A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods to Determine Significant Somatic Mutations. <s> Motivation: The sequencing of tumors and their matched normals is frequently used to study the genetic composition of cancer. Despite this fact, there remains a dearth of available software tools designed to compare sequences in pairs of samples and identify sites that are likely to be unique to one sample. ::: ::: Results: In this article, we describe the mathematical basis of our SomaticSniper software for comparing tumor and normal pairs. We estimate its sensitivity and precision, and present several common sources of error resulting in miscalls. ::: ::: Availability and implementation: Binaries are freely available for download at http://gmt.genome.wustl.edu/somatic-sniper/current/, implemented in C and supported on Linux and Mac OS X. ::: ::: Contact: ude.ltsuw@nosraled; ude.ltsuw@gnidl ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online. <s> BIB001 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods to Determine Significant Somatic Mutations. <s> Detection of somatic point substitutions is a key step in characterizing the cancer genome. However, existing methods typically miss low-allelic-fraction mutations that occur in only a subset of the sequenced cells owing to either tumor heterogeneity or contamination by normal cells. Here we present MuTect, a method that applies a Bayesian classifier to detect somatic mutations with very low allele fractions, requiring only a few supporting reads, followed by carefully tuned filters that ensure high specificity. We also describe benchmarking approaches that use real, rather than simulated, sequencing data to evaluate the sensitivity and specificity as a function of sequencing depth, base quality and allelic fraction. Compared with other methods, MuTect has higher sensitivity with similar specificity, especially for mutations with allelic fractions as low as 0.1 and below, making MuTect particularly useful for studying cancer subclones and their evolution in standard exome and genome sequencing data. <s> BIB002 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods to Determine Significant Somatic Mutations. <s> Summary: VarSim is a framework for assessing alignment and variant calling accuracy in high-throughput genome sequencing through simulation or real data. In contrast to simulating a random mutation spectrum, it synthesizes diploid genomes with germline and somatic mutations based on a realistic model. This model leverages information such as previously reported mutations to make the synthetic genomes biologically relevant. VarSim simulates and validates a wide range of variants, including single nucleotide variants, small indels and large structural variants. It is an automated, comprehensive compute framework supporting parallel computation and multiple read simulators. Furthermore, we developed a novel map data structure to validate read alignments, a strategy to compare variants binned in size ranges and a lightweight, interactive, graphical report to visualize validation results with detailed statistics. Thus far, it is the most comprehensive validation tool for secondary analysis in next generation sequencing. ::: ::: Availability and implementation: Code in Java and Python along with instructions to download the reads and variants is at http://bioinform.github.io/varsim. ::: ::: Contact: moc.anib@dr ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online. <s> BIB003 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods to Determine Significant Somatic Mutations. <s> Motivation: Somatic variant calling typically requires paired tumor-normal tissue samples. Yet, paired normal tissues are not always available in clinical settings or for archival samples. Results: We present SomVarIUS, a computational method for detecting somatic variants using high throughput sequencing data from unpaired tissue samples. We evaluate the performance of the method using genomic data from synthetic and real tumor samples. SomVarIUS identifies somatic variants in exome-seq data of ∼150 × coverage with at least 67.7% precision and 64.6% recall rates, when compared with paired-tissue somatic variant calls in real tumor samples. We demonstrate the utility of SomVarIUS by identifying somatic mutations in formalin-fixed samples, and tracking clonal dynamics of oncogenic mutations in targeted deep sequencing data from pre- and post-treatment leukemia samples. Availability and implementation: SomVarIUS is written in Python 2.7 and available at http://www.sjdlab.org/resources/ Contact: subhajyoti.de@ucdenver.edu Supplementary information: Supplementary data are available at Bioinformatics online. <s> BIB004
After VCF annotation, a WES sample can have thousands of SNVs identified; however, most of them will be silent (synonymous) mutations and will not be meaningful for followup study. Therefore, it is important to identify significant somatic mutations from these variants. Several tools have been developed to do this task for the analysis of cancer WES data, including SomaticSniper BIB001 , MuTect BIB002 , VarSim BIB003 , and SomVarIUS BIB004 . SomaticSniper is a computational program that compares the normal and tumor samples to find out which mutations are unique to the tumor sample, hence predicted as somatic mutations BIB001 . SomaticSniper uses the genotype likelihood model of MAQ (as implemented in SAMtools) and then calculates the probability that the tumor and normal genotypes are different. The probability is reported as a somatic score which is the Phred-scaled probability. SomaticSniper has been applied in various cancer research studies to detect significant somatic variants. Another popular somatic mutation identification tool is MuTect BIB002 , developed by the Broad Institute. MuTect, like SomaticSniper, uses paired normal and cancer samples as input for detecting somatic mutations. After removing lowquality reads, MuTect uses a variant detection statistic to determine if a variant is more probable than a sequencing error. MuTect then searches for six types of known sequencing artifacts and removes them. A panel of normal samples as well as the dbSNP database is used for comparison to remove common polymorphisms. By doing this, the number of somatic mutations is not only identified but also reduced to a more probable set of candidate genes. MuTect has been widely used in Broad Institute cancer genomics studies. While SomaticSniper and MuTect require data from both paired cancer and normal samples, VarSim BIB003 and SomVarIUS BIB004 do not require a normal sample to call somatic mutations. Unlike most programs of its kind, VarSim BIB003 uses a two-step process utilizing both simulation and experimental data for assessing alignment and variant calling accuracy. In the first step, VarSim simulates diploid genomes with germline and somatic mutations based on a realistic model that includes SNVs and SVs. In the second step, VarSim performs somatic variant detection using the simulated data and validates the cancer mutations in the tumor VCF. SomVarIUS is another recent computational method to detect somatic variants in cancer exomes without a normal paired sample BIB004 . In brief, SomVarIUS consists of 3 steps for somatic variant detection. SomVarIUS first prioritizes potential variant sites, estimates the probability of a sequencing error followed by the probability that an observed variant is germline or somatic. In samples with greater than 150x coverage, SomVarIUS identifies somatic variants with at least 67.7% precision and 64.6% recall rates, when compared with paired-tissue somatic variant calls in real tumor samples BIB004 . Both VarSim and SomVarIUS will be useful for cancer samples that lack the corresponding normal samples for somatic variant detection.