of many-to-one alignments). Frith and Kawaguchi also teach (Claim 1.iii) generating split group scores for split alignments of the candidate split groups (Page 4, Column 2, Paragraph 3: Split-alignment guarantees to find a set of many-to-one alignments that maximizes the sum of (alignment score − f ), where each alignment in the set is part (or all) of a candidate alignment; Page 4, Column 1, Paragraph 4: The idea is to seek a set of one-to-one alignments between two genomes that maximizes Equation 1 (i.e. equation 1 generates the score for the set)). Frith and Kawaguchi also teach (Claim 1.iv) selecting, for nucleobase calling of the genomic region, a predicted split group based on the split group scores (Page 4, Column 1, Paragraph 4: The idea is to seek a set of one-to-one alignments between two genomes that maximizes Equation 1). The alignment that is maximized by the equation as the one to one becomes the final (i.e. the selected) alignment. The final alignment selected represents a specific sequence (i.e. it is for base calling). Additionally, Frith and Kawaguchi teach the methods are performed by a computer, which inherently contains memory, including non-transitory, and at least one processor (Page 12, Column 1, Paragraph 2: The software is available, and also in the last-align package). Claims 11 and 17 recite the limitations of claim 1 directed to systems.
Regarding Claims 3 and 19, Frith and Kawaguchi teach (Claim 3.i) generating alignment scores for individual alignments of a candidate split group with the reference genome (Page 4, Column 1, Paragraph 3: Equation 1 shows an alignment score; Page 4, Column 2, Paragraph 3: Find local alignments between the two genomes, by seed-and-extend (many-to-many)). Frith and Kawaguchi also teach (Claim 3.ii) generating a split group score for the candidate split group based on the fragment alignment scores (Page 4, Column 1, Paragraph 3: Equation 1 shows summing the alignment score over alignments). The summing of the alignment score is what generates the group score (i.e. for the alignments of the set). Claim 19 recites the limitations of claim 3 directed to a system.
Regarding Claim 13, Frith and Kawaguchi teach determine that a fragment alignment score fails to satisfy a threshold and remove the fragment alignment from consideration in forming the candidate split groups (Page 12, Column 2, Paragraph 5: each alignment was rescored with gentle masking of lowercase letters: if it lacked any segment with score ≥ e it was discarded). Discarding the alignment is interpreted as equivalent to removing the alignment.
Regarding Claim 14, Frith and Kawaguchi teach determine that an alignment score for a candidate split group fails to satisfy a minimum alignment score and refrain from reporting a split alignment in an alignment file or a variant call file (Page 12, Column 2, Paragraph 5: each alignment was rescored with gentle masking of lowercase letters: if it lacked any segment with score ≥ e it was discarded). Frith and Kawaguchi indicate the alignments that are not discarded as indicated above are output (Page 6, Table 2: Output). Therefore, discarding from the output of the selection is interpreted as equivalent to refrain from reporting an alignment within the output.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7-11, 13-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Frith and Kawaguchi (2015, Genome Biology, Vol. 16, No. 116: 1-17), as applied to the 35 USC 102 rejection above, in view of Darling et al. (2010, Plos One, Vol. 5, No. 6: 1-17), and in further view of Shrestha et al. (2018, Bioinformatics, Vol. 34, No. 21: 3631–3637, IDS filed 25 April 2024) and Sikora et al. (US 20190371432 A1). Italicized text from reference art.
Applicable claims include:
Claim 1. A computer-implemented method comprising: (Claim 1.i) identifying one or more nucleotide reads corresponding to a genomic region of a genomic sample; (Claim 1.ii) determining candidate split groups comprising fragment alignments corresponding to the one or more nucleotide reads; (Claim 1.iii) generating split group scores for split alignments of the candidate split groups with a reference genome; and (Claim 1.iv) selecting, for nucleobase calling of the genomic region, a predicted split group from the candidate split groups based on the split group scores.
Claim 2. The computer-implemented method of claim 1, further comprising (Claim 2.i) determining a candidate split group of the candidate split groups by:(Claim 2.ii) grouping, into the candidate split group, one or more fragment alignments of a single-end nucleotide read; or grouping, into the candidate split group, one or more fragment alignments of a paired-end nucleotide read from a pair of paired-end nucleotide reads.
Claim 3. The computer-implemented method of claim 1, further comprising: (Claim 3.i) generating fragment alignment scores for individual fragment alignments of a candidate split group with the reference genome; and (Claim 3.ii) generating a split group score for the candidate split group based on the fragment alignment scores.
Claim 4. The computer-implemented method of claim 1, further comprising: (Claim 4.i) generating, for a candidate split group of the candidate split groups, a break penalty for relative geometries of a first fragment alignment and a second fragment alignment with respect to the reference genome; and (Claim 4.ii) generating a split group score for the candidate split group based on the break penalty.
Claim 7. The computer-implemented method of claim 1, further comprising: (Claim 7.i) determining the candidate split groups by iteratively grouping individual fragment alignments following an order of outermost fragment alignments to innermost fragment alignments of a nucleotide read; and (Claim 7.ii) generating the split group scores by iteratively scoring groupings of individual fragment alignments following the order in which the individual fragment alignments were grouped.
Claim 8. The computer-implemented method of claim 1, further comprising: (Claim 8.i) identifying, from the candidate split groups, candidate pairs of split groups comprising different fragment alignments for mates of a paired-end nucleotide read; (Claim 8.ii) generating, for the candidate pairs of split groups, pair scores evaluating pair alignments of the candidate pairs of split groups with the reference genome; and (Claim 8.iii) selecting, for each mate of the paired-end nucleotide read, the predicted split group based further on the pair scores.
Claim 9. The computer-implemented method of claim 8, further comprising: (Claim 9.i) determining sums of split group scores for respective candidate pairs of split groups; (Claim 9.ii) generating pairing penalties based on an estimated insert size between innermost fragment alignments of the candidate pairs of split groups; and (Claim 9.iii) generating the pair scores for the candidate pairs of split groups based on the sums of split group scores and the pairing penalties.
Claim 10. The computer-implemented method of claim 8, further comprising: (Claim 10.i) determining an alt-contig fragment alignment score for an inner fragment alignment and an outer fragment alignment corresponding to a nucleotide read with an alternate contiguous sequence within the reference genome; (Claim 10.ii) determining a split group score for the inner fragment alignment and the outer fragment alignment with a primary-assembly region of the reference genome; and (Claim 10.iii) selecting the alt-contig fragment alignment score as a replacement split group score based on determining that the alt-contig fragment alignment score exceeds the split group score.
Claim 11. A system comprising: at least one processor; and a non-transitory computer-readable medium comprising instructions that, when executed by the at least one processor, cause the system to:(Claim 11.i) identify one or more nucleotide reads corresponding to a genomic region of a genomic sample; (Claim 11.ii) determine candidate split groups comprising fragment alignments corresponding to the one or more nucleotide reads; (Claim 11.iii) generate split group scores for split alignments of the candidate split groups with a reference genome; and (Claim 11.iv) select, for nucleobase calling of the genomic region, a predicted split group from the candidate split groups based on the split group scores.
Claim 13. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to: determine that a fragment alignment score of a fragment alignment fails to satisfy a threshold fragment alignment score; and remove the fragment alignment from consideration in forming the candidate split groups.
Claim 14. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to: determine that an alignment score for a candidate split group fails to satisfy a minimum alignment score; and refrain from reporting a split alignment of the candidate split group in an alignment file or a variant call file based on the alignment score failing to satisfy the minimum alignment score.
Claim 16. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to:(Claim 16.i) determine the candidate split groups by iteratively grouping individual fragment alignments following an order of outermost fragment alignments to innermost fragment alignments of a nucleotide read; and (Claim 16.ii) generate the split group scores by iteratively scoring groupings of individual fragment alignments following the order in which the individual fragment alignments were grouped.
Claim 17. A non-transitory computer-readable medium comprising instructions that, when executed by at least one processor, cause a computing device to: (Claim 17.i) identify one or more nucleotide reads corresponding to a genomic region of a genomic sample; (Claim 17.ii) determine candidate split groups comprising fragment alignments corresponding to the one or more nucleotide reads; (Claim 17.iii) generate split group scores for split alignments of the candidate split groups with a reference genome; and (Claim 17.iv) select, for nucleobase calling of the genomic region, a predicted split group from the candidate split groups based on the split group scores.
Claim 18. The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the at least one processor, cause the computing device to (Claim 18.i) determine a candidate split group of the candidate split groups by: (Claim 18.ii) grouping, into the candidate split group, one or more fragment alignments of a single-end nucleotide read; or grouping, into the candidate split group, one or more fragment alignments of a paired-end nucleotide read from a pair of paired-end nucleotide reads.
Claim 19. The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the at least one processor, cause the computing device to: (Claim 19.i) generate fragment alignment scores for individual fragment alignments of a candidate split group with the reference genome; and (Claim 19.ii) generate a split group score for the candidate split group based on the fragment alignment scores.
Claim 20. The non-transitory computer-readable medium of claim 17, further comprising instructions that, when executed by the at least one processor, cause the computing device to: (Claim 20.i) generate, for a candidate split group of the candidate split groups, a break penalty for relative geometries of a first fragment alignment and a second fragment alignment with respect to the reference genome; and (Claim 20.i) generate a split group score for the candidate split group based on the break penalty.
Regarding Claims 1, 11 and 17, Frith and Kawaguchi teach (Claim 1.i) identifying nucleotide reads corresponding to a genomic region of a genomic sample (Page 12, Column 1, Paragraph 3: The input is a set of local alignments between one query sequence and one genome). The set of input alignments is interpreted as a sample. Frith and Kawaguchi also teach (Claim 1.ii) determining candidate split groups comprising fragment alignments (Page 4, Column 2, Paragraph 3: Find local alignments between the two genomes, by seed-and-extend (many-to-many). Apply the repeated matches algorithm, constrained to the candidate alignments found in step 1. We refer to this constrained version of the repeated matches algorithm as “split-alignment”. Split-alignment guarantees to find a set of many-to-one alignments). Frith and Kawaguchi also teach (Claim 1.iii) generating split group scores for split alignments of the candidate split groups (Page 4, Column 2, Paragraph 3: Split-alignment guarantees to find a set of many-to-one alignments that maximizes the sum of (alignment score − f ), where each alignment in the set is part (or all) of a candidate alignment; Page 4, Column 1, Paragraph 4: The idea is to seek a set of one-to-one alignments between two genomes that maximizes Equation 1 (i.e. equation 1 generates the score for the set)). Frith and Kawaguchi also teach (Claim 1.iv) selecting, for nucleobase calling of the genomic region, a predicted split group based on the split group scores (Page 4, Column 1, Paragraph 4: The idea is to seek a set of one-to-one alignments between two genomes that maximizes Equation 1). The alignment that is maximized by the equation as the one to one becomes the final (i.e. the selected) alignment. The final alignment selected represents a specific sequence (i.e. it is for base calling). Additionally, Frith and Kawaguchi teach the methods are performed by a computer, which inherently contains memory, including non-transitory, and at least one processor (Page 12, Column 1, Paragraph 2: The software is available, and also in the last-align package). Claims 11 and 17 recite the limitations of claim 1 directed to systems.
Regarding Claims 3 and 19, Frith and Kawaguchi teach (Claim 3.i) generating alignment scores for individual alignments of a candidate split group with the reference genome (Page 4, Column 1, Paragraph 3: Equation 1 shows an alignment score; Page 4, Column 2, Paragraph 3: Find local alignments between the two genomes, by seed-and-extend (many-to-many)). Frith and Kawaguchi also teach (Claim 3.ii) generating a split group score for the candidate split group based on the fragment alignment scores (Page 4, Column 1, Paragraph 3: Equation 1 shows summing the alignment score over alignments). The summing of the alignment score is what generates the group score (i.e. for the alignments of the set). Claim 19 recites the limitations of claim 3 directed to a system.
Regarding Claims 4 and 20, Frith and Kawaguchi teach (Claim 4.i) generating, for a candidate split group, a break penalty for relative geometries of a first fragment alignment and a second fragment alignment (Page 4, Column 1, Paragraph 3: Here, f is an “alignment existence cost”, which is necessary to avoid trivial solutions with lots of length-1 alignments. It is similar to Mauve’s breakpoint penalty). It is related to relative geometries of the different segments and is functionally equivalent to a breakpoint penalty. Frith and Kawaguchi also teach (Claim 4.ii) generating a split group score for the candidate split group based on the break penalty (Page 4, Column 1, Paragraph 3: The idea is to seek a set of one-to-one alignments between two genomes that maximizes Equation 1). The sum of scores indicated in equation 1 (Page 4, Column 1, Paragraph 3) is interpreted as the split group score. See Regarding claim 4.i for the split group score incorporated the break penalty. Claim 20 recites the limitations of claim 4 directed to a system.
Regarding Claim 9, Frith and Kawaguchi teach (Claim 9.i) determining sums of split group scores for respective candidate split groups (see regarding claims 3, 4, 5, and 6). Frith and Kawaguchi also teach (Claim 9.iii) generating the group scores for the of split groups based on the sums of group scores and the penalties (see regarding claims 3, 4, 5, and 6). It would be obvious to subtract penalties from the pair scores based on Frith and Kawaguchi teachings of subtracting geometry based penalties from the generated scores within the group scoring system.
Regarding Claim 10, Frith and Kawaguchi teach (Claim 10.iii) selecting maximum score (Page 4, Column 1, Paragraph 3: The idea is to seek a set of one-to-one alignments between two genomes that maximizes). It would be obvious to combine this method of seeking the maximum score as the final score with Darling et al. use of generating scores related to contigs (see reason to combine) whether that the score was based on alignments that were alt contigs or other alignments.
Regarding Claim 13, Frith and Kawaguchi teach determine that a fragment alignment score fails to satisfy a threshold and remove the fragment alignment from consideration in forming the candidate split groups (Page 12, Column 2, Paragraph 5: each alignment was rescored with gentle masking of lowercase letters: if it lacked any segment with score ≥ e it was discarded). Discarding the alignment is interpreted as equivalent to removing the alignment.
Regarding Claim 14, Frith and Kawaguchi teach determine that an alignment score for a candidate split group fails to satisfy a minimum alignment score and refrain from reporting a split alignment in an alignment file or a variant call file (Page 12, Column 2, Paragraph 5: each alignment was rescored with gentle masking of lowercase letters: if it lacked any segment with score ≥ e it was discarded). Frith and Kawaguchi indicate the alignments that are not discarded as indicated above are output (Page 6, Table 2: Output). Therefore, discarding from the output of the selection is interpreted as equivalent to refrain from reporting an alignment within the output.
Frith and Kawaguchi does not teach determining a candidate split group of the candidate split groups by grouping, into the candidate split group, one or more fragment alignments of a single-end nucleotide read; or grouping, into the candidate split group, one or more fragment alignments of a paired-end nucleotide read from a pair of paired-end nucleotide reads (Claims 2 and 18). Frith and Kawaguchi also do not teach determining the candidate split groups by iteratively grouping individual fragment alignments following an order of outermost fragment alignments to innermost fragment alignments of a nucleotide read; and generating the split group scores by iteratively scoring groupings of individual fragment alignments following the order in which the individual fragment alignments were grouped. (Claims 7 and 16). Frith and Kawaguchi also do not teach identifying, from the candidate split groups, candidate pairs of split groups comprising different fragment alignments for mates of a paired-end nucleotide read; generating, for the candidate pairs of split groups, pair scores evaluating pair alignments of the candidate pairs of split groups with the reference genome; and selecting, for each mate of the paired-end nucleotide read, the predicted split group based further on the pair scores (Claim 8). Frith and Kawaguchi also do not teach determining scores for respective candidate pairs; generating pairing penalties based on an estimated insert size between innermost fragment alignments of the candidate pairs of split groups (Claim 9.i and 9.ii). Frith and Kawaguchi also do not teach determining an alt-contig fragment alignment score for an inner fragment alignment and an outer fragment alignment corresponding to an alternate contiguous sequence within the reference genome; determining a split group score for the inner fragment alignment and the outer fragment alignment with a primary-assembly region of the reference genome (Claim 10.i and 10.ii).
Regarding Claims 2 and 18, Darling et al. teach (Claim 2.i) determining a candidate split group of the candidate split groups (Page 3, Column 1, Paragraph 3: Our genome alignment algorithm takes as input a set of G genome sequences. The basic building blocks of the whole genome alignment are local multiple alignments (LMAs), which we will denote by Aloc). Aloc is interpreted as the selected group of the groups. Darling et al. also teach (Claim 2.ii) grouping fragment alignments of a single-end nucleotide read or a paired-end nucleotide reads (Page 3, Column 1, Paragraph 3: Coordinates can be denoted by a signed integer xi in gi. The sign of xi indicates strandedness, with negative values denoting alignments to the reverse strand). The system is used for forward and reverse strand data indicating the read data can come from single end or paired end sequencing data. Claims 18 recite the limitations of claim 2 directed to systems.
Regarding Claims 4 and 20, Darling et al. teaches generating a break penalty used in calculating a score with alignment scores related to grouping alignments (Page 5, Column 1, Paragraph 4: Having transformed LMAs into local pairwise alignments, we apply the well-known breakpoint analysis procedure; Page 5, Column 2, Paragraph 6: In equation 3, the constant b is a breakpoint penalty).
Regarding Claims 7 and 16, Darling et al. teach (Claim 7.i) determining the candidate split groups by iteratively grouping individual fragment alignments following an order of outermost fragment alignments to innermost fragment alignments (Page 3, Column 2, Paragraph 3: The resulting local multiple alignments are ungapped and always align a contiguous subsequence of two or more genomes in G. Any given local multiple alignment m can be described formally by its length DmD and vector of integers: x~(x1,x2 . . . ,xG), where xi is a signed left-end coordinate of the LMA in gi , or 0. When xi takes on a value of 0, the ith genome is absent from all of m). The algorithm will work from position 0 and group potential alignments along the chain, which encompasses from 5’ to 3’ (outermost to innermost). Darling et al. teach (Claim 7.ii) generating the split group scores by iteratively scoring groupings of individual fragment alignments following the order in which the individual fragment alignments were grouped (Page 6, Column 1, Paragraph 4: We apply a greedy breakpoint elimination heuristic to optimize (Aloc) which removes potential anchors from Aloc until the score can no longer be increased. Multiple iterations of the optimization procedure result in a strictly decreasing sequence of LMAs). Claim 16 recites the limitations of claim 7 directed to a system.
Regarding Claim 10, Darling et al. teach (Claim 10.i) determining an alt-contig fragment alignment score for an inner fragment alignment and an outer fragment alignment corresponding to an alternate contiguous sequence within the reference genome (Page 3, Column 1, Paragraph 3: Contigs in unfinished or multi-chromosome genomes are concatenated to form a single coordinate system; Page 3, Column 2, Paragraph 3: The resulting local multiple alignments are ungapped and always align a contiguous subsequence of two or more genomes). The methods of Darling et al. utilize contigs from different alignments which are interpreted as alt contigs and generates scores for the different possible alignments across the sequence (see regarding claim 7). Darling et al. teach (Claim 10.ii) determining a split group score for the inner fragment alignment and the outer fragment alignment with a primary-assembly region of the reference genome. This is interpreted as equivalent to the combined score that was made from the different assemblies aligned to the reference genome (Page 3, Column 2, Paragraph 7: We combine the traditional substitution score for a pair of nucleotides with an adjustment for the multiplicity of k-mer seeds at the aligned positions).
Darling et al. do not teach identifying, from the candidate split groups, candidate pairs of split groups comprising different fragment alignments for mates of a paired-end nucleotide read; generating, for the candidate pairs of split groups, pair scores evaluating pair alignments of the candidate pairs of split groups with the reference genome; and selecting, for each mate of the paired-end nucleotide read, the predicted split group based further on the pair scores (Claim 8). Darling et al. also do not teach determining scores for respective candidate pairs; generating pairing penalties based on an estimated insert size between innermost fragment alignments of the candidate pairs of split groups (Claim 9.i and 9.ii).
Regarding Claim 8, Shrestha et al. teach (Claim 8.i) identifying, from the candidate split groups, candidate pairs of split groups comprising different fragment alignments for mates of a paired-end read (Page 3633, Column 1, Paragraph 2: Let x be a read obtained from paired-end sequencing of a DNA fragment f, and let y be its mate. We find high-scoring local alignments of x and y, separately, to g. Next we compute the probability of each column appearing in the alignments of x. We update the column probabilities using information coming from the alignments of y. Finally, we choose high-probability columns to form an alignment (possibly split) of x.). Shrestha et al. also teach (Claim 8.ii) generating, for the candidate pairs of split groups, pair scores evaluating pair alignments of the candidate pairs of split groups with the reference genome (Page 3633, Column 1, Paragraph 3: Suppose we obtain sets of high-scoring local alignments of x and y, respectively, to g; Page 3633: Column 2, Paragraph 2: Next we update the column probabilities associated with each position i in x based on the information coming from its mate y in the form of the set of alignments Y). The updated score is based on the mated pair. Shrestha et al. also teach (Claim 8.iii) selecting, for each mate of the paired-end nucleotide read, the predicted split group based further on the pair scores (Page 3636, Column 1, Paragraph 2: We wish to report a final alignment for x, in which each base of x appears at most once. Therefore we need to make a choice of which columns will appear in the reported alignment. For each base of x, we simply choose the column that has the highest posterior probability, and report the collection of all such columns as the final alignment; Page 3636, Column 1, Paragraph 2: Finally, we repeat the procedure for y, this time with the alignments in X treated as data). The scores were used to determine the probabilities for the final alignment. Therefore the selection of the group is based on the pared scores.
Regarding Claim 9, Shrestha et al. teach (Claim 9.i) determining scores for respective candidate pairs (see regarding claim 8). Shrestha et al. also teach (Claim 9.ii) generating pairing penalties based on an estimated insert size between innermost fragment alignments of the candidate pairs of split groups (Page 3633, Column 2, Paragraph 3: We distinguish two cases: conjoint, when read y is informative about the alignment of x, and disjoint when it’s not). The probability that there is a disjoint represents a numerical value the pair is not aligned correctly which agrees with paragraph 0057 of the published specification which defines pairing penalty as a metric indicating a likelihood or unlikelihood of fragment alignments being correctly paired based on a geometry of two or more fragment alignments with respect to a reference genome.
Additionally Regarding Claims 2 and 18, Sikora et al. teach grouping fragments of split end reads (Paragraph 0005: In some embodiments, the processed sequence reads with the same start-stop positions on the reference sequence are grouped into a family. In some embodiments, the genetic sequence reads comprises paired end sequence reads).
Additionally Regarding Claims 7 and 16, Sikora et al. teach determining split groups in order of outermost fragment alignments to innermost fragment alignments (Paragraph 0004: calling a fusion cluster as comprising an insertion and/or deletion where: breakpoint pairs map to the same chromosome, distance between the first breakpoint and the second breakpoint in the breakpoint pair is less than a predetermined maximum distance on the reference sequence, and sub-sequences are in the same 5′-3′ orientation; Paragraph 0005: the processed sequence reads with the same start-stop positions on the reference sequence are grouped into a family. In some embodiments, the genetic sequence reads comprises paired end sequence reads).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Frith and Kawaguchi, Darling et al., Shrestha et al., and Sikora et al. Darling et al. teach novel methods of analyzing genome alignments that lead to increased accuracy and considers structural variations (Page 2, Column 1, Paragraph 3: In the present work, we describe a new method to construct positional homology multiple genome alignments. The new method can align a larger number of genomes than the previous method, and does so with higher accuracy as demonstrated by simulation; Page 14, Column 2, Paragraph 4: Key features of the approach are an anchor scoring function that penalizes alignment anchoring in repetitive regions of the genome and penalizes genomic rearrangement). Shrestha et al. teach methods of analyzing genome alignments to ascertain information on structural variations that are more accurate than previous methods (Page 3637, Column 1, Paragraph 2: We demonstrated that our method produces more accurate split-alignments than existing methods, and as a consequence leads to more accurate identification of large variants from whole genome DNA-sequencing reads). Sikora et al. teach methods especially adept at dealing with a wide variety of structural variation when analyzing genetic sequence alignments (Paragraph 0077: For example, half of the sequence in the overlapped region at 3′ ends can be removed to exclude bases with low sequence quality, molecular barcodes on 3′ ends, and any mismatches. This step is useful in reducing sequencing errors; Paragraph 0125: These system and methods may be used to detect any number of genetic aberrations. These may include but are not limited to mutations, rare mutations, indels, copy number variations, transversions, translocations, inversion, deletions, chromosomal instability, chromosomal structure alterations, gene fusions, chromosome fusions, gene truncations). These the methods would therefore be obvious to combine with those of Frith and Kawaguchi which seeks to refine sequence alignment analyses that can be used to identify and overcome complications from structural variations between genomes (Page 1, Column 1, Paragraph 1: If we compare two genome sequences, such as those of human and chimp, to see how they differ, then intuitively we wish to align the “equivalent” regions of the genomes; Page 12, Column 1, Paragraph 2: The new alignments should be especially beneficial when searching for interesting and unusual features in genome evolution, because these are particularly confounded by alignment errors). Additionally, Frith and Kawaguchi teach their methods are adaptable and meant to be combined with other models of alignment analyses (Page 3, Column 1, Paragraph 4: In this study we shall just use the classic alignment model, though our new methods could be combined with more complex models). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because all utilize similar inputs within the same technical field - analyze genomic sequence data though aligning segments to generate scores. Accordingly, 1-4, 7-11, 13-14, and 16-20 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103.
Claims 1, 3-6, 11, 13-15, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Frith and Kawaguchi, as applied to claims 1-4, 7-11, 13-14, and 16-20 above, in view of Ulahannan et al. (2019, bioRxiv, 1-19). Italicized text from reference art.
Applicable claims include:
Claims 1-4, 7-11, 13-14, and 16-20 are included above.
Claim 5. The computer-implemented method of claim 1, further comprising: (Claim 5.i) generate, for a candidate split group of the candidate split groups, an overlap penalty for an overlap within a nucleotide read between a first fragment alignment and a second fragment alignment; and (Claim 5.ii) generate a split group score for the candidate split group based on the overlap penalty.
Claim 6. The computer-implemented method of claim 1, further comprising (Claim 6.i) generating a split group score for a candidate split group of the candidate split groups by: (Claim 6.ii) generating fragment alignment scores, a break penalty, and an overlap penalty for fragment alignments of the candidate split group; and (Claim 6.iii) combining the fragment alignment scores and subtracting the break penalty and the overlap penalty from the combined fragment alignment scores.
Claim 15. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to (Claim 15.i) generate a split group score for a candidate split group of the candidate split groups by: (Claim 15.ii) generating fragment alignment scores, a break penalty, and an overlap penalty for fragment alignments of the candidate split group; and (Claim 15.iii) combining the fragment alignment scores and subtracting the break penalty and the overlap penalty from the combined fragment alignment scores.
Regarding Claims 1, 3, 4, 11, 13, 14, 17, and 19, the limitations are taught by Frith and Kawaguchi as above.
Regarding Claim 5, Frith and Kawaguchi suggest (Claim 5.i) generate, for a candidate split group, an overlap penalty between alignments (Page 4, Column 2, Paragraph 3: given a set of alignments that overlap in the query, it finds an optimal set of nonoverlapping alignment parts. One aspect of this is finding optimal breakpoints for jumping between overlapping alignments; Page 5, Column 2, Paragraph 3: To mitigate this problem, a “gapless alignment culling” step was added. This step discards any gapless alignment whose query segment lies in those of two or more other alignments with greater score-per-length). Frith and Kawaguchi also suggest (Claim 5.ii) generate a split group score for the candidate split group based on the overlap penalty (Page 4, Column 2, Paragraph 3: Split-alignment guarantees to find a set of many-to-one alignments that maximizes the sum of (alignment score − f ), where each alignment in the set is part (or all) of a candidate alignment). The f represents the alignment existence cost (see regarding claim 4) and is a penalty related the geometries of the fragment alignments that can include consideration of overlapping areas.
Regarding Claims 6 and 15, Frith and Kawaguchi teach (Claim 6.i) generating a split group score for a candidate split group (Page 4, Column 1, Paragraph 3: Equation 1 shows summing the alignment score over the alignments). The summing of the alignment score is what generates the group score (i.e. for the alignments in the set). Frith and Kawaguchi teach (Claim 6.ii) generating fragment alignment scores (see regarding claim 3) and a break penalty (see regarding claim 4) for fragment alignments of the candidate split group. Frith and Kawaguchi suggest (Claim 6.ii) generating an overlap penalty for fragment alignments of the candidate split group (see regarding claim 5). Frith and Kawaguchi teach (Claim 6.iii) combining the fragment alignment scores and subtracting the break penalty from the combined fragment alignment scores (Page 4, Column 1, Paragraph 3: The idea is to seek a set of one-to-one alignments between two genomes that maximizes Equation 1 (Sum of alignments – breakpoint penalty). Here, f is an “alignment existence cost”, which is necessary to avoid trivial solutions with lots of length-1 alignments. It is similar to Mauve’s breakpoint penalty). It would also be obvious to add subtracting the overlap penalty of Ulahannan et al. which is also subtracted from the alignment scores (Page 4, Column 2, Paragraph 3: In other words, given a set of alignments that overlap in the query, it finds an optimal set of nonoverlapping alignment parts) when the penalty subtracted by Frith and Kawaguchi can itself be a composite of variables related to fragment geometries (See page 14, Column 1, Equation 13). Claim 15 recites the limitations of claim 6 directed to a system.
Frith and Kawaguchi do not teach an overlap penalty between alignments and generate a group score for the candidate based on the overlap penalty (Claim 5, 6, and 15). Frith and Kawaguchi also do not teach combining the fragment alignment scores and subtracting the overlap penalty from the combined fragment alignment scores (Claim 6.iii).
Regarding Claim 5, Ulahannan et al. teach an overlap penalty between alignments and generate a group score for the candidate based on the overlap penalty (Page 15, Figure s1: The DAG is constituted first with nodes representing the read start and end, and then with edges reflecting the combination of the alignment score as well as a gap or overlap penalty between the two involved reads or a given read and either the start or end of the read). The use of the overlap penalty in combination with the alignment score make is obvious to use this a part of the group score (See Claim 5.ii).
Regarding Claims 6 and 15, Ulahannan et al. teach (Claim 6.ii) generating an overlap penalty for fragment alignments (see regarding claim 5). Ulahannan et al. teach (Claim 6.iii) combining the fragment alignment scores and subtracting the overlap penalty from the combined fragment alignment scores (Page 15, Figure s1: The DAG is constituted first with nodes representing the read start and end, and then with edges reflecting the combination of the alignment score as well as a gap or overlap penalty between the two involved reads or a given read and either the start or end of the read).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Frith and Kawaguchi and Ulahannan et al. Ulahannan et al. teach novel methods analyzing sequence data to conder alignment and structural variation (Page 3, Column 1, Paragraph 2: We hypothesized that this approach would reveal higher-order 3D chromatin structure at both the scale of chromosomal compartments and chromatin loops, identify novel 3D structures generated through complex cancer genomic rearrangements, and improve the scaffolding of long read, whole genome sequencing (WGS) derived assemblies). These the methods would therefore be obvious to combine with those of Frith and Kawaguchi which seeks to refine sequence alignment analyses that can be used to identify and overcome complications from structural variations between genomes (Page 1, Column 1, Paragraph 1: If we compare two genome sequences, such as those of human and chimp, to see how they differ, then intuitively we wish to align the “equivalent” regions of the genomes; Page 12, Column 1, Paragraph 2: The new alignments should be especially beneficial when searching for interesting and unusual features in genome evolution, because these are particularly confounded by alignment errors). Additionally, Frith and Kawaguchi teach their methods are adaptable and meant to be combined with other models of alignment analysis (Page 3, Column 1, Paragraph 4: In this study we shall just use the classic alignment model, though our new methods could be combined with more complex models). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because all utilize similar inputs within the same technical field - analyze genomic sequence data though aligning segments to generate scores. Accordingly, Claims 1, 3-6, 11, 13-15, 17 and 19 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103.
Claims 1, 3, 11-14, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Frith and Kawaguchi, as applied to claims 1-4, 7-11, 13-14, and 16-20 above, in view of Li et al. (2008, Genome Research, Vol 18: 1851-1858). Italicized text from reference art.
Applicable claims include:
Claims 1, 3, 11, 13-14, 17, and 19 are included above.
Claim 12. The system of claim 11, further comprising instructions that, when executed by the at least one processor, cause the system to determine nucleobase calls for the genomic region based on an alignment of the predicted split group with the reference genome.
Regarding Claims 1, 3, 11, 13, 14, 17, and 19, the limitations are taught by Frith and Kawaguchi as above.
Frith and Kawaguchi do not teach determine nucleobase calls for the genomic region based on an alignment of the predicted split group with the reference genome (Claim 12).
Regarding Claim 12, Li et al. teach determine nucleobase calls for the genomic region based on an alignment of the predicted split group with the reference genome (Page 1857, Column 1, Paragraph 6: It calculates the posterior distribution of genotypes and calls the genotype that maximizes the posterior probability. Before consensus calling, MAQ first combines mapping quality and base quality).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Frith and Kawaguchi and Li et al. Li et al. teach novel and capable methods analyzing sequence data to conder alignment and structural variation (Page 1851, Column 2, Paragraph 2: Here, we show how to calculate the error probability of a read mapping. We also introduce a new statistical model for consensus genotype calling and subsequent SNP calling; Page 1855, Column 1, Paragraph 3: MAQ is capable of human whole-genome alignments and supports SNP calling on a diploid sample). These the methods would therefore be obvious to combine with those of Frith and Kawaguchi which seeks to refine sequence alignment analyses that can be used to identify and overcome complications from structural variations between genomes (Page 1, Column 1, Paragraph 1: If we compare two genome sequences, such as those of human and chimp, to see how they differ, then intuitively we wish to align the “equivalent” regions of the genomes; Page 12, Column 1, Paragraph 2: The new alignments should be especially beneficial when searching for interesting and unusual features in genome evolution, because these are particularly confounded by alignment errors). Additionally, Frith and Kawaguchi teach their methods are adaptable and meant to be combined with other models of alignment analysis (Page 3, Column 1, Paragraph 4: In this study we shall just use the classic alignment model, though our new methods could be combined with more complex models). Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because all utilize similar inputs within the same technical field - analyze genomic sequence data though aligning segments to generate scores. Accordingly, Claims 1, 3, 11-14, 17 and 19 taken as a whole would have been prima facie obvious before the effective filing date and are rejected under 35 U.S.C. 103.
Double Patenting
There are no double patenting identified.
Conclusion
No claims are allowed.
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/B.H.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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