Low-frequency SNV identification software tools | Whole-genome sequencing data analysis
Accurate identification of low-frequency somatic point mutations in tumor samples has important clinical utilities. Although high-throughput sequencing technology enables capturing such variants while sequencing primary tumor samples, our ability for accurate detection is compromised when the variant frequency is close to the sequencer error rate. Most current experimental and bioinformatic strategies target mutations with >/=5% allele frequency, which limits our ability to understand the cancer etiology and tumor evolution.
A platform-independent mutation caller for targeted, exome, and whole-genome resequencing data generated on Illumina, SOLiD, Life/PGM, Roche/454, and similar instruments. The newest version, VarScan 2, is written in Java, so it runs on most operating systems. It can be used to detect different types of variation: 1) germline variants (SNPs and indels) in individual samples or pools of samples, 2) multi-sample variants (shared or private) in multi-sample datasets (with mpileup), 3) somatic mutations, LOH events, and germline variants in tumor-normal pairs and 4) somatic copy number alterations (CNAs) in tumor-normal exome data.
Provides quantitative variant callers for detecting subclonal mutations in ultra-deep sequencing experiments. DeepSNV is a comparative targeted deep-sequencing approach combined with a customised statistical algorithm, which can detect and quantify subclonal single-nucleotide variants (SNVs) in mixed populations. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and the shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters.
A sensitive and robust approach for calling single-nucleotide variants (SNVs) from high-coverage sequencing datasets, based on a formal model for biases in sequencing error rates. LoFreq adapts automatically to sequencing run and position-specific sequencing biases and can call SNVs at a frequency lower than the average sequencing error rate in a dataset. LoFreq’s robustness, sensitivity and specificity were validated using several simulated and real datasets (viral, bacterial and human) and on two experimental platforms (Fluidigm and Sequenom).
Finds single nucleotide variants (SNVs) and short indels from circulating cell-free DNA (cfDNA) sequencing data. SiNVICT improves the sensitivity and specificity of SNV and indel discovery at very low variant allele frequencies. It supports multiple sequencing platforms with different error properties. This tool reduces false positives resulting from mapping errors and other technology specific artifacts.
Provides a sequencing analysis pipeline dedicated to the identification of somatic variants present at low-allelic fraction from high-throughput sequencing of amplicons in matched tumor-normal specimen. Mutascope determines the amplicon of origin for each read and measures the specific experimental error rate from sequencing the normal DNA. It identifies mutations in the tumor detected by comparison with the error rate by using binomial statistics and classified as germ line or somatic comparison with the normal DNA.
Includes two main operating modes: (1) a classical variant-caller; (2) a tool to evaluate local quality metrics. OutLyzer estimates the sum of several background noise sources, including sequencing mistakes, errors generated by sample preparation and bioinformatics analysis, based on an outlier detection algorithm. For a given sample, this tool evaluates whether a mutation is present at a given position, specifies all raw sequencing information and assesses local background noise.
Differentiates patient-specific from cohort-specific alterations.j MICADo is based on the well-known representation of next generation sequencing (NGS) sequencing reads, de Bruijn graphs (DBG). It permits to circumvent the alignment step required by most single nucleotide variation (SNV) callers. This tool can avoid additional biases due to the alignment itself. It is useful in targeted sequencing with high background noise from cohorts of patients.
Identifies low-frequency single-nucleotide variants (SNVs) from high-throughput sequencing data. RareVar aims to push the detection limit to 0.5%–1% under standard sequencing experiment protocols. It is based on position-specific error modeling (PSEM) method and machine learning-based variant calibration approach. The tool is able to identify variants by calculating Bayes factor. It is capable of high specificity for variant identification.
Assists for profiling circulating tumor DNA (ctDNA). NG-TAS features several functions: (1) optimization for low input ctDNA; (2) high level of multiplexing for analyzes of multiple gene targets; (3) a computational pipeline for data analysis and (4) a competitive costing. This tool can be adapted to different cancer types and clinical contexts and is conceived to be flexible for the choice of gene targets and regions of interest.
Assists in finding signal in the noise in cancer and hereditary disease next generation sequencing (NGS) data. Biomedical Genomics Workbench integrates visualization, validation, reporting, and filtering tools. Users can modify workflows and discovery parameters for hypothesis-led analysis. This resource also includes high-sensitivity for detection of germline as well as low frequency variants from DNA-Seq and RNA-Seq data.