SeSiMCMC 4.36 – Dig For DNA Motifs Gibbs Sampler

SeSiMCMC 4.36

:: DESCRIPTION

The SeSiMCMC (Sequence Similarities by Markov Chain Monte Carlo) algorithm finds DNA motifs of unknown length and complicated structure, such as direct repeats or palindromes with variable spacers in the middle in a set of unaligned DNA sequences. It uses an improved motif length estimator and careful Bayesian analysis to consider site absence in a sequence.

::DEVELOPER

Alexander Favorov

:: SCREENSHOTS

N/A

:: REQUIREMENTS

WIndows / Linux

:: DOWNLOAD

SeSiMCMC

:: MORE INFORMATION

Citation:

A Gibbs sampler for identification of symmetrically structured, spaced DNA motifs with improved estimation of the signal length.
Favorov AV, Gelfand MS, Gerasimova AV, Ravcheev DA, Mironov AA, Makeev VJ.
Bioinformatics. 2005 May 15;21(10):2240-5. Epub 2005 Feb 22.

DMINDA 2.0 – DNA Motif Identification and Analyses

DMINDA 2.0

:: DESCRIPTION

DMINDA is an integrated web server for DNA motif identification and analyses

::DEVELOPER

Qin Ma , Bioinformatic and Mathematical Biosciences Lab, The Ohio State University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation:

Yang J, Chen X, McDermaid A, Ma Q.
DMINDA 2.0: integrated and systematic views of regulatory DNA motif identification and analyses.
Bioinformatics. 2017 Aug 15;33(16):2586-2588. doi: 10.1093/bioinformatics/btx223. PMID: 28419194.

DMINDA: an integrated web server for DNA motif identification and analyses.
Ma Q, Zhang H, Mao X, Zhou C, Liu B, Chen X, Xu Y.
Nucleic Acids Res. 2014 Jul;42(Web Server issue):W12-9. doi: 10.1093/nar/gku315.

LRMotifs 1.0 – Logistic Regression-Based DNA Motif Discovery

LRMotifs 1.0

:: DESCRIPTION

LRMotifs is a novel method of DNA sequence motif discovery based on logistic regression and rigorous hypothesis testing.

::DEVELOPER

David M Simcha dsimcha@gmail.com

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

LRMotifs

:: MORE INFORMATION

Citation

PLoS One. 2012;7(11):e47836. doi: 10.1371/journal.pone.0047836. Epub 2012 Nov 7.
The limits of de novo DNA motif discovery.
Simcha D1, Price ND, Geman D.

Epigram 0.004 – Predicting Human Epigenome from DNA Motifs

Epigram 0.004

:: DESCRIPTION

Epigram is an analysis pipeline that predicts histone modification and DNA methylation patterns from DNA motifs.

::DEVELOPER

Wei Wang’s group

:: SCREENSHOTS

N/A

::REQUIREMENTS

Linux

:: DOWNLOAD

Epigram

:: MORE INFORMATION

Citation

Nat Methods. 2015 Mar;12(3):265-72. doi: 10.1038/nmeth.3065. Epub 2014 Sep 21.
Predicting the human epigenome from DNA motifs.
Whitaker JW, Chen Z, Wang W

DistAMo 1.0 – Distribution Analysis of DNA Motifs

DistAMo 1.0

:: DESCRIPTION

DistAMo is a versatile tool for analyzing motif distributions in bacteria, archaea and viruses. It allows for an analysis of motif over/underrepresentation from the level of single genes to the level of whole replicons.

::DEVELOPER

Bioinformatics and Systems Biology, Justus-Liebig-University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows / MacOsX
Java

:: DOWNLOAD

DistAMo

:: MORE INFORMATION

Citation

DistAMo: A Web-Based Tool to Characterize DNA-Motif Distribution on Bacterial Chromosomes.
Sobetzko P, Jelonek L, Strickert M, Han W, Goesmann A, Waldminghaus T.
Front Microbiol. 2016 Mar 11;7:283. doi: 10.3389/fmicb.2016.00283.

ChIPMunk v8 – DNA Motif Digger

ChIPMunk v8

:: DESCRIPTION

ChIPMunk is an iterative algorithm that combines greedy optimization with bootstrapping and uses coverage profiles as motif positional preferences. ChIPMunk does not require truncation of long DNA segments and it is practical for processing up to tens of thousands of data sequences. Comparison with traditional (MEME) or ChIP-Seq-oriented (HMS) motif discovery tools shows that ChIPMunk identifies the correct motifs with the same or better quality but works dramatically faster.

::DEVELOPER

Boeva lab & Ilya Vorontsov and Ivan Kulakovskiy [ivan-dot-kulakovskiy-at-gmail-dot-com].

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / Mac OsX
Java

:: DOWNLOAD

ChIPMunk

:: MORE INFORMATION

Citation:

Kulakovskiy IV, Boeva VA, Favorov AV, Makeev VJ
Deep and wide digging for binding motifs in ChIP-Seq data.
Bioinformatics 2010 Oct 15;26(20):2622-3. Epub 2010 Aug 24

STAMP 1.1 – Tool-kit for DNA Motif Comparison

STAMP 1.1

:: DESCRIPTION

STAMP is a newly developed web server that is designed to support the study of DNA-binding motifs. STAMP may be used to query motifs against databases of known motifs; the software aligns input motifs against the chosen database (or alternatively against a user-provided dataset), and lists of the highest-scoring matches are returned. Such similarity-search functionality is expected to facilitate the identification of transcription factors that potentially interact with newly discovered motifs.

:: DEVELOPER

Benos Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

STAMP

:: MORE INFORMATION

Citation:

S Mahony, PV Benos,
“STAMP: a web tool for exploring DNA-binding motif similarities“,
Nucleic Acids Research (2007) 35(Web Server issue):W253-W258.

kmerHMM 20120625 – DNA Motif Elucidation

kmerHMM 20120625

:: DESCRIPTION

To discover DNA motifs on PBM data, a computational pipeline using Hidden Markov Model (HMM) has been proposed and named, kmerHMM. The method has been compared with the state-of-the-arts methods on well-studied datasets. The results demonstrated the effectiveness of the proposed approach. In addition, belief propagations have been applied to reveal its multimodal motif discovery ability validated by wet-lab experiments.

::DEVELOPER

The Zhang Lab, University of Toronto

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Weindows/Linux/MacOsX
R package /MatLab

:: DOWNLOAD

kmerHMM

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Sep;41(16):e153. doi: 10.1093/nar/gkt574. Epub 2013 Jun 29.
DNA motif elucidation using belief propagation.
Wong KC1, Chan TM, Peng C, Li Y, Zhang Z.

MONKEY 2.0 – Identify Matches to DNA Motifs in Multiple Alignments

MONKEY 2.0

:: DESCRIPTION

MONKEY is a set of programs designed to search alignments of non-coding DNA sequence for matches to matrices representing the sequence specificity of transcription factors.MONKEY employs probabilistic models of factor specificity and binding-site evolution, on which basis we compute the likelihood that putative sites are conserved and assign statistical significance to each hit.

::DEVELOPER

Alan Moses’ Computational Biology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

MONKEY

:: MORE INFORMATION

Citation

Moses et. al.
MONKEY: identifying conserved transcription-factor binding sites in multiple alignments using a binding site-specific evolutionary model
Genome Biol. 2004;5(12):R98