Copy number variation detection software tools | Whole-genome sequencing data analysis
Copy number variation (CNV) is a common source of genetic variation that has been implicated in many genomic disorders. CNV is a form of structural variation (SV) in the genome. Usually, CNV refers to the duplication or deletion of DNA segments larger than 1 kbp.
Leverages a breakpoint junction library for structural variants (SVs) detection. BreakSeq is an approach that identifies SVs by aligning raw reads directly onto SV breakpoint junctions of the alternative, non-reference, alleles contained in a library. The software can serve for identifying specific SV alleles in personal genomics data. It enables a step towards overcoming reference biases.
Enables discovering and genotyping structural variations using sequencing data. Genome STRiP performs discovery and genotyping of copy number variations (CNVs) by analyzing the data from many samples simultaneously in a population-based framework. The software can discover polymorphisms and produce genotypes. It can be used to find novel structural variations or to genotype known variants in new samples.
Determines complex sets of DNA rearrangements and deletions in cancer genomes. ChainFinder is a program based on a statistically based search rooted in graph theory with the aim of highlighting rearrangements that can possibly permits users to detect coordinate chromosomal alterations. This application starts from a pre-computed model and performs comparisons to detect genomic rearrangements and their associated deletions. This algorithm was tested on prostate tumors.
To characterize the mutational spectrum of somatic SVs in cancer, it is important to identify both simple (e.g., deletion, insertion, and inversion) and complex SVs at base-pair resolution. Meerkat predicts both germline and somatic SVs directly from short read data, focusing on complex events.
Identifies large segmental duplications and deletions. mrCaNaVaR is a package furnishing a copy number caller based on the investigation of whole-genome sequence mapping read depth. It includes features for hiding common and tandem repeats, mapping reads in conjunction with mrFAST/mrsFAST software or building search indexes. It can also be used to determine absolute copy numbers of genomic intervals.
Finds copy number variations (CNVs) from a statistical analysis of mapping density of short reads from next-generation sequencing platforms. CNVnator separates the whole genome into non-overlapping bins of equal size and utilizes the count of mapped reads within each bin as the read-depth (RD) signal. It can be useful for the study of various human and nonhuman 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 using NGS reads with partial alignments to a reference genome to directly map structural variations at the nucleotide level. Application of CREST to whole-genome sequencing data from five pediatric T-lineage acute lymphoblastic leukemias (T-ALLs) and a human melanoma cell line, COLO-829, identified 160 somatic structural variations. Experimental validation exceeded 80%, demonstrating that CREST had a high predictive accuracy.
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.
Identifies structural variants, performs sequence assembly at the breakpoints, and reconstructs the complex structural variants using the long-fragment information from the 10x Genomics platform. GROCSVs is implemented as a multi sample analysis pipeline, allowing the simultaneous analysis of multiple tumor and matched normal samples, or multiple related individuals.
A tool for discovery and genotyping of transposable element variants (TEVs) (also known as mobile element insertions) from next-generation sequencing reads aligned to a reference genome in BAM format. The goal is to call TEVs that are not present in the reference genome but present in the sample that has been sequenced. It should be noted that RetroSeq can be used to locate any class of viral insertion in any species where whole-genome sequencing data with a suitable reference genome is available.
Retrieves somatic and germline copy number variations (CNVs). BIC-seq employs a combination of normalization of the data at the nucleotide level and Bayesian information criterion-based segmentation to work. It is able to handle the GC-content, the nucleotide composition of the short reads and the mappability. This tool can include reads that can be aligned to multiple positions.
Allows users to detect structural mosaicism abnormalities in targeted or whole-genome sequencing (WGS) data. MrMosaic measures deviations in copy number and allele frequency by comparison between depth and B-allele fraction from studied files and randomly chromosomes from the same data. The software was tested with 4,911 whole-exome sequencing (WES) data.
Identifies copy number variation (CNV) using shotgun sequencing. CNV-seq provides a method based on a statistical model derived from aCGH that can be applied to low sequencing coverage. This application is combined to a model to compute the theoretical limit of resolution for given data at a desired confidence level. It was tested on both simulated and real human data.
A computational tool for copy number variants (CNV) detection in whole human genome sequence data using read depth (RD) coverage. CNV detection is based on the Event-Wise Testing (EWT) algorithm. The read depth coverage is estimated in non-overlapping intervals (100bp Windows) across an individual genome based on the pileup generated by SAMTools.
A tool for identification of copy number changes from diverse sequencing experiments including whole-genome matched tumor-normal and single-sample normal re-sequencing, as well as whole-exome matched and unmatched tumor-normal studies. In addition to variant calling, Canvas infers genome-wide parameters such as cancer ploidy, purity and heterogeneity. It provides fast and simple to execute workflows that can scale to thousands of samples and can be easily incorporated into existing variant calling pipelines.
Detects genotype insertions and deletions from paired-end reads. CTK is a suite of tools for next-generation sequencing (NGS) data analysis and is based on an internal segment size approach to discover indel variation from paired-end read data. It contains also, among others, a long-indel-aware read mapper (LASER), a BAM converter to a list of alignment pairs with prior probabilities and a split feature by chromosome.
Allows structural variant (SV) discovery. LUMPY is a general probabilistic SV discovery framework that integrates multiple SV detection signals, including those generated from read alignments or prior evidence. The software is based upon a general probabilistic representation of an SV breakpoint that allows any number of alignment signals to be integrated into a single discovery process. It can detect SV from multiple alignment signals in files from one or more samples. A simplified wrapper for standard analyses, LUMPY Express, can also be executed.
Assists users in handling structural variants (SVs) breakpoints. Hydra-sv confronts various discordant mappings with the aim of enabling the detection, assembly, and interpretation of the mechanics related to these breakpoints. This software can be employed on a genome used for testing to highlight novel DNA junctions or, theoretically, to detect genetic events triggering a breakpoint.
Detects copy number variation (CNV) that enhances the depth-of-coverage with paired-end mapping information. CNVer uses matepairs mapping that are discordant to the reference to pinpoint the presence of variation. This software can minimize the sequencing biases that cause uneven local coverages by utilizing the combining feature included in the donor graph. It can also reconstruct the absolute copy counts of segments of the donor genome and is compatible with low coverage datasets.