SEME 1.0 – A de novo Motif Finder for ChIP-seq data

SEME 1.0

:: DESCRIPTION

SEME ( Sampling with Expectation maximization for Motif Elicitation) is a de novo motif discovery algorithm  which uses pure probabilistic mixture model to model the motif’s binding features and uses expectation maximization (EM) algorithms to simultaneously learn the sequence motif, position, and sequence rank preferences without asking for any prior knowledge from the user.

::DEVELOPER

Sung Wing Kin, Ken

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

 SEME 

:: MORE INFORMATION

Citation

J Comput Biol. 2013 Mar;20(3):237-48. doi: 10.1089/cmb.2012.0233.
Simultaneously learning DNA motif along with its position and sequence rank preferences through expectation maximization algorithm.
Zhang Z, Chang CW, Hugo W, Cheung E, Sung WK.

NestedMICA 0.8.0 – Motif Finder

NestedMICA 0.8.0

:: DESCRIPTION

NestedMICA is a method for discovering over-represented short motifs in large sets of strings. Typical applications include finding candidate transcription factor binding sites in DNA sequences.

::DEVELOPER

Thomas Down at thomas.down@gurdon.cam.ac.uk.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX
  • Java

:: DOWNLOAD

 NestedMICA

:: MORE INFORMATION

Citation

NestedMICA as an ab initio protein motif discovery tool.
Doğruel M, Down TA and Hubbard TJ
BMC bioinformatics2008;9;19

PhyloGibbs-MP 2.0 – Motif Finder in Cis-regulatory Sequences of DNA

PhyloGibbs-MP 2.0

:: DESCRIPTION

PhyloGibbs-MP is a motif finder to find binding sites for transcription factors in cis-regulatory sequences of DNA.

 

::DEVELOPER

RAHUL SIDDHARTHAN

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows

:: DOWNLOAD

 PhyloGibbs-MP

:: MORE INFORMATION

Citation:

Rahul Siddharthan,
PhyloGibbs-MP: Module Prediction and Discriminative Motif-Finding by Gibbs Sampling“,
PLoS Comput Biol 4(8): e1000156 (2008)

SVMotif 0.1 – A SVM Based Transcription Factor Binding Motif Finder

SVMotif 0.1

:: DESCRIPTION

SVMotif is a mechine learning based motif finder. It can be classified into K-mer enumeration based methods. the evaluation of each possible K-mer is done by Recursive SVM feature selection

::DEVELOPER

Mark KonYue Fan

:: SCREENSHOTS

n/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Java/ Matlab

:: DOWNLOAD

  SVMotif

:: MORE INFORMATION

Citation

Ensemble machine methods for DNA binding
(with Y. Fan, and C. DeLisi),
Machine Learning and Applications 7, M. Wani, et al., eds. IEEE, Washington (2008),709-716.

MULTIPROFILER – Subtle Motif Finder

MULTIPROFILER

:: DESCRIPTION

MULTIPROFILER is a software to detect subtle motifs.

::DEVELOPER

Uri Keich  and Pavel A. Pevzner

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 MULTIPROFILER

:: MORE INFORMATION

Citation:

Bioinformatics. 2002 Oct;18(10):1382-90.
Subtle motifs: defining the limits of motif finding algorithms.
Keich U, Pevzner PA.

SOMBRERO 1.1 – Motif Finder

SOMBRERO 1.1

:: DESCRIPTION

SOMBRERO (The Self-Organizing Map for Biological Regulatory Element Recognition and Ordering) is a motif-finder that is based on the Self-Organizing Map neural network algorithm. In contrast to other probabilistic motif discovery tools, SOMBRERO poses motif-finding as a clustering problem. As such, SOMBRERO simultaneously estimates all motif signals in the input sequences (regulatory signals are separated from others during post-processing), as opposed to estimating each significant signal one-by-one

:: DEVELOPER

Shaun Mahony , Pilib ó Broin (pilib.obroin-at-gmail.com)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/MacOsX/Windows

:: DOWNLOAD

 SOMBRERO

:: MORE INFORMATION

Citation:

S Mahony, A Golden, TJ Smith, PV Benos
Improved detection of DNA motifs using a self-organized clustering of familial binding profiles.” (2005)
Bioinformatics 21(Suppl 1):i283-i291

 

GibbsILR – Motif-finder that optimizes for the incomplete likelihood ratio (ILR)

GibbsILR

:: DESCRIPTION

GibbsILR – Motif-finder that optimizes for the incomplete likelihood ratio (ILR)

:: DEVELOPER

Patrick Ng

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  GibbsILR

:: MORE INFORMATION

Citation:

Patrick Ng, Niranjan Nagarajan, Neil Jones, and Uri Keich.
Apples to apples: improving the performance of motif finders and their significance analysis in the Twilight Zone.
Bioinformatics 2006 22(14):e393-e401

 

CMF – Contrast Motif Finder

CMF

:: DESCRIPTION

CMF (Contrast Motif Finder) Contrast motif finder that finds motifs with differential enrichment between two datasets.CMF aims to take advantage of multiple high quality binding datasets to identify subtle regulatory signals, such as context-dependent motifs, within bound sequences. It is specifically designed to discriminate between two sets of bound sequences and takes into account false positive sites when updating PWMs and other model parameters.

::DEVELOPER

Qing Zhou

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C Compiler

:: DOWNLOAD

 CMF

:: MORE INFORMATION