MITSU 1.0 – Stochastic EM for Transcription Factor Binding Site Motif Discovery

MITSU 1.0

:: DESCRIPTION

MITSU (Motif discovery by ITerative Sampling and Updating) is a command line application for the discovery of transcription factor binding site (TFBS) motifs.

::DEVELOPER

Alastair M Kilpatrick

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /  MacOsX / Windows
  • Java
  • BioJava

:: DOWNLOAD

 MITSU

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Jun 15;30(12):i310-i318. doi: 10.1093/bioinformatics/btu286.
Stochastic EM-based TFBS motif discovery with MITSU.
Kilpatrick AM, Ward B, Aitken S.

BayesMotif 1.0 – Sorting Motif Discovery using Bayes Classifiers

BayesMotif 1.0

:: DESCRIPTION

BayesMotif is a de novo identification algorithm for finding a common type of protein sorting motifs in which a highly conserved anchor is present along with a less conserved motif regions.

::DEVELOPER

Machine Learning and Evolution Laboratory (MLEG)

:: SCREENSHOTS

n/a

:: REQUIREMENTS

  • Web Browser
:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2010 Jan 18;11 Suppl 1:S66. doi: 10.1186/1471-2105-11-S1-S66.
BayesMotif: de novo protein sorting motif discovery from impure datasets.
Hu J1, Zhang F.

BLSSPELLER 1.0 – Exhaustive Comparative Motif Discovery

BLSSPELLER 1.0

:: DESCRIPTION

The BLSSpeller software provides 3 main functionalities to the user: de novo comparative motif discovery, pattern matching (with BLS cutoff) and target prediction.

::DEVELOPER

Dieter De Witte Email: dieter.dewitte@intec.ugent.be

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

BLSSpeller

:: MORE INFORMATION

Citation:

De Witte D, Van De Velde J, Decap D, Van Bel M, Audenaert P, Demeester P, Dhoedt B, Vandepoele K, Fostier J.
BLSSpeller: Exhaustive comparative motif discovery of conserved cis-regulatory elements.
Bioinformatics. 2015 Aug 8. pii: btv466.

W-ChIPMotifs – de novo Motif Discovery from ChIP-based High throughput data

W-ChIPMotifs

:: DESCRIPTION

W-ChIPMotifs is a web application tool for de novo motif discovery from ChIP-based high throughput data.

::DEVELOPER

Jin Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

W-ChIPMotifs: a web application tool for de novo motif discovery from ChIP-based high-throughput data.
Jin VX, Apostolos J, Nagisetty NS, Farnham PJ.
Bioinformatics. 2009 Dec 1;25(23):3191-3.

mCUDA-MEME 3.0.16 – Motif Discovery software based on MEME

mCUDA-MEME 3.0.16

:: DESCRIPTION

mCUDA-MEME is a further extension of CUDA-MEME based on MEME algorithm for mutliple GPUs using a hybrid combination of CUDA, MPI and OpenMP.

::DEVELOPER

Liu, Yongchao

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • CUDA toolkits and SDK 2.0 or higher.

:: DOWNLOAD

   CUDA-MEME / mCUDA-MEME

:: MORE INFORMATION

Citation:

Yongchao Liu, Bertil Schmidt, Weiguo Liu, Douglas L. Maskell:
CUDA-MEME: accelerating motif discovery in biological sequences using CUDA-enabled graphics processing units“.
Pattern Recognition Letters, 2010, 31(14): 2170 – 2177

Yongchao Liu, Bertil Schmidt, Douglas L. Maskell:
An ultrafast scalable many-core motif discovery algorithm for multiple GPUs“.
10th IEEE International Workshop on High Performance Computational Biology (HiCOMB 2011), 2011, 428-434

EXTREME 2.0 – EM algorithm for Motif Discovery

EXTREME 2.0

:: DESCRIPTION

EXTREME is an online EM implementation of the MEME model for fast motif discovery in large ChIP-Seq and DNase-Seq Footprinting data

::DEVELOPER

CBCL Lab (Computational Biology and Computational Learning) @ UCI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/MacOsX
  • Java
  • Python

:: DOWNLOAD

   EXTREME

:: MORE INFORMATION

Citation

EXTREME: an online EM algorithm for motif discovery.
Quang D, Xie X.
Bioinformatics. 2014 Mar 10

TEISER 1.0 – de-novo Motif Discovery tool for Finding Informative Structural Elements in RNA

TEISER 1.0

:: DESCRIPTION

TEISER (Tool for Eliciting Informative Structural Elements in RNA) is a robust and powerful framework for discovering post-transcriptional regulatory elemenets.

::DEVELOPER

Tavazoie lab at Columbia University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX
  • Perl

:: DOWNLOAD

 TEISER

:: MORE INFORMATION

Citation:

Nature. 2012 Apr 8;485(7397):264-8. doi: 10.1038/nature11013.
Systematic discovery of structural elements governing stability of mammalian messenger RNAs.
Goodarzi H, Najafabadi HS, Oikonomou P, Greco TM, Fish L, Salavati R, Cristea IM, Tavazoie S.

FIRE-pro 1.1 – Motif Discovery and Characterization program in Proteins based on Mutual Information

FIRE-pro 1.1

:: DESCRIPTION

FIRE-pro (Finding Informative Regulatory Elements in proteins) is a motif discovery and characterization program for proteins based on mutual information

::DEVELOPER

Tavazoie lab at Columbia University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX / Windows
  • Perl

:: DOWNLOAD

 FIRE-pro

:: MORE INFORMATION

Citation:

PLoS One. 2010 Dec 29;5(12):e14444. doi: 10.1371/journal.pone.0014444.
Large-scale discovery and characterization of protein regulatory motifs in eukaryotes.
Lieber DS, Elemento O, Tavazoie S.

FIRE 1.1a – Motif Discovery and Characterization program based on Mutual Information

FIRE 1.1a

:: DESCRIPTION

FIRE (Finding Informative Regulatory Elements) is a motif discovery and characterization program based on mutual information

::DEVELOPER

Tavazoie lab at Columbia University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX / Windows
  • Perl

:: DOWNLOAD

 FIRE

:: MORE INFORMATION

Citation:

Mol Cell. 2007 Oct 26;28(2):337-50.
A universal framework for regulatory element discovery across all genomes and data types.
Elemento O, Slonim N, Tavazoie S.

BayesMD – Biological Modeling for Motif Discovery

BayesMD

:: DESCRIPTION

BayesMD is a flexible, fully Bayesian model for motif discovery consisting of motif, background and alignment modules. BayesMD can be customized to different kind of biological applications, e.g. microarray, ChIP-chip, ditag, CAGE data analysis by integrating appropriately chosen features and functionalities.

::DEVELOPER

BayesMD team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX/ Windows
  • Matlab

:: DOWNLOAD

  BayesMD

:: MORE INFORMATION

Citation:

J Comput Biol. 2008 Dec;15(10):1347-63. doi: 10.1089/cmb.2007.0176.
BayesMD: flexible biological modeling for motif discovery.
Tang MH, Krogh A, Winther O.