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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.
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A software tool for copy number detection that uses both the targeted reads and the nonspecifically captured off-target reads to infer copy number evenly across the genome. This combination achieves both exon-level resolution in targeted regions and sufficient resolution in the larger intronic and intergenic regions to identify copy number changes. In particular, we successfully inferred copy number at equivalent to 100-kilobase resolution genome-wide from a platform targeting as few as 293 genes. After normalizing read counts to a pooled reference, we evaluated and corrected for three sources of bias that explain most of the extraneous variability in the sequencing read depth: GC content, target footprint size and spacing, and repetitive sequences. We compared the performance of CNVkit to copy number changes identified by array comparative genomic hybridization. We packaged the components of CNVkit so that it is straightforward to use and provides visualizations, detailed reporting of significant features, and export options for integration into existing analysis pipelines.
A tool for the detection of copy number aberrations from targeted sequencing. All currently available methods are based on exonic depth of coverage, and suffer from the problems that bait efficiencies are non-uniform and that exons are irregularly distributed over the genome. By exploiting the off-target sequence reads, CopywriteR bypasses these problems. It allows for extracting DNA copy number profiles of a high quality comparable to those of ‘dedicated’ techniques such as SNP array, arrayCGH and low-coverage whole-genome sequencing techniques.
BACOM / Bayesian Analysis of COpy number Mixtures
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.
CLOSE / Cna and LOh analysis with SEquencing data
A robust and efficient strategy for deriving global and allele-specific copy number alternations (CNA) from cancer whole exome sequencing data based on Log R ratios and B-allele frequencies. CLOSE is a lightweight approach for assessing WES-based global DNA copy number aberrations that (i) replicates copy number calls from existing analysis methodologies; (ii) derives tumor ploidy and cellularity; and (iii) provides information for cross-tumor integrative analysis. Applying the approach to the analysis of over 200 skin cancer samples, we demonstrate its utility for discovering distinct CNA events and for deriving ancillary information such as tumor purity.
SAIC / Significant Aberration in Cancer
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.
CoVaCS / Consensus Variant Calling System
Enables genotyping and variant annotation of resequencing data produced by second generation next generation sequencing (NGS) technologies. CoVaCS is an automated system that provides tools for variant calling and annotation along with a pipeline for the analysis of whole genome shotgun (WGS), whole exome sequencing (WES) and targeted resequencing data (TGS). The software allows non-specialists to perform all steps from quality trimming to variant annotation.
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.
A user-friendly tool with functions specifically designed to facilitate the process of interactively visualizing and editing somatic CNV calling results. Different from other general genomics viewers, the index and display of CNV calling results in cnvCurator is segment central. It incorporates multiple CNV-specific information for concurrent, interactive display, as well as a number of relevant features allowing user to examine and curate the CNV calls. cnvCurator provides important and practical utilities to assist the manual review and edition of results from a chosen somatic CNV caller, such that curated CNV segments will be used for down-stream applications.
CoNCoS / Copy Number estimation with Controlled Support
Increases the accuracy of copy number (CN) estimation in paired tumor/normal exome sequencing data sets by assessing and optimizing the support for a site-specific CN estimate. We show by simulations and in a benchmarking study against single nucleotide polymorphism (SNP) microarray data that our approach outperforms the commonly used methods CNAnorm and VarScan2. CoNCoS is suitable to increase the accuracy of somatic CN analysis by a support-optimized estimation approach.
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