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. Deep sequencing technology provides single-nucleotide resolution genomic data and is considered one of the best measurement technologies to detect SCNAs.
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.
Segments the genome by analyzing the read-depth and B-allele fraction profiles using a double sliding window method. Segmentum is a tool for the identification of copy number alterations (CNAs) and copy-neutral loss of heterozygosity (LOH) in tumor samples using whole-genome sequencing (WGS) data. This tool serves to determining somatic copy numbers using WGS from paired tumor/normal samples. It allows accurate detection as suggested by the evaluation results from simulated and real data.
Finds and locates copy-number alterations from massively parallel sequence data. SegSeq leans on the high density of sequence reads and employs a subsequent merging procedure that joins adjacent chromosomal segments. It creates a list of candidate breakpoints based on read counts in local windows. This tool is useful for discovering extremely small intragenic events such as homozygous deletions.
A suite of tools to make copy number estimations for whole genome data with GC and mappability correction, then segment and classify copy number profiles with a robust Hidden Markov Model. Designed to work with high coverage whole genome HTS data, it can derive copy number estimations from large (~250GB) aligned BAM files in 2 hours on a single core with minimal memory requirements.
This package for R can detect copy number aberrations by measuring the depth of coverage obtained by massively parallel sequencing of the genome. In contrast to other published methods, readDepth does not require the sequencing of a reference sample, and uses a robust statistical model that accounts for overdispersed data. It includes a method for effectively increasing the resolution obtained from low-coverage experiments by utilizing breakpoint information from paired end sequencing to do positional refinement. It can also be used to infer copy number using reads obtained from bisulfite sequencing experiments.
Allows to find recurrent copy number alterations (CNAs). The GAIA method uses a discrete representation of the data to perform a permutation test. With it, a novel iterative procedure taking into account both significance and within-sample homogeneity (homogeneous peel-off) is used to identify the most significant peaks. This tool is useful for user which desires to work on high-resolution data.