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BIC-seq

forum (1)
Combines normalization of the data at the nucleotide level and Bayesian information criterion-based segmentation to detect both somatic and germline copy number variations (CNVs) accurately. Whereas the first version in 2011 used a control genome for normalization and was thus limited to identification of somatic variants, the new version is also applicable to germline variants and is more robust overall. BIC-seq2 first normalizes the sequencing data by taking into account the GC-content, the nucleotide composition of the short reads and the mappability. It then performs segmentation and detects CNVs based on the normalized data using a Bayesian information criterion. Unlike other algorithms, BIC-seq2 performs normalization at a nucleotide level rather than at a large bin level, resulting in its high sensitivity of detection for small CNVs. If there is a control genome or a sample was sequenced on multiple runs, we perform normalization individually for each data set and perform joint segmentation for CNV detection. Analysis of simulation data showed that this method outperforms existing methods.

cn.MOPS / Copy number estimation by a Mixture Of PoissonS

A data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.MOPS. It models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.MOPS decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively.

fastMitoCalc

Estimates Mitochondrial DNA (mtDNA) copy number highly accurately using 0.1% of the genome. fastMitoCalc is an application that takes advantage of the indexing of sequencing alignment files, focusing on a small subset of the nuclear genome to estimate autosomal DNA coverage accurately. It analyzes hundreds of thousands of genomes currently being sequenced by large research consortia, and facilitates association studies of mtDNA copy number with quantitative trait values or nuclear variants.

SeqCNV

Uses capture next-generation sequencing (NGS) data. SeqCNV identifies the copy number ratio and copy number variant (CNV) boundary by extracting the read depth information and utilizing the maximum penalized likelihood estimation (MPLE) model. It was applied to both bacterial artificial clone (BAC) and human patient NGS data to identify CNVs. It shows a significant improvement in both sensitivity and specificity. The tool appears to be a robust way to identify CNVs of different size using capture NGS data.

FACETS / Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing

An allele-specific copy number analysis (ASCN) tool and open-source software with a broad application to whole genome, whole-exome, as well as targeted panel sequencing platforms. FACETS provides a complete analysis pipeline that include BAM file post-processing steps including library size and GC-normalization, joint segmentation of total and allele-specific signals, and integer copy number calls taking into account of tumor purity, ploidy and clonal heterogeneity, all seamlessly integrated in a single workflow with comprehensive output, integrated visualization, with fast computation to facilitate large-scale application. FACETS provides a complete ASCN analysis pipeline. This is distinct from most existing methods which often require separate software packages for GC-normalization, sequencing bias adjustment and/or segmentation analysis. An integrated analysis pipeline from start to finish will provide more consistent results.

GENSENG

A read-depth-based method that uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, GENSENG outperforms existing read-depth-based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis.

HadoopCNV

Infers copy number changes through both allelic frequency and read depth information. HadoopCNV enables multiple compute nodes to work in parallel. It is at least 6 times faster than CNVnator in term of speed. The tool has a slightly better performance than CNVnator, especially for large deletions. It uses read depth information and alternative allele frequency information, and integrates them into a single coherent model for the most powerful detection of copy number variations (CNVs).

Canvas

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.

GenomeCAT

Allows analysis and integrative visualization of copy number variants (CNVs). GenomeCAT is a standalone application that provides comprehensive tools for the analysis of DNA CNVs. The software facilitates the evaluation of their biological relevance in the context of genome annotations and results obtained from different experiment types. Moreover, it can act as an interface to other software tools since results generated in GenomeCAT can be exported in standard file formats.

iCNV / integrated Copy Number Variation caller

Allows copy number variation (CNV) detection. iCNV can be applied in whole exome sequencing (WES) only, whole genome sequencing (WGS) only, single-nucleotide polymorphism (SNP) array only, or any combination of SNP and sequencing data. It uses high throughput sequencing data, allowing for integration of SNP-array data. This tool utilizes B-allele frequency information from sequencing data, which is valuable for CNV detection and exact copy number inference.

PureCN

Estimates tumor purity, copy number, loss of heterozygosity (LOH), and status of single nucleotide variants (SNVs). PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection pipelines, and has support for tumor samples without matching normal samples. It integrates standard GATK-based pipelines, utilizes standard Bioconductor infrastructure for data import and export, supports both matched and unmatched samples, and was tested on targeted panels.

TrioCNV

A tool designed to jointly detecting copy number variations (CNVs) from whole genome sequencing data in parent-offspring trios. TrioCNV models read depth signal with the negative binomial regression to accommodate over-dispersion and considered GC content and mappability bias. It leverages parent-offspring relationship to apply Mendelian inheritance constraint while allowing for the rare incidence of de novo events. It uses a hidden Markov model (HMM) by combining the two aforementioned models to jointly perform CNV segmentation for the trio.

ADM-CNV

Identifies copy number variations (CNVs) from raw next-generation sequencing (NGS) profiles. ADM-CNV is a method that formulates the read-depth (RD) signal reconstruction problem into a quadratic minimization problem involving two constraints. The software can separate CNVs from other variations in wide data types, including synthetic, simulated and empirical sequencing data. It requires three user-defined parameters, including cut-off value for breakpoint detection, the window size in which the read counts are calculated, and the overlap length of each sliding window.

PopSV

Enables the systematic detection of Copy-Number Variations (CNVs) across the genome. PopSV is an analytical method which detects abnormal read coverage using a set of samples as references. The software detects Abnormal Read-Depth signal by using a population of samples as reference, and this population view allows to interrogate the whole genome, including regions of low mappability. The software also detects any divergence from the reference samples, even if the signal is incomplete and provides functions for pre/post-processing.

SV-AUTOPILOT / Structural Variation AUTOmated PIpeLine Optimization Tool

Obsolete
Standardizes the Structural Variation (SV) detection pipeline. SV-AUTOPILOT is a pipeline that can be used on existing computing infrastructure in the form of a Virtual Machine (VM) Image. It provides a “meta-tool” platform for using multiple SV-tools, to standardize benchmarking of tools, and to provide an easy, out-of-the-box SV detection program. In addition, the user can choose which of several alignment algorithms is used in their analysis.

PDAC Disease Models / Pancreatic Ductal AdenoCarcinoma Disease Models

Provides a pipeline for analyzing whole-genome sequencing (WGS) data for simple somatic mutation (SSM), structural variation (SV) and copy number variation (CNV). PDAC Disease Models is a R package which provide a way for reproducing an experiment leads on Pancreatic Ductal Adenocarcinoma (PDAC) across ten primary-patient-derived xenografts (PDX) pairs, six metastasis-PDX pairs and five primary-PDX- patient-derived organoids (PDO) trios.

SRBreak / Split-read and Read-depth based Breakpoints

Identifies approximate breakpoints in the detection of copy-number variable regions (CNVRs). SRBreak combines read-depth and split-read information to infer breakpoints, using information from multiple samples to allow an imputation approach to be taken. It uses split-read information directly from CIGAR strings of BAM files, without using a re-alignment step. The tool is able to report breakpoints for very low-coverage samples including those for which only single-end reads were available.

GROM-RD

Obsolete
Analyzes multiple biases in read coverage to detect CNVs in NGS data. To overcome a typical weakness of read depth (RD) methods, GROM-RD employs a CNV search using size-varying overlapping windows to improve breakpoint resolution. GROM-RD was able to improve RD sensitivity, specificity, and breakpoint accuracy compared to CNVnator and RDXplorer, the two most frequently used RD algorithms. Additionally, GROM-RD had a short run time that was relatively insensitive to read coverage indicating excellent scalability of the method for different datasets.

QuicK-mer

A unified pipeline for estimating genome copy-number from high-throughput Illumina sequencing data. QuicK-mer utilizes the Jellyfish application to efficiently tabulate counts of predefined sets of k-mers. The program performs GC-normalization using defined control regions and reports paralog-specific estimates of copy-number suitable for downstream analysis. This approach is mapping-free and relies upon efficient tabulation of read depth at predefined sets of informative k-mers.

SVS / SNP and Variation Suite

Allows to perform complex analyses and visualizations on genomic and phenotypic data. SVS provides a set of tools to (1) empower quickly and easily perform quality-assurance and statistical tests for genetic association studies, (2) perform genetic prediction including various means of defining the relationship between samples, (3) validate models and visualize the results, (4) identify regions of copy number variability, (5) perform statistical tests on the copy number results and others.

ERDS / Estimation by Read Depth with Single-nucleotide variants

Obsolete
An open-source software tool free to academia and nor-profit organization, designed for inferring copy number variants (CNVs) in high-coverage human genomes using next generation sequence (NGS) data. When a CNV presents in a test genome, multiple signatures, weak or strong, would present in the alignment data. ERDS starts from read depth (RD) information, and integrates other information including paired end mapping (PEM) and soft-clip signature to call CNVS sensitively and accurately.