Tag: Cancer Genome

NetSig – Network-based Discovery from Cancer Genomes

NetSig

:: DESCRIPTION

NetSig is a robust statistic software that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates.

::DEVELOPER

Lage Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

NetSig

:: MORE INFORMATION

Citation

Horn H, Lawrence MS, Chouinard CR, Shrestha Y, Hu JX, Worstell E, Shea E, Ilic N, Kim E, Kamburov A, Kashani A, Hahn WC, Campbell JD, Boehm JS, Getz G, Lage K.
NetSig: network-based discovery from cancer genomes.
Nat Methods. 2018 Jan;15(1):61-66. doi: 10.1038/nmeth.4514. Epub 2017 Dec 4. PMID: 29200198; PMCID: PMC5985961.

APOLLOH 0.1.1 – HMM for Profiling Loss of Heterozygosity in Whole Cancer Genome Shotgun Sequencing data

APOLLOH 0.1.1

:: DESCRIPTION

APOLLOH is a hidden Markov model (HMM) for predicting somatic loss of heterozygosity and allelic imbalance in whole tumour genome sequencing data.

::DEVELOPER

Shah Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX / Windows
  • Matlab

:: DOWNLOAD

 APOLLOH

:: MORE INFORMATION

Citation

G. Ha, A. Roth, D. Lai, A. Bashashati, J. Ding, R. Goya, R. Giuliany, J. Rosner, A. Oloumi, K. Shumansky, S.-F. Chin, G. Turashvili, M. Hirst, C. Caldas, M. A. Marra, S. Aparicio, S. P. Shah.
Integrative analysis of genome-wide loss of heterozygosity and mono-allelic expression at nucleotide resolution reveals disrupted pathways in triple negative breast cancer.
Genome Research 2012, 2012 Oct;22(10):1995-2007.

EBCall – Empirical Bayesian framework for Mutation Detection from Cancer Genome Sequencing data

EBCall

:: DESCRIPTION

EBCall (Empirical Bayesian mutation Calling) is a software package for somatic mutation detection (including InDels). EBCall uses not only paired tumor/normal sequence data of a target sample, but also multiple non-paired normal reference samples for evaluating distribution of sequencing errors, which leads to an accurate mutaiton detection even in case of low sequencing depths and low allele frequencies.

::DEVELOPER

Yuichi Shiraishi (yshira@hgc.jp) ,Kenichi Chiba

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • samtools
  • R package
  • C++ Compiler
  • The VGAM package for R

:: DOWNLOAD

 EBCall

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Apr;41(7):e89. doi: 10.1093/nar/gkt126. Epub 2013 Mar 6.
An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data.
Shiraishi Y, Sato Y, Chiba K, Okuno Y, Nagata Y, Yoshida K, Shiba N, Hayashi Y, Kume H, Homma Y, Sanada M, Ogawa S, Miyano S.

TAGCNA 1.0 – Identify Significant Consensus Events from Copy Number Alterations in Cancer Genome

TAGCNA 1.0

:: DESCRIPTION

TAGCNA is designed to identify significant consensus events from copy number alterations in cancer genome.

::DEVELOPER

Xiguo Yuan: xiguoyuan/at/mail.xidian.edu.cn

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux/ MacOsX
  • R package
:: DOWNLOAD

 TAGCNA

:: MORE INFORMATION

Citation

PLoS One. 2012;7(7):e41082. doi: 10.1371/journal.pone.0041082. Epub 2012 Jul 18.
TAGCNA: a method to identify significant consensus events of copy number alterations in cancer.
Yuan X, Zhang J, Yang L, Zhang S, Chen B, Geng Y, Wang Y.

SAIC – Identify Significant Consensus Aberrations in Cancer Genome

SAIC

:: DESCRIPTION

SAIC (Significant Aberrations in Cancer) is a software to identify significant consensus aberrations in cancer genome.

::DEVELOPER

Computational Bioinformatics & Bio-imaging Laboratory (CBIL)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • C Compiler

:: DOWNLOAD

 SAIC

:: MORE INFORMATION