splitMixed – Finding in a matched Tumor/normal paired-end reads dataset a consistent explanation

cis-X is a new computational method for the discovery of noncoding regulatory variants in individual cancer genomes that will cause cis-activation of target gene transcription.

::DEVELOPER

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

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

cis-X

:: MORE INFORMATION

SNV-PPILP 1.3 – Refined SNV calling for Tumor data using perfect Phylogenies and ILP

SNV-PPILP 1.3

:: DESCRIPTION

SNV-PPILP is a fast and easy to use tool for refining GATK’s Unified Genotyper SNV calls, for multiple samples assumed to form a phylogeny.

::DEVELOPER

GSA (Genome-scale algorithmics) group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux /Windows/ MacOsX
Python

:: DOWNLOAD

SNV-PPILP

:: MORE INFORMATION

Citation:

SNV-PPILP: Refined SNV calling for tumor data using perfect phylogenies and ILP.
van Rens KE, Mäkinen V, Tomescu AI.
Bioinformatics. 2014 Nov 13. pii: btu755

DOTS-Finder – Driver Oncogenes and Tumor Suppressors Finder

DOTS-Finder

:: DESCRIPTION

DOTS-Finder is a new tool that allows the detection of driver genes through the sequential application of functional and frequentist approaches, and is specifically tailored to the analysis of few tumor samples.

::DEVELOPER

Cancer Genomics and System Biology (CGSB)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

DOTS-Finder

:: MORE INFORMATION

Citation

DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes.
Melloni GE, Ogier AG, de Pretis S, Mazzarella L, Pelizzola M, Pelicci PG, Riva L.
Genome Med. 2014 Jun 10;6(6):44. doi: 10.1186/gm563.

SciClone 1.1 – Infer subclonal Architecture of Tumors

SciClone 1.1

:: DESCRIPTION

SciClone is an R package for inferring the subclonal architecture of tumors.It is the first method that can efficiently detect subclonal populations of neoplastic cells by leveraging somatic mutation data from many samples of a tumor. This capability allows it to detect “cryptic” subclones that cannot be identified from a single primary tumor.

::DEVELOPER

The Genome Institute

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux /Windows/ MacOsX
R

:: DOWNLOAD

SciClone

:: MORE INFORMATION

Citation

in review:
Christopher A. Miller et al.
SciClone: Inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution.

UNDO 1.34.0 – Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions

UNDO 1.34.0

:: DESCRIPTION

UNDO (UNsupervised DecOnvolution) is an R package that can be used to automatically detect cell-specific marker genes located on the scatter radii of mixed gene expressions, estimate cellular proportions in each sample, and deconvolute mixed expressions into cell-specific expression profiles.

::DEVELOPER

Computational Bioinformatics & Bio-imaging Laboratory (CBIL)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / MacOsX
R
Bioconductor

:: DOWNLOAD

UNDO

:: MORE INFORMATION

Citation

UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples.
Wang N, Gong T, Clarke R, Chen L, Shih IM, Zhang Z, Levine DA, Xuan J, Wang Y.
Bioinformatics. 2015 Jan 1;31(1):137-9. doi: 10.1093/bioinformatics/btu607.

PhISCS 1.0 – Tumor Phylogeny Reconstruction via Integrative use of Single Cell and Bulk Sequencing Data

PhISCS 1.0

:: DESCRIPTION

PhISCS is a tool for sub-perfect tumor phylogeny reconstruction via integrative use of single cell and bulk sequencing data.

::DEVELOPER

Lab for Bioinformatics and Computational Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

PhISCS

:: MORE INFORMATION

Citation

Malikic S, Mehrabadi FR, Ciccolella S, Rahman MK, Ricketts C, Haghshenas E, Seidman D, Hach F, Hajirasouliha I, Sahinalp SC.
PhISCS: a combinatorial approach for subperfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data.
Genome Res. 2019 Nov;29(11):1860-1877. doi: 10.1101/gr.234435.118. Epub 2019 Oct 18. PMID: 31628256; PMCID: PMC6836735.

CITUP v0.1.0 – Clonality Inference in Multiple Tumor Samples using Phylogeny

CITUP v0.1.0

:: DESCRIPTION

CITUP is a bioinformatics tool that can be used to infer tumor heterogeneity using multiple samples from a single patient. Given mutational frequencies for each sample, CITUP uses an optimization based algorithm to find the evolutionary tree best explaining the data.

::DEVELOPER

Lab for Bioinformatics and Computational Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python
IBM ILOG CPLEX Optimization Studio

:: DOWNLOAD

CITUP

:: MORE INFORMATION

Citation

Clonality Inference in Multiple Tumor Samples using Phylogeny.
Malikic S, McPherson AW, Donmez N, Sahinalp CS.
Bioinformatics. 2015 Jan 6. pii: btv003.

InfiniumPurify 1.3.1 – Predicting Tumor Purity from Methylation Microarray data

InfiniumPurify 1.3.1

:: DESCRIPTION

InfiniumPurify is a simple but effective method to estimate purities from the DNA methylation 450k array data.

::DEVELOPER

Yufang Qin <yfqin at shou.edu.cn>

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Mac OsX
R

:: DOWNLOAD

InfiniumPurify

:: MORE INFORMATION

Citation:

Qin Y, Feng H, Chen M, Wu H, Zheng X.
InfiniumPurify: An R package for estimating and accounting for tumor purity in cancer methylation research.
Genes Dis. 2018 Feb 21;5(1):43-45. doi: 10.1016/j.gendis.2018.02.003. PMID: 30258934; PMCID: PMC6147081.

Predicting tumor purity from methylation microarray data.
Zhang N, Wu HJ, Zhang W, Wang J, Wu H, Zheng X.
Bioinformatics. 2015 Jun 25. pii: btv370.

SMUG – Somatic Mutation Gleaner for Detecting Tumor Somatic Mutations

SMUG

:: DESCRIPTION

SMUG (Somatic Mutation Gleaner) was developed to effectively detect base substitutions and loss of heterozygosity (LOH) using next-generation sequencing data for normal and tumor tissues. It first screens bam files using walker programs (modules we developed to run under GATK), and then summarizes the results using Perl scripts.

::DEVELOPER

Chun Li, Ph.D.

:: REQUIREMENTS

Linux
Perl

:: DOWNLOAD

SMUG

:: MORE INFORMATION

Citation

Song Z, Long J, He J, Shi J, Shu XO, Cai Q, Zheng W, Li C (2012)
Efficient detection of tumor somatic mutations using next-generation sequencing data.
(to be submitted)