Somatic copy-number alterations (SCNAs) are an important type of structural variation affecting tumor pathogenesis. Accurate detection of genomic regions with SCNAs is crucial for cancer genomics as these regions contain likely drivers of cancer development.
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.
Automatically detects copy number alterations (CNAs) and loss of heterozygosity (LOH) regions using next-generation sequencing (NGS) data. Control-FREEC consists of three steps: (i) calculation and segmentation of copy number profiles, (ii) calculation and segmentation of smoothed BAF profiles; and (iii) prediction of final genotype status. The software can call genotype status including when no control experiment is available and/or the genome is polyploid. It also corrects for GC-content and mappability biases.
A tool for copy number variation (CNV) detection for whole-exome data from paired tumour/matched normal samples. ADTEx uses ratio of tumour and matched normal average read depths at each exonic region, to predict the copy gain or loss.
Estimates copy number genomic deletion types and normal tissue contamination. BACOM is based on a statistically principled in silico approach. This software detects significant consensus events (SCE) after in silico adjustment of normal tissue contamination. The BACOM algorithm exploits the allele-specific information provided by single nucleotide polymorphism (SNP) chips to differentiate between hemi-deletion and homo-deletion and subsequently estimates the fraction of normal cells in tissues.
Addresses these issues and automatically detecting clonal and subclonal somatic copy number alterations from heterogeneous tumor samples. CloneCNA fully explores the log ratio of read counts between paired tumor-normal samples and tumor B allele frequency of germline heterozygous SNP positions, further employs efficient statistical models to quantitatively represent copy number status of tumor sample containing multiple clones. We examine CloneCNA on simulated heterogeneous and real tumor samples, and the results demonstrate that CloneCNA has higher power to detect copy number alterations than existing methods.
Detects significant aberrations in cancer (SAC) genome. SAIC identifies and characterizes copy number alterations (CNA) in human genome. The software’s main features are: (1) definition of the CNA unit in order to capture the intrinsic correlation structure in copy number data, (2) production of an unbiased null distribution via an iterative aberration permutation and (3) application to real cancer copy number datasets and identification of the most previously reported aberrations covering well-known cancer genes.
Allows users to identify and characterize somatic mutation patterns in exons and introns from coding and non-coding genes. MIRA can detect deep intronic mutations. It defines significantly mutated regions (SMRs) and motif enrichment by the positions of the mutations, hence providing the precise regions where the mutations are likely to play a role. This tool is based on binomial test and corrected for local nucleotide biases.