ReadDepth 0.9.8.4 – Detects Copy Number Aberrations in Deep Sequencing Data

ReadDepth 0.9.8.4

:: DESCRIPTION

The readDepth package for R can detect copy number aberrations by measuring the depth of coverage obtained by massively parallel sequencing of the genome. It achieves higher accuracy than many other packages, and runs much faster by utilizing multi-core architectures to parallelize the processing of these large data sets.

::DEVELOPER

the Bioinformatics Research Laboratory at Baylor College of Medicine

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ Windows/ MacOsX
R package

:: DOWNLOAD

ReadDepth

:: MORE INFORMATION

Citation

Miller CA, Hampton O, Coarfa C, Milosavljevic A, 2011
ReadDepth: A Parallel R Package for Detecting Copy Number Alterations from Short Sequencing Reads.
PLoS ONE 6(1): e16327. doi:10.1371/journal.pone.0016327

CNIT 5.1 – Copy Number Inferring tool

CNIT 5.1

:: DESCRIPTION

CNIT is designed for Affymetrix GeneChip to analyze copy number of each SNP allele. CNIT can be applicable in chromosome-abnormal disease, cancer and copy number variation studies, and can provide accurate CN estimations with low false-positive rate.

::DEVELOPER

Cathy S.J. Fann lab,Institute of Biomedical Informatics, National Yang-Ming University, Taipei

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows
R package

:: DOWNLOAD

CNIT

:: MORE INFORMATION

Citation

Genome-wide copy number analysis using copy number inferring tool (CNIT) and DNA pooling.
Lin CH, Huang MC, Li LH, Wu JY, Chen YT, Fann CS.
Hum Mutat. 2008 Aug;29(8):1055-62

CNVineta 1.0-1 – Data mining tool for large case-control copy number variation data sets

CNVineta 1.0-1

:: DESCRIPTION

CNVineta is a flexible data mining tool for the analysis of copy number variations (CNVs) in large case-control SNP array data sets. The tool is available as an R statistical package. CNVineta offers a flexible and fast access to CNVs by a quick graphical overview in large case-control datasets. In addition, CNVineta provides rapid access to the log2 of raw data ratios (LRR) and B-allele frequencies (BAF) of specific or all samples, thereby allowing for a fast verification of the underlying raw data. CNVineta is also equipped with analysis methods for genome-wide screening for associated rare as well as common CNVs. Hence, CNVineta is a unique data mining tool to rapidly explore CNVs in large case-control data sets.

::DEVELOPER

Institute for Clinical Molecular Biology

:: SCREENSHOTS

N/A

::REQUIREMENTS

Linux/Windows
R Package

:: DOWNLOAD

CNVineta

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Sep 1;26(17):2208-9. Epub 2010 Jul 6.
CNVineta: a data mining tool for large case-control copy number variation datasets.
Wittig M, Helbig I, Schreiber S, Franke A.

AscatNGS 4.4.1 – Somatic Copy Number analysis using WGS paired end wholegenome sequencing

AscatNGS 4.4.1

:: DESCRIPTION

AscatNGS contains the Cancer Genome Projects workflow implementation of the ASCAT copy number algorithm for paired end sequencing.

::DEVELOPER

CASM IT

:: SCREENSHOTS

N/a

:: REQUIREMENTS

Linux / MacOsX / Windows
Perl

:: DOWNLOAD

AscatNGS

:: MORE INFORMATION

Citation

Raine KM, Van Loo P, Wedge DC, Jones D, Menzies A, Butler AP, Teague JW, Tarpey P, Nik-Zainal S, Campbell PJ.
ascatNgs: Identifying Somatically Acquired Copy-Number Alterations from Whole-Genome Sequencing Data.
Curr Protoc Bioinformatics. 2016 Dec 8;56:15.9.1-15.9.17. doi: 10.1002/cpbi.17. PMID: 27930809; PMCID: PMC6097604.

PRINCE v2.3 – VNTR Copy Number Approximation

PRINCE v2.3

:: DESCRIPTION

PRINCE (Processing Reads to Infer the Number of Copies via Estimation) estimates Variable Number Tandem Repeats (VNTR) copy number from raw next generation sequencing (NGS) data.

::DEVELOPER

Computational Methods for Paleogenomics and Comparative Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
conda

:: DOWNLOAD

PRINCE

:: MORE INFORMATION

Citation

Mehrdad Mansouri and Julian Booth and Margaryta Vityaz and Cedric Chauve and Leonid Chindelevitch
PRINCE: Accurate Approximation of the Copy Number of Tandem Repeats
18th International Workshop on Algorithms in Bioinformatics (WABI 2018)

genoCN 1.09 – Identify Copy Number States and Genotype Calls.

genoCN 1.09

:: DESCRIPTION

GenoCN is a software that simultaneously identify copy number states and genotype calls. Different strategies are implemented for the study of Copy Number Variations (CNVs) and Copy Number Aberrations (CNAs). While CNVs are naturally occurring and inheritable, CNAs are acquired somatic alterations most often observed in tumor tissues only. CNVs tend to be short and more sparsely located in the genome compared to CNAs. GenoCN consists of two components, genoCNV and genoCNA, designed for CNV and CNA studies, respectively. In contrast to most existing methods, genoCN is more flexible in that the model parameters are estimated from the data instead of being decided a priori. genoCNA also incorporates two important strategies for CNA studies. First, the effects of tissue contamination are explicitly modeled. Second, if SNP arrays are performed for both tumor and normal tissues of one individual, the genotype calls from normal tissue are used to study CNAs in tumor tissue.

::DEVELOPER

Wei Sun

:: SCREENSHOTS

N/A

::REQUIREMENTS

Linux/Windows/MacOsX
R Package
C Compiler

:: DOWNLOAD

genoCN

:: MORE INFORMATION

Citation

Sun, W., Wright , F., Tang, Z.Z., Nordgard , S.H., Van Loo, P., Yu, T., Kristensen, V., Perou, C.,
Integrated study of copy number states and genotype calls using high density SNP arrays.
Nucleic Acids Res. 2009, 37(16), 5365-77

HiNT v2.2.7 – Hi-C for Copy Number Variation and Translocation Detection

HiNT v2.2.7

:: DESCRIPTION

HiNT is a computational method to detect CNVs and Translocations from Hi-C data.

::DEVELOPER

Peter Park’s lab at CBMI, Harvard Medical School

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows
Perl
R package
Python

:: DOWNLOAD

HiNT

:: MORE INFORMATION

Citation

Wang S, Lee S, Chu C, Jain D, Kerpedjiev P, Nelson GM, Walsh JM, Alver BH, Park PJ.
HiNT: a computational method for detecting copy number variations and translocations from Hi-C data.
Genome Biol. 2020 Mar 23;21(1):73. doi: 10.1186/s13059-020-01986-5. PMID: 32293513; PMCID: PMC7087379.

BIC-seq 2 0.2.4 – Copy Number analysis from Whole-genome Sequencing data

BIC-seq 2 0.2.4

:: DESCRIPTION

BIC-seq can accurately and efficiently identify CNVs via minimizing the Bayesian information criterion.Using BIC-seq, we identified hundreds of CNVs as small as 40 bp in the cancer genome sequenced at 10× coverage, whereas we could only detect large CNVs (> 15 kb) in the array comparative genomic hybridization profiles for the same genome.

::DEVELOPER

Peter Park’s lab at CBMI, Harvard Medical School

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX / Windows
R package / Perl

:: DOWNLOAD

BIC-seq

:: MORE INFORMATION

Citation

Xi et al,
Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion
PNAS, 2011 November 15, 2011 vol. 108 no. 46

rSW-seq – Detection of Copy Number Alterations in Deep Sequencing data

rSW-seq

:: DESCRIPTION

rSW-seq is designed to identify copy number alterations between tumor-vs-matched normal genomes (or between normal-vs-normal genomes for CNV detection) from deep sequencing data generated by next-generation sequencing. Compared to other algorithms (BreakDancer or MoDIL) using PEM (paired-end mapping) signatures, rSW-seq uses ‘read-depth’ as primary measure, which can be applied to single-end sequencing read set.

::DEVELOPER

Tae-Min Kim . , Peter Park’s lab at CBMI, Harvard Medical School

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX / Windows

:: DOWNLOAD

rSW-seq

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2010 Aug 18;11:432.
rSW-seq: algorithm for detection of copy number alterations in deep sequencing data.
Kim TM, Luquette LJ, Xi R, Park PJ.

CONSERTING – Copy Number Segmentation by Regression Tree in Next Generation Sequencing

CONSERTING

:: DESCRIPTION

CONSERTING (Copy Number Segmentation by Regression Tree in Next Generation Sequencing) is an accurate method for detecting somatic DNA copy number variation in whole genome sequencing data.

::DEVELOPER

Zhang (Jinghui Zhang) Lab,St. Jude Children’s Research Hospital

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX
R package

:: DOWNLOAD

CONSERTING

:: MORE INFORMATION