# US 2015/0178445 A1 · Detecting variants in sequencing data and benchmarking > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2015/0178445 A1 - Document ID: US20150178445A1 - Status: Published application - Kind: A1 - Application number: 14/633,321 - Filed: 2015-02-27 - Published: 2015-06-25 - Earliest claimed: 2012-08-28 - Assignees: The Broad Institute, Inc. - Applicants: The Broad Institute, Inc. - Inventors: Kristian CIBULSKIS; Gad GETZ; Michael LAWRENCE - Web page: https://patentgrep.com/patent/US20150178445A1 ## CPC - G16B 30/10: Sequence alignment; Homology search - G16B 20/00: ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations - G16B 20/10: Ploidy or copy number detection - G16B 20/20: Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection - G16B 30/00: ICT specially adapted for sequence analysis involving nucleotides or amino acids - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY ## Abstract A system, method, and computer program product for detecting variants from sequencing data. Aligned sequencing data can be provided and filters can be applied to the aligned sequencing data. The filtered data can be used as input, and a first classifier can be applied to determine if any alteration is present beyond an expected threshold due to a sequencing error and candidate variants can be identified. The identified candidate variants can be passed through additional filters to remove false positives. A somatic status of the filtered candidate variants can be determined using a second classifier. The related apparatus, systems, techniques and articles are also described. ## Claims ### Claim 1 A method for detecting one or more variants from sequencing data, the method comprising: receiving aligned sequencing data; applying one or more filters to the aligned sequencing data; using the filtered data as input, applying a first classifier to determine if any alteration is present beyond an expected threshold due to a sequencing error and identifying one or more candidate variants; passing the one or more identified candidate variants through one or more additional filters to remove one or more false positives; and determining a somatic status of the one or more filtered candidate variants using a second classifier, wherein at least one of the above is performed by at least one data processor. ### Claim 2 (depends on 1) A method according to claim 1, wherein the one or more variants are mutations. ### Claim 3 (depends on 2) A method according to claim 2, wherein the mutations are point mutations. ### Claim 4 (depends on 3) A method according to claim 3, wherein the point mutations are somatic or germline point mutations. ### Claim 5 (depends on 1) A method according to claim 1, wherein the one or more false positives are created by correlated sequencing noise. ### Claim 6 (depends on 1) A method according to claim 1, wherein a Panel of Normals is used to identify one or more false positives. ### Claim 7 (depends on 1) A method according to claim 1, wherein the sequencing data comprises DNA sequencing or RNA sequencing data. ### Claim 8 (depends on 1) A method according to claim 1, wherein at least one of the first and second classifiers is a Bayesian classifier. ### Claim 9 (depends on 1) A method according to claim 1, wherein the one or more filters include a proximal gap filter which rejects variants with neighboring insertion and/or deletion events. ### Claim 10 (depends on 1) A method according to claim 1, wherein the one or more filters include a poor mapping region filter which rejects sites having a determined mapping quality score of zero. ### Claim 11 (depends on 1) A method according to claim 1, wherein the one or more filters include a clustered position filter which looks for correlation in the position of mutant alleles within their reads. ### Claim 12 (depends on 1) A method according to claim 1, wherein the one or more filters include a strand bias filter which rejects sites where a distribution of strand observations of mutant allele is biased compared to the allele of the reference genome. ### Claim 13 (depends on 1) A method according to claim 1, wherein the one or more filters include a triallelic site filter which excludes sites each having at least three alleles beyond what is expected by sequencing error. ### Claim 14 (depends on 1) A method according to claim 1, wherein the one or more filters include an observed in control filter which uses sequencing data from a matched normal as control data to eliminate sites where the reference genome has evidence of mutant allele. ### Claim 15 (depends on 1) A non-transitory computer readable medium comprising computer-executable instructions recorded thereon for causing a computer to perform the method according to claim 1. ### Claim 16 A system for detecting one or more variants from sequencing data, the system comprising: means for receiving aligned sequencing data; means for applying one or more filters to the aligned sequencing data; means for using the filtered data as input, applying a first classifier to determine if any alteration is present beyond an expected threshold due to a sequencing error and identifying one or more candidate variants; means for passing the one or more identified candidate variants through one or more additional filters to remove one or more false positives; and means for determining a somatic status of the one or more filtered candidate variants using a second classifier. ### Claim 17 A method for benchmarking performance of variant detection, comprising: providing variants that were discovered in deep-coverage sequencing data sets; down-sampling by randomly excluding a subset of reads of the sequencing data set at sites of known validated variants; and repeating the down-sampling one or more times and estimating a sensitivity as a fraction of the times the known variants are detected; wherein at least one of the above is performed by at least one data processor. ### Claim 18 A method for benchmarking performance of mutation detection, comprising: creating a normal virtual tumor that has no true variants; providing sequence data from a single normal sample; assigning reads of the sequence data to be either “tumor” or “normal” to a desired depth; and measuring a specificity by comparing the normal virtual tumor against the sequence data; wherein at least one of the above is performed by at least one data processor.