Tag: Somatic Mutation

Cake 1.0 – Somatic Mutation Discovery

Cake 1.0

:: DESCRIPTION

Cake is a bioinformatics software pipeline that integrates four publicly available somatic variant-calling algorithms to identify single nucleotide variants with higher sensitivity and accuracy than any one algorithm alone.

::DEVELOPER

Adams Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Perl

:: DOWNLOAD

 Cake

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Sep 1;29(17):2208-10. doi: 10.1093/bioinformatics/btt371. Epub 2013 Jun 25.
Cake: a bioinformatics pipeline for the integrated analysis of somatic variants in cancer genomes.
Rashid M1, Robles-Espinoza CD, Rust AG, Adams DJ.

MosaicForecast – Identification of Somatic Mutation from bulk Whole-genome Sequencing data

MosaicForecast

:: DESCRIPTION

MosaicForecast is machine learning method that leverages read-based phasing and read-level features to accurately detect mosaic SNVs (SNPs, small indels) from NGS data. It builds on existing algorithms to achieve a multifold increase in specificity.

::DEVELOPER

Peter Park’s lab at CBMI, Harvard Medical School

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R
  • Python

:: DOWNLOAD

MosaicForecast

:: MORE INFORMATION

Citation

Dou Y, Kwon M, Rodin RE, Cortés-Ciriano I, Doan R, Luquette LJ, Galor A, Bohrson C, Walsh CA, Park PJ.
Accurate detection of mosaic variants in sequencing data without matched controls.
Nat Biotechnol. 2020 Mar;38(3):314-319. doi: 10.1038/s41587-019-0368-8. Epub 2020 Jan 6. PMID: 31907404; PMCID: PMC7065972.

SCHISM 1.1.3 – Subclonal Hierarchy Inference from Somatic Mutations

SCHISM 1.1.3

:: DESCRIPTION

SCHISM is a computational tool designed to infer subclonal hierarchy and the tumor evolution from somatic mutations. The inference process involves computational assessment of two fundamental properties of tumor evolution: lineage precedence rule and lineage divergence rule.

::DEVELOPER

Karchin Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

SCHISM

:: MORE INFORMATION

Citation

Niknafs N, Beleva-Guthrie V, Naiman DQ, Karchin R.
SubClonal Hierarchy Inference from Somatic Mutations: Automatic Reconstruction of Cancer Evolutionary Trees from Multi-region Next Generation Sequencing.
PLoS Comput Biol. 2015 Oct 5;11(10):e1004416. doi: 10.1371/journal.pcbi.1004416. PMID: 26436540; PMCID: PMC4593588.

CHASM 3.0 / CHASMplus 1.0 – Cancer-specific High-throughput Annotation of Somatic Mutations

CHASM 3.0 / CHASMplus 1.0

:: DESCRIPTION

CHASM is a method that predicts the functional significance of somatic missense mutations observed in the genomes of cancer cells, allowing mutations to be prioritized in subsequent functional studies, based on the probability that they give the cells a selective survival advantage.

CHASMplus is a machine learning method that accurately distinguishes between driver and passenger missense mutations, even for those found at low frequencies or are cancer type-specific.

::DEVELOPER

Karchin Lab

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux
  • MySQL Server
  • Python module MySQLdb

:: DOWNLOAD

CHASMplus

:: MORE INFORMATION

Citation

Tokheim C, Karchin R.
CHASMplus Reveals the Scope of Somatic Missense Mutations Driving Human Cancers.
Cell Syst. 2019 Jul 24;9(1):9-23.e8. doi: 10.1016/j.cels.2019.05.005. Epub 2019 Jun 12. PMID: 31202631; PMCID: PMC6857794.

CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer.
Wong WC, Kim D, Carter H, Diekhans M, Ryan MC, Karchin R.
Bioinformatics. 2011 Aug 1;27(15):2147-8. doi: 10.1093/bioinformatics/btr357. Epub 2011 Jun 17.

HapMuC 1.0 – Somatic Mutation Caller

HapMuC 1.0

:: DESCRIPTION

HapMuC is a somatic mutation caller, which can utilize the information of heterozygous germline variants near candidate mutations.

::DEVELOPER

Naoto Usuyama

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • GCC
  • Boost
  • SAMtools
  • BEDTools

:: DOWNLOAD

 HapMuC

:: MORE INFORMATION

Citation

HapMuC: somatic mutation calling using heterozygous germline variants near candidate mutations.
Usuyama N, Shiraishi Y, Sato Y, Kume H, Homma Y, Ogawa S, Miyano S, Imoto S.
Bioinformatics. 2014 Aug 14. pii: btu537

mutationSeq 4.3.9 – Detect Somatic Mutation from Tumour/normal DNA Pair

mutationSeq 4.3.9

:: DESCRIPTION

mutationSeq is a software suite using feature-based classifiers for somatic mutation prediction from paired tumour/normal next-generation sequencing data. mutationSeq has the advantages of integrating different features (e.g., base qualities, mapping qualities, strand bias, and tailed distance features), and validated somatic mutations to make predictions. Given paired normal/tumour bam files, mutationSeq will output the probability of each candidate site being somatic.

::DEVELOPER

Shah Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 mutationSeq

:: MORE INFORMATION

Citation

Bioinformatics. 2012 Jan 15;28(2):167-75. doi: 10.1093/bioinformatics/btr629.
Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.
Ding J, Bashashati A, Roth A, Oloumi A, Tse K, Zeng T, Haffari G, Hirst M, Marra MA, Condon A, Aparicio S, Shah SP.

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

:: 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)

Somatic mutation FDR – FDR Calculation for Somatic Mutations

Somatic mutation FDR

:: DESCRIPTION

Somatic mutation FDR – FDR Calculation for Somatic Mutations

::DEVELOPER

The Institute for Translational Oncology and Immunology(TrOn)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R package

:: DOWNLOAD

  Somatic mutation FDR

:: MORE INFORMATION

Citation

PLoS Comput Biol. 2012;8(9):e1002714. doi: 10.1371/journal.pcbi.1002714. Epub 2012 Sep 27.
Confidence-based somatic mutation evaluation and prioritization.
Löwer M, Renard BY, de Graaf J, Wagner M, Paret C, Kneip C, Türeci O, Diken M, Britten C, Kreiter S, Koslowski M, Castle JC, Sahin U.

MUFFINN – Cancer Gene Discovery via Network Analysis of Somatic Mutation data

MUFFINN

:: DESCRIPTION

MUFFINN (MUtations For Functional Impact on Network Neighbors) is a method for prioritizing cancer genes accounting for not only for mutations of individual genes but also those of neighbors in functional networks

::DEVELOPER

Network Biomedicine Laboratory at Yonsei University, Korea

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

MUFFINN

:: MORE INFORMATION

Citation

MUFFINN: cancer gene discovery via network analysis of somatic mutation data.
Cho A, Shim JE, Kim E, Supek F, Lehner B, Lee I.
Genome Biol. 2016 Jun 23;17(1):129. doi: 10.1186/s13059-016-0989-x.

SomaticSeq 3.3.0 – Accurately Detect Somatic Mutations

SomaticSeq 3.3.0

:: DESCRIPTION

SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions.

::DEVELOPER

Roche Sequencing Solutions

:: SCREENSHOTS

N/a

:: REQUIREMENTS

  • Linux
  • Python
  • R

:: DOWNLOAD

SomaticSeq

:: MORE INFORMATION

Citation

Genome Biol. 2015 Sep 17;16:197. doi: 10.1186/s13059-015-0758-2.
An ensemble approach to accurately detect somatic mutations using SomaticSeq.
Li Tai Fang, et al.