Muver, a computational framework for accurately calling accumulated mutations
Abstract Background Identification of mutations from next-generation sequencing data typically requires a balance between sensitivity and accuracy. This is particularly true of DNA insertions and deletions (indels), that can impart significant phenotypic consequences on cells but are harder to call...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-05-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12864-018-4753-3 |
| _version_ | 1811324697537675264 |
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| author | Adam B. Burkholder Scott A. Lujan Christopher A. Lavender Sara A. Grimm Thomas A. Kunkel David C. Fargo |
| author_facet | Adam B. Burkholder Scott A. Lujan Christopher A. Lavender Sara A. Grimm Thomas A. Kunkel David C. Fargo |
| author_sort | Adam B. Burkholder |
| collection | DOAJ |
| description | Abstract Background Identification of mutations from next-generation sequencing data typically requires a balance between sensitivity and accuracy. This is particularly true of DNA insertions and deletions (indels), that can impart significant phenotypic consequences on cells but are harder to call than substitution mutations from whole genome mutation accumulation experiments. To overcome these difficulties, we present muver, a computational framework that integrates established bioinformatics tools with novel analytical methods to generate mutation calls with the extremely low false positive rates and high sensitivity required for accurate mutation rate determination and comparison. Results Muver uses statistical comparison of ancestral and descendant allelic frequencies to identify variant loci and assigns genotypes with models that include per-sample assessments of sequencing errors by mutation type and repeat context. Muver identifies maximally parsimonious mutation pathways that connect these genotypes, differentiating potential allelic conversion events and delineating ambiguities in mutation location, type, and size. Benchmarking with a human gold standard father-son pair demonstrates muver’s sensitivity and low false positive rates. In DNA mismatch repair (MMR) deficient Saccharomyces cerevisiae, muver detects multi-base deletions in homopolymers longer than the replicative polymerase footprint at rates greater than predicted for sequential single-base deletions, implying a novel multi-repeat-unit slippage mechanism. Conclusions Benchmarking results demonstrate the high accuracy and sensitivity achieved with muver, particularly for indels, relative to available tools. Applied to an MMR-deficient Saccharomyces cerevisiae system, muver mutation calls facilitate mechanistic insights into DNA replication fidelity. |
| first_indexed | 2024-04-13T14:20:06Z |
| format | Article |
| id | doaj.art-b4ed54e91cb7433e914d1cbea74bcdfe |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-13T14:20:06Z |
| publishDate | 2018-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-b4ed54e91cb7433e914d1cbea74bcdfe2022-12-22T02:43:32ZengBMCBMC Genomics1471-21642018-05-0119111910.1186/s12864-018-4753-3Muver, a computational framework for accurately calling accumulated mutationsAdam B. Burkholder0Scott A. Lujan1Christopher A. Lavender2Sara A. Grimm3Thomas A. Kunkel4David C. Fargo5Integrative Bioinformatics, National Institute of Environmental Health Sciences, NIH, DHHSLaboratory of Genomic Integrity and Structural Biology, National Institute of Environmental Health Sciences, NIH, DHHSIntegrative Bioinformatics, National Institute of Environmental Health Sciences, NIH, DHHSIntegrative Bioinformatics, National Institute of Environmental Health Sciences, NIH, DHHSLaboratory of Genomic Integrity and Structural Biology, National Institute of Environmental Health Sciences, NIH, DHHSIntegrative Bioinformatics, National Institute of Environmental Health Sciences, NIH, DHHSAbstract Background Identification of mutations from next-generation sequencing data typically requires a balance between sensitivity and accuracy. This is particularly true of DNA insertions and deletions (indels), that can impart significant phenotypic consequences on cells but are harder to call than substitution mutations from whole genome mutation accumulation experiments. To overcome these difficulties, we present muver, a computational framework that integrates established bioinformatics tools with novel analytical methods to generate mutation calls with the extremely low false positive rates and high sensitivity required for accurate mutation rate determination and comparison. Results Muver uses statistical comparison of ancestral and descendant allelic frequencies to identify variant loci and assigns genotypes with models that include per-sample assessments of sequencing errors by mutation type and repeat context. Muver identifies maximally parsimonious mutation pathways that connect these genotypes, differentiating potential allelic conversion events and delineating ambiguities in mutation location, type, and size. Benchmarking with a human gold standard father-son pair demonstrates muver’s sensitivity and low false positive rates. In DNA mismatch repair (MMR) deficient Saccharomyces cerevisiae, muver detects multi-base deletions in homopolymers longer than the replicative polymerase footprint at rates greater than predicted for sequential single-base deletions, implying a novel multi-repeat-unit slippage mechanism. Conclusions Benchmarking results demonstrate the high accuracy and sensitivity achieved with muver, particularly for indels, relative to available tools. Applied to an MMR-deficient Saccharomyces cerevisiae system, muver mutation calls facilitate mechanistic insights into DNA replication fidelity.http://link.springer.com/article/10.1186/s12864-018-4753-3DNA-seqIndelMutationMutation accumulationMutation rate |
| spellingShingle | Adam B. Burkholder Scott A. Lujan Christopher A. Lavender Sara A. Grimm Thomas A. Kunkel David C. Fargo Muver, a computational framework for accurately calling accumulated mutations BMC Genomics DNA-seq Indel Mutation Mutation accumulation Mutation rate |
| title | Muver, a computational framework for accurately calling accumulated mutations |
| title_full | Muver, a computational framework for accurately calling accumulated mutations |
| title_fullStr | Muver, a computational framework for accurately calling accumulated mutations |
| title_full_unstemmed | Muver, a computational framework for accurately calling accumulated mutations |
| title_short | Muver, a computational framework for accurately calling accumulated mutations |
| title_sort | muver a computational framework for accurately calling accumulated mutations |
| topic | DNA-seq Indel Mutation Mutation accumulation Mutation rate |
| url | http://link.springer.com/article/10.1186/s12864-018-4753-3 |
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