Dataset Information

JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.

ABSTRACT: Identification of somatic single nucleotide variants (SNVs) in tumour genomes is a necessary step in defining the mutational landscapes of cancers. Experimental designs for genome-wide ascertainment of somatic mutations now routinely include next-generation sequencing (NGS) of tumour DNA and matched constitutional DNA from the same individual. This allows investigators to control for germline polymorphisms and distinguish somatic mutations that are unique to the tumour, thus reducing the burden of labour-intensive and expensive downstream experiments needed to verify initial predictions. In order to make full use of such paired datasets, computational tools for simultaneous analysis of tumour-normal paired sequence data are required, but are currently under-developed and under-represented in the bioinformatics literature.In this contribution, we introduce two novel probabilistic graphical models called JointSNVMix1 and JointSNVMix2 for jointly analysing paired tumour-normal digital allelic count data from NGS experiments. In contrast to independent analysis of the tumour and normal data, our method allows statistical strength to be borrowed across the samples and therefore amplifies the statistical power to identify and distinguish both germline and somatic events in a unified probabilistic framework.The JointSNVMix models and four other models discussed in the article are part of the JointSNVMix software package available for download at http://compbio.bccrc.casshah@bccrc.caSupplementary data are available at Bioinformatics online.

SUBMITTER: Roth A

PROVIDER: S-EPMC3315723 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.

Roth Andrew A Ding Jiarui J Morin Ryan R Crisan Anamaria A Ha Gavin G Giuliany Ryan R Bashashati Ali A Hirst Martin M Turashvili Gulisa G Oloumi Arusha A Marra Marco A MA Aparicio Samuel S Shah Sohrab P SP

Bioinformatics (Oxford, England) 20120127 7

<h4>Motivation</h4>Identification of somatic single nucleotide variants (SNVs) in tumour genomes is a necessary step in defining the mutational landscapes of cancers. Experimental designs for genome-wide ascertainment of somatic mutations now routinely include next-generation sequencing (NGS) of tumour DNA and matched constitutional DNA from the same individual. This allows investigators to control for germline polymorphisms and distinguish somatic mutations that are unique to the tumour, thus r ...[more]

PMID: 22285562

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Project description:BackgroundAdapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing.ResultsTo improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm, which has O(kn) expected time with O(m) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer (https://sourceforge.net/projects/skewer). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that Skewer outperforms all other similar tools that have the same utility. Further, Skewer is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing.ConclusionsSkewer achieved as yet unmatched accuracies for adapter trimming with low time bound.

Project description:Background: Paired tumor-normal targeted next-generation sequencing (NGS) is primarily used to identify actionable somatic mutations, but can also detect germline variants including pathogenic germline mutations in DNA mismatch repair (MMR) genes that underlie Lynch syndrome. In the present study we examined paired NGS data from lung cancer patients to identify germline mutations in MMR genes. As lung cancer is not one of the recognized Lynch syndrome-associated neoplasms, we also investigated whether these lung cancer cases are due to Lynch syndrome or are instead sporadic cancers occurring in Lynch syndrome patients. Methods: A retrospective study of 1,179 lung cancer patients with available paired NGS data was performed to identify germline mutations in the MMR genes MLH1, MSH2, MSH6, and PMS2, and evaluate tumor mutation burden (TMB). Microsatellite instability (MSI) testing was done on select cases with MMR gene mutations by either NGS or PCR/capillary electrophoresis approach. Immunohistochemistry (IHC) for MMR proteins was performed in select patients. Results: Pathogenic or likely-pathogenic germline mutations in PMS2, MSH2, or MSH6 were detected in 0.5% (6/1,179) of lung cancer patients; three of the patients had a family history of colon or gastric cancer. The median age at diagnosis of these cases was 68.5 years old. None of these six patients exhibited MSI or loss of MMR protein expression. Among them, no second hit somatic mutations in MMR genes (including single-nucleotide variants, small insertions or deletions and copy number alterations) were detected, and the median TMB was 4.5 muts/MB. Subsequent genetic testing of family members identified new Lynch syndrome cases in two first-degree relatives. Conclusion: These data imply that lung cancers in Lynch syndrome patients are unrelated to the underlying Lynch syndrome diagnosis and occur spontaneously. Nonetheless, paired tumor-normal NGS can identify germline mutations to help reveal Lynch syndrome in cancer patients. This has important implications for cancer screening and risk reduction in these patients and their families.

Dataset Information

JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.

Publications

JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.

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