DIPS program implements an algorithm to find PWM motifs that discriminate two sets of DNA sequences (high counts in one set and low counts in other set).
PhyME (Phylogenetic Motif Elicitation) is a software tool for finding motifs in sets of orthologous sequences.PhyME is an ab initio motif-finding algorithm, which finds overrepresented motifs in input sequences while accounting for their evolutionary conservation in orthologs of those sequences.
PhyME discovers motifs by integrating two important aspects of the motif’s significance, overrepresentation and interspecies conservation, into one probabilistic score. The algorithm is based on multiple alignment and expectation-maximization.
S. Sinha, “PhyME: a software tool for finding motifs in sets of orthologous sequences”, Methods Mol. Biol., vol. 395 (2007) 309-18. Pubmed 17993682.
S. Sinha, M. Blanchette, M. Tompa, “PhyME: a probabilistic algorithm for finding motifs in sets of orthologous sequences”, BMC Bioinformatics, vol. 5(2004) 170. Pubmed 15511292.
CoMoFinder strives to discover reliable composite network motifs in co-regulatory networks which consist of microRNAs, transcriptional regulators and genes.
DynaMIT is a flexible platform for sequence and structure motifs integration, providing the means to execute multiple motif search tools, integrate their output and display the obtained results in a plethora of different ways.
MOST+ is a fast MOTIF finding tool(MOtif finding by Suffix tree and heterogeneous Tags). It extracts distribution features of nearby epigenomic markers, like histone modification or nucleosome occupancy, to help de novo find motif, thus rendering a higher level of accuracy on characterizing motif (cross-validated by ChIP-seq data) and more co-factors.
RNASampler is a program that predicts common RNA secondary structure motifs in a group of related sequences.RNASampler finds the common structures between two sequences by probabilistically sampling aligned stems based on stem conservation calculated from intrasequence base pairing probabilities and intersequence base alignment probabilities. It iteratively updates these probabilities based on sampled structures and subsequently recalculates stem conservation using the updated probabilities. The iterative process terminates upon convergence of the sampled structures.
::DEVELOPER
Stormo Lab in Department of Genetics, Washington University
ALIDOT (ALIgned DOT-plots) detects conserved secondary structure elements in relatively small sets of RNAs by combining multiple sequence alignments and secondary structure predictions. Both a (good) sequence alignment and predicted secondary structure predictions for each sequence in the alignment must be provided as inputs. alidot works either with predicted mfe structures, or with base pairing probability matrices.
The program alidot and the associated perl scripts are part of the Vienna RNA Package.
MEME (Multiple Em for Motiv Elicitation) is a tool for discovering motifs in a group of related DNA or protein sequences.A motif is a sequence pattern that occurs repeatedly in a group of related protein or DNA sequences. MEME represents motifs as position-dependent letter-probability matrices which describe the probability of each possible letter at each position in the pattern. Individual MEME motifs do not contain gaps. Patterns with variable-length gaps are split by MEME into two or more separate motifs.MEME takes as input a group of DNA or protein sequences and outputs as many motifs as requested. MEME uses statistical modeling techniques to automatically choose the best width, number of occurrences, and description for each motif.
MCAST searches sequences for clusters of matches to one or more nucleotide motifs
The MEME Suite.
Bailey TL, Johnson J, Grant CE, Noble WS.
Nucleic Acids Res. 2015 May 7. pii: gkv416.
Timothy L. Bailey, Mikael Bodén, Fabian A. Buske, Martin Frith, Charles E. Grant, Luca Clementi, Jingyuan Ren, Wilfred W. Li, William S. Noble,
“MEME SUITE: tools for motif discovery and searching“,
Nucleic Acids Research, 37:W202-W208, 2009
The Protein Sequence Motif Extractor reads a fasta file or tab delimited file containing protein sequences, then looks for the specified motif in each protein sequence. Results are stored in a new file containing the regions of the protein that contain the specified motif. The default output format is a new fasta file named _Motifs.fasta, but you can alternatively use /T to specify that a tab-delimited text file be created.