DRIM – Discover Motifs in a list of ranked DNA sequences

DRIM

:: DESCRIPTION

DRIM (Discovery of Rank Imbalanced Motifs) is a tool for discovering short motifs in a list of nucleic acid sequences. DRIM was originally developed for DNA sequences and successfully applied on ChIP-chip and CpG methylation data. The current version has enhanced functionality and can be applied for both DNA and RNA. This new version was used to predict UTR motifs and Splicing Factor binding motifs based on RIP-Chip or CLIP data.
From a mathematical point of view, DRIM identifies subsequences that tend to appear at the top of the list more often than in the rest of the list. The definition of TOP in this context is flexible and driven by the data. Explicitly – DRIM identifies a threshold at which the statistical difference between top and rest is maximized. An exact p-value for the optimized event is also provided.

::DEVELOPER

Yakhini Lab and the Mandel-Gutfreund Lab, at the Technion.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 DRIM

:: MORE INFORMATION

Citation

E. Eden, D. Lipson, S. Yogev & Z. Yakhini.
Discovering Motifs in Ranked Lists of DNA Sequences,
PLoS Computational Biology, 2007.

QuateXelero – Fast Motif Detection algorithm

QuateXelero

:: DESCRIPTION

QuateXelero is an extremely fast motif detection algorithm which has a Quaternary Tree data structure in the heart.

::DEVELOPER

Laboratory of Systems Biology & Bioinformatics (LBB)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows

:: DOWNLOAD

 QuateXelero

:: MORE INFORMATION

Citation

PLoS One. 2013 Jul 18;8(7):e68073. doi: 10.1371/journal.pone.0068073. Print 2013.
QuateXelero: an accelerated exact network motif detection algorithm.
Khakabimamaghani S1, Sharafuddin I, Dichter N, Koch I, Masoudi-Nejad A.

MODA – Network Motif Discovery in Biological Networks

MODA

:: DESCRIPTION

MODA is an efficient algorithm for network motif discovery in biological networks.

::DEVELOPER

Laboratory of Systems Biology & Bioinformatics (LBB)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 MODA

:: MORE INFORMATION

Citation

Genes Genet Syst. 2009 Oct;84(5):385-95.
MODA: an efficient algorithm for network motif discovery in biological networks.
Omidi S1, Schreiber F, Masoudi-Nejad A.

CSTP 1.0 – Condition-specific Target Prediction from Motifs and Expression

CSTP 1.0

:: DESCRIPTION

CSTP is a computational tool to predict the TF-target regulation with expression data based on a philosophy of guilt by association. Different from other tools, CSTP does not insist on clear TF binding site in the promoters of target genes.The expression information of genes allows prediction of CSTP to be condition-specific or tissue-specific

::DEVELOPER

Department Computational Molecular Biology, Max Planck Institute for Molecular Genetics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Bioinformatics. 2014 Feb 27
Condition-specific target prediction from motifs and expression.
Meng G1, Vingron M.

Wregex 2.1 – Amino Acid Motif searching

Wregex 2.1

:: DESCRIPTION

Wregex (weighted regular expression).is a software tool for amino acid motif searching.

::DEVELOPER

EhuBio

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 Wregex

:: MORE INFORMATION

Citation:

Bioinformatics. 2014 May 1;30(9):1220-7. doi: 10.1093/bioinformatics/btu016. Epub 2014 Jan 9.
Prediction of nuclear export signals using weighted regular expressions (Wregex).
Prieto G1, Fullaondo A, Rodriguez JA.

PETModule – Motif Module based approach for Enhancer Target Prediction

 

PETModule

:: DESCRIPTION

PETModule is a software developed to find enhancer target gene (ETG) pairs through a motif module based approach. The output of the software is the enhancer target gene pairs with a probability score that measures how likely the predicted target gene is reliable. PETModule only needs enhancer locations to predict their target genes.

::DEVELOPER

Hu Lab – Data Integration and Knowledge Discovery @ UCF

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python
  • Java

:: DOWNLOAD

PETModule

:: MORE INFORMATION

Citation:

Sci Rep. 2016 Jul 20;6:30043. doi: 10.1038/srep30043.
PETModule: a motif module based approach for enhancer target gene prediction.
Zhao C, Li X, Hu H.

MOPAT – Predict recurrent Cis-regulatory Modules from known Motif

MOPAT

:: DESCRIPTION

MOPAT (Motif Pair Tree) identifies CRMs through the identification of motif modules, groups of motifs co-ccurring in multiple CRMs. It can identify orthologous CRMs without multiple alignments. It can also find CRMs given a large number of known motifs.

::DEVELOPER

Data Integration and Knowledge Discovery Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows

:: DOWNLOAD

 MOPAT

:: MORE INFORMATION

Citation:

Hu J, Hu H, Li X.
MOPAT: a graph-based method to predict recurrent cis-regulatory modules from known motifs.
Nucleic Acids Res. 2008; 36(13):4488-97

CENTDIST – Discovery of Co-associated Factors by Motif Distribution

CENTDIST

:: DESCRIPTION

CENTDIST is a novel web-application for identifying co-localized transcription factors around ChIP-seq peaks. Unlike traditional motif scanning program, CENTDIST does not require any user-specific parameters and the background. It automatically learns the best set of parameters for different motifs and ranks them based on the skewness of their distribution around ChIP-seq peaks.

::DEVELOPER

Sung Wing Kin, Ken

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web  Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2011 Jul;39(Web Server issue):W391-9. doi: 10.1093/nar/gkr387. Epub 2011 May 20.
CENTDIST: discovery of co-associated factors by motif distribution.
Zhang Z1, Chang CW, Goh WL, Sung WK, Cheung E.

EMD 1.0 – Ensemble Motif Discovery

EMD 1.0

:: DESCRIPTION

EMD is an ensemble (consensus) algorithm that identifies one or more frequent motifs among multiple sequences.

::DEVELOPER

Kihara Bioinformatics Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Perl

:: DOWNLOAD

 EMD

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2006 Jul 13;7:342.
EMD: an ensemble algorithm for discovering regulatory motifs in DNA sequences.
Hu J1, Yang YD, Kihara D.

Motif-All 1.0 – Discovering All Phosphorylation Motifs

Motif-All 1.0

:: DESCRIPTION

The Motif-All algorithm discovers motifs from a set of phosphorylated sequences P and a much larger set of background sequences N.

::DEVELOPER

Laboratory for Bioinformatics and Computational Biology, HKUST

:: SCREENSHOTS

Motif-All

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • Java

:: DOWNLOAD

  Motif-All

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2011 Feb 15;12 Suppl 1:S22. doi: 10.1186/1471-2105-12-S1-S22.
Motif-All: discovering all phosphorylation motifs.
He Z1, Yang C, Guo G, Li N, Yu W.