Copy number variation identification software tools | Whole-exome sequencing data analysis
Gene copy number variation is a particular form of polymorphism and structural variation where the number of copies of a single gene varies between individuals of a same species. This phenomenon plays a key role in individual variability and can be influenced by selection. Whole-exome sequencing is used to detect and characterize genome and copy number variation. Software tools are used for processing, analysis, and visualization of structural variation based on sequencing data.
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.
Focuses on variant discovery and genotyping. GATK provides a toolkit, developed at the Broad Institute, composed of several tools and able to support projects of any size. The application compiles an assortment of command line allowing one to analyze of high-throughput sequencing (HTS) data in various formats such as SAM, BAM, CRAM or VCF. The website includes multiple documentation for guiding users.
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.
Extracts copy-number signal from noisy read depth by leveraging the large-scale nature of sequencing projects to discern patterns of read-depth biases. XHMM is a statistical toolset that normalizes sequencing coverage in large-scale exome sequencing. It uses this information to discover Copy-Number Variants (CNVs) while providing quality metrics that indicate how strongly the data support a particular CNV.
Calls copy number variants (CNVs) from targeted sequence data, typically exome sequencing experiments designed to identify the genetic basis of Mendelian disorders. ExomeDepth was developed as a standalone software that uses read count data from exome or targeted sequencing experiments to call CNVs. This method allows efficient identification and can increase the value of the future exome sequencing experiments.