RDDpred 1.1 – Random Forest RDD Classifier

RDDpred 1.1

:: DESCRIPTION

RDDpred is a condition-specific RNA-editing prediction model from RNA-seq data.

::DEVELOPER

Bio & Health Informatics Lab , Seoul National University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 RDDpred

:: MORE INFORMATION

Citation

RDDpred: a condition-specific RNA-editing prediction model from RNA-seq data.
Kim MS, Hur B, Kim S.
BMC Genomics. 2016 Jan 11;17 Suppl 1:5. doi: 10.1186/s12864-015-2301-y.

pSuc-Lys – Predict Lysine Succinylation sites in Proteins with PseAAC and Ensemble Random Forest Approach

pSuc-Lys

:: DESCRIPTION

The web-server pSuc-Lys used to predict the lysine succinylation in protein.

::DEVELOPER

Xiao Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach.
Jia J, Liu Z, Xiao X, Liu B, Chou KC.
J Theor Biol. 2016 Jan 22. pii: S0022-5193(16)00053-9. doi: 10.1016/j.jtbi.2016.01.020

ABCRF 1.8 – Approximate Bayesian Computation via Random Forests

ABCRF 1.8

:: DESCRIPTION

ABCRF is an R library to perform Approximate Bayesian Computation (ABC) model choice and parameter inference via random forests.

::DEVELOPER

The Computational Biology Institute (IBC)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux /macOsX
  • R

:: DOWNLOAD

ABCRF

:: MORE INFORMATION

Citation

Bioinformatics. 2019 May 15;35(10):1720-1728. doi: 10.1093/bioinformatics/bty867.
ABC random forests for Bayesian parameter inference.
Raynal L, Marin JM, Pudlo P, Ribatet M, Robert CP, Estoup A

HapForest – Forest for Detecting Haplotypes and Interactions

HapForest

:: DESCRIPTION

HapForest implements a forest-based approach to accommodate the haplotype uncertainties and variable importance to sort out significant haplotypes and their interactions in genomewide case-control association studies.

::DEVELOPER

The Collaborative Center for Statistics in Science(C2S2)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux /MacOsX
  • Java

:: DOWNLOAD

 HapForest

:: MORE INFORMATION

Citation:

X. Chen, C.-T. Liu, M. Zhang and H.P. Zhang.
A forest-based approach to identifying gene and gene-gene interactions,
PNAS, 104: 19199–19203, 2007.

SilVA 1.1.1 – Silent Variant Analysis using random Forests

SilVA 1.1.1

:: DESCRIPTION

SilVA (Latin for “forest”) is a tool for the automated harmfulness prediction of synonymous (silent) mutations within the human genome. SilVA bases its predictions on a number of features, including conservation, codon usage, splice sites, splicing enhancers and suppressors, and mRNA folding free energy. Given variants in a VCF file, SilVA will rank the rare synonymous variants according to their predicted harmfulness.

::DEVELOPER

Orion Buske and Michael Brudno

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux
  • Perl
  • Python
  • R package

:: DOWNLOAD

 SilVA

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Aug 1;29(15):1843-50. doi: 10.1093/bioinformatics/btt308.
Identification of deleterious synonymous variants in human genomes.
Buske OJ, Manickaraj A, Mital S, Ray PN, Brudno M.