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.
Finds somatic copy-number alterations (SCNAs) mediating gene dysregulation in cis by integrating SCNAs, expression and chromatin interaction domain data. CESAM employs statistical concepts from expression quantitative trait locus mapping to proceed. It integrates SCNA breakpoint data with donor-matched transcriptome (mRNA-seq) data to recognize candidate genes in cis. This tool can be useful to uncover genetic driver alterations in cancer genomes.
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.
An algorithm for detecting somatic copy-number alteration (CNA) using whole-genome sequencing (WGS) data. CONSERTING performs iterative analysis of segmentation on the basis of changes in read depth and the detection of localized structural variations, with high accuracy and sensitivity.
Caters to the varying protocols of different next-generation sequencing protocols, to detect copy number alterations (CNAs). SynthEx uses a “synthetic-normal” strategy to correct for sample-specific bias in target regions due to pre-analytical variation between tumor-normal matched pairs. It employs a synthetic normal to mimic the technical bias of the tumor to be assayed. This tool utilizes whole exome sequencing (WES) data with improved precision and accuracy.
An R package, designed for estimating tumor copy number profile, tumor cellularity, tumor ploidy, based on whole genome sequencing (WGS) or whole exome sequencing (WES) data. sCNAphase integrates haplotype-specific allele counts together with total read depth in a Hidden Markov model (HMM) that explicitly models both tumor DNA purity and ploidy.
Detects common DNA events (recurrent CNVs) across individuals. JointSLM is an algorithm extending univariate shifting level model (SLM). This application can be used for identifying small shifts in the signals, identifies boundaries of common DNA events as well as for analyzing multiple tumor samples data for the discovery of recurrent copy number alterations.
Infers subclonal copy number alterations (CAN) and loss of heterozygosity (LOH) segments from whole-genome sequencing (WGS) data of heterogeneous tumor samples. CLImAT-HET is based on the framework of CLImAT. The software infers subclonal CNA and LOH segments by taking into account the intra-tumor heterogeneity issue, in the case where a reference or matched normal sample is absent. It was evaluated using both simulated and real WGS data.
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.
Features copy number alterations and loss-of-heterozygosity (LOH) events in cancer samples from whole genome sequencing (WGS) data. OncoSNP-SEQ is a standalone software dedicated to the determination of specific genomic variation based on the use of allele-specific information. It can assess levels of contamination by normal cells, enable for degrees of intra-tumor heterogeneity and polyploidy.
A simple and robust algorithm to infer purity, ploidy and absolute copy numbers in whole numbers for tumor cells from sequencing data. A simulation study shows that estimates have reasonable accuracy, and that the algorithm is robust against the presence of segmentation errors and subclonal populations.
A software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. The segmentation algorithm is implemented in MATLAB, and copy number assignment is implemented Perl.
An interactive tool for mining large copy number datasets. Copy Number Explorer facilitates rapid visual and statistical identification of recurrent regions of gain or loss, identifies the genes most likely to drive CNA formation using the cghMCR method and identifies recurrently broken genes that may be disrupted or fused. The software tool also allows users to identify recurrent CNA regions that may be associated with differential survival.
Enables copy number profiling and downstream analyses in disease genetic studies. MARATHON is a pipeline that gathers statistical software: CODEX and CODEX2 perform read depth normalization for total copy number profiling, iCNV receives read depth normalized by CODEX/CODEX2, FALCON and FALCON-X perform allele-specific copy number (ASCN) analysis and Canopy receives input from FALCON/FALCON-X to perform tumor phylogeny reconstruction. The pipeline adapts to different study designs and research goals.
A parallelized R package for an integral copy number analysis of high-throughput sequencing cancer data. seqCNA includes novel methodology on (i) filtering, reducing false positives, and (ii) GC content correction, improving copy number profile quality, especially under great read coverage and high correlation between GC content and copy number. Adequate analysis steps are automatically chosen based on availability of paired-end mapping, matched normal samples and genome annotation. seqCNA provides accurate copy number predictions in tumoural data, thanks to the extensive filtering and better GC bias correction, while providing an integrated and parallelized workflow.
Infers the cellular prevalences of subclonal populations. MixClone is a probabilistic mixture model that infers tumor subclonal populations by integrating sequence information gathered from somatic copy number alterations (SCNAs) and heterozygous single nucleotide polymorphism (SNP) sites. The software does not require deep sequencing data. It was applied on a breast cancer sequencing dataset and discovered events not reported before.
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.
Permits users to identify, characterize and quantify somatic copy number aberration (SCNAs) from cancer genome sequencing. SomatiCA is an application that was developed to analyze tumor samples with contamination and/or heterogeneity by accounting for tumor purity and subclonality. It also reduces false positive rate in the segmentation. It has been implemented as four functional modules in R: initial segmentation, estimation of somatic ratio with segmentation refinement, adjusting for admixture rate and subclonality characterization.
An accurate, sensitive and easy to use tool in detecting cancer-specific SCNAs using short-read sequence data. In addition to cancer, COPS can be used for any disease as long as sequence reads from both disease and normal samples from the same individual are available. An added boundary segmentation detection module makes COPS detected SCNA boundaries more specific for the samples studied.
A method for identification of copy number alterations in a tumor genome compared to its matched control, based on application of Smith-Waterman algorithm to single-end sequencing data. In a performance test with simulated data, rSW-seq shows >90% sensitivity and >90% precision in detecting a single copy number change that contains approximately 500 reads for the normal sample. With 100-bp reads, this corresponds to a ~50 kb region for 1X genome coverage of the human genome.