MuSE 1.0-RC – Somatic Point Mutation Caller

MuSE 1.0-RC

:: DESCRIPTION

MuSE (Mutation calling using a Markov Substitution model for Evolution) is a novel approach for modeling the evolution of the allelic composition of the tumor and normal tissue at each reference base.

::DEVELOPER

Statistical Bioinformatics Lab, The University of Texas M. D. Anderson Cancer Center

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /MacOsX/Windows
  • C++ Compiler

:: DOWNLOAD

MuSE

:: MORE INFORMATION

Citation

Fan Y, Xi L, Hughes DS, Zhang J, Zhang J, Futreal PA, Wheeler DA, Wang W.
MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data.
Genome Biol. 2016 Aug 24;17(1):178. doi: 10.1186/s13059-016-1029-6. PMID: 27557938; PMCID: PMC4995747.

ELASPIC – Ensemble Learning Approach for Stability Prediction of Interface and Core Mutations

ELASPIC

:: DESCRIPTION

ELASPIC constructs homology models of domains and domain-domain interactions, and uses those models, together with sequential and other features, to predict the energetic impact of a mutation on the stability of a single domain or the affinity between two domains.

::DEVELOPER

Kim Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

ELASPIC

:: MORE INFORMATION

Citation

ELASPIC web-server: proteome-wide structure based prediction of mutation effects on protein stability and binding affinity.
Witvliet D, Strokach A, Giraldo-Forero AF, Teyra J, Colak R, Kim PM.
Bioinformatics. 2016 Jan 21. pii: btw031.

SEMBA – Single-rEsidue Mutational based Binding Affinity

SEMBA

:: DESCRIPTION

SEMBA is a program for analyzing the binding affinity of amyloid proteins. It uses an energy function that computes the Lennard-Jones, Coulomb, and solvation energies
to determine the effect of a mutation on a protein’s stability.

::DEVELOPER

SEMBA team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

 SEMBA

:: MORE INFORMATION

Citation

Probing the binding affinity of amyloids to reduce toxicity of oligomers in diabetes.
Smaoui MR, Orland H, Waldispühl J.
Bioinformatics. 2015 Mar 15. pii: btv143.

BeAtMuSiC 1.0 – Prediction of Binding Affinity Changes upon Mutations

BeAtMuSiC 1.0

:: DESCRIPTION

The BeAtMuSiC program evaluates the change in binding affinity between proteins (or protein chains) caused by single-site mutations in their sequence.

::DEVELOPER

Service de Biomodélisation, Bioinformatique et Bioprocédés (3BIO)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Jul;41(Web Server issue):W333-9. doi: 10.1093/nar/gkt450. Epub 2013 May 30.
BeAtMuSiC: Prediction of changes in protein-protein binding affinity on mutations.
Dehouck Y, Kwasigroch JM, Rooman M, Gilis D.

mGPfusion – Predicting Stability Changes upon Single and Multiple Mutations

mGPfusion

:: DESCRIPTION

mGPfusion is a Gaussian process based method for predicting stability changes upon single and multiple mutations of proteins that complements the available experimental data with large amounts of simulated data.

::DEVELOPER

Computational systems biology group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Matlab

:: DOWNLOAD

mGPfusion

:: MORE INFORMATION

Citation

Bioinformatics, 34 (13), i274-i283 2018 Jul 1
mGPfusion: Predicting Protein Stability Changes With Gaussian Process Kernel Learning and Data Fusion
Emmi Jokinen , Markus Heinonen , Harri Lähdesmäki

DUET – Predicting Effects of Mutations on Protein Stability

DUET

:: DESCRIPTION

DUET is a web server for an integrated computational approach for studying missense mutations in proteins.

::DEVELOPER

Biosig Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach.
Pires DE, Ascher DB, Blundell TL.
Nucleic Acids Res. 2014 Jul;42(Web Server issue):W314-9. doi: 10.1093/nar/gku411.

MOAT v1.0 – Mutations Overburdening Annotations Tool

MOAT v1.0

:: DESCRIPTION

MOAT is a computational system for identifying significant mutation burdens in genomic elements with an empirical, nonparametric method. Taking a set of variant calls and a set of annotations, MOAT calculates which annotations have observed variant counts that are substantially elevated with respect to a distribution of expected variant counts determined by permutation of the input data.

::DEVELOPER

Gerstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

MOAT 

:: MORE INFORMATION

Citation:

Bioinformatics. 2018 Mar 15;34(6):1031-1033. doi: 10.1093/bioinformatics/btx700.
MOAT: efficient detection of highly mutated regions with the Mutations Overburdening Annotations Tool.
Lochovsky L, Zhang J, Gerstein M.

DawnRank 1.2 – Discovering Personalized Driver Mutations in Cancer

DawnRank 1.2

:: DESCRIPTION

DawnRank is an R package that identifies personalized driver mutations for any given patient sample.

::DEVELOPER

Ma Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ MacOsX/Linux
  • R

:: DOWNLOAD

DawnRank

:: MORE INFORMATION

Citation:

Genome Med. 2014 Jul 31;6(7):56. doi: 10.1186/s13073-014-0056-8. eCollection 2014.
DawnRank: discovering personalized driver genes in cancer.
Hou JP, Ma J