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GATK / Genome Analysis ToolKit
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Focuses on variant discovery and genotyping. GATK provides a toolkit, developed at the Broad Institute, composed of several tools and ables 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.
CESAM / Cis Expression Structural Alteration Mapping
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.
Control-FREEC / Control-FREE Copy number and allelic content caller
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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.
JointSLM
An algorithm that extend the univariate SLM to the multivariate case in order to detect recurrent shifts in the mean of multiple sequential processes. The resolution of JointSLM strictly depends on the signal to noise ratio (SNR) of the data: increasing the SNR of DOC data by reducing the sequencing error rate or augmenting the coverage of the sequencing experiments, will improve the performance of JointSLM in detecting small shifts in the signals. The JointSLM algorithm can be also used to analyse multiple tumour samples data for the discovery of recurrent copy number alterations.
MARATHON / copy nuMber vARiAtion and Tumor pHylOgeNy
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.
seqCNA
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.
readDepth
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.
SomatiCA
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.
iGC
Identifies differentially expressed genes driven by Copy Number Alterations (CNA) from samples with both gene expression and CNA data. iGC supports multiple input formats and users can define their own criteria for identifying differentially expressed genes driven by CNAs. In addition to microarray datasets, next-generation sequencing (NGS) data can be analyzed. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions.
Segmentum
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.
VCF2CNA
Detects copy number alteration (CNA). VCF2CNA is a web-based tool capable of accurate and efficient detection of CNAs from variants called from high-coverage wide genome sequencing (WGS) data sequenced on various platforms. It consists of two main modules: (i) single nucleotide polymorphism (SNP) information retrieval and processing from the input data and (ii) recursive partitioning–based segmentation using SNP allele counts. This web app is robust to library construction artifacts and captures medium to large CNA segments with high.
CopyCat
Detects copy number aberrations by measuring the depth of coverage obtained by massively parallel sequencing of the genome. CopyCat achieves higher accuracy than many other packages, and runs quickly by utilizing multi-core architectures to parallelize the processing of these large data sets. Other features include 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.
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