Copy number variation visualization software tools | High-throughput sequencing data analysis
Copy number variation (CNV) is a major component of structural differences between individual genomes. The recent emergence of population-scale whole-genome sequencing (WGS) datasets has enabled genome-wide CNV delineation. However, molecular validation at this scale is impractical, so visualization is an invaluable preliminary screening approach when evaluating CNVs. Standardized tools for visualization of CNVs in large WGS datasets are therefore in wide demand.
Enables users to explore large, integrated genomic datasets. IGV provides next-generation sequencing (NGS) data visualization and provides features for identification of sequencing and analysis artifacts, leading to errant single-nucleotide variant (SNV) calls, as well as support for viewing large-scale structural variants (SV) detected by paired-end read technology. The software also includes features to support third-generation long-read sequencing technologies. Several IGV features have been developed to aid manual review of aligned reads.
Detects copy number variations (CNVs) with high resolution. PennCNV is an integrated hidden Markov model (HMM) method that incorporates the population allele frequency for each single nucleotide polymorphism (SNP) and the distance between adjacent SNPs. This application was developed specifically for data generated on the Illumina Infinium platform, but it can be extended to other similar SNP genotyping platforms.
Allows users to display various biological data such as genotypes or copy number variation. Genatomy is a standalone software leaning on the exploitation of a repository gathering information related to sequence, gene ontology (GO) annotations and more to assist users in analyzing the displayed data. This application can be used to investigate genetic interactions between quantitative trait locus (QTL).
Permits visualization and annotation of copy number variations (CNVs) analysis result. ShinyCNV assists users in identifying reliable CNVs and adjusting inaccurate segment boundaries. The software can facilitate the identification of commonly affected CNV regions from a group of samples, and the visual checking of whether important focal gains/losses are missing from reported CNVs. It was designed for users with limited experience in programming.
A software tool for normalized visualization, statistical scoring, and annotation of CNVs from population-scale whole-genome sequencing (WGS) datasets. CNView has six sequential steps: (1) matrix filtering, (2) matrix compression, (3) intra-sample normalization, (4) inter-sample normalization, (5) coverage visualization, and (6) genome annotation. CNView surmounts challenges of sequencing depth variability between individual libraries by locally adapting to cohort-wide variance in sequencing uniformity at any locus. Importantly, CNView is broadly extensible to any reference genome assembly and most current WGS data types.
Represents absolute copy number and copy neutral variations of groups of samples. aCNViewer recognizes recurrent events through three different graphical outputs: dendrograms, bi-dimensional heatmaps, and stacked histograms. It can’t account for intra-tumor heterogeneity or compare two groups of samples. This tool is efficient in datasets from Affymetrix single nucleotide polymorphism (SNP) arrays as well as whole exome sequencing (WES)/whole genome sequencing (WGS) data.
A user-friendly tool with functions specifically designed to facilitate the process of interactively visualizing and editing somatic CNV calling results. Different from other general genomics viewers, the index and display of CNV calling results in cnvCurator is segment central. It incorporates multiple CNV-specific information for concurrent, interactive display, as well as a number of relevant features allowing user to examine and curate the CNV calls. cnvCurator provides important and practical utilities to assist the manual review and edition of results from a chosen somatic CNV caller, such that curated CNV segments will be used for down-stream applications.