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Detects motifs in large scale chromatin-immunoprecipitation (ChIP) data. Trawler is a program that can be run according two different manners: (i) a standalone version providing a pipeline that generates position weight matrices (PWMs) from the extraction and clustering of over-represented motifs; and (ii) a web application supplying the possibility to submit sequences in both FASTA or BED format, to rank predicted motifs by conservation score as well as to produce a set of background sequences.
DME / Discriminating Matrix Enumerator
A program that discovers transcription factor binding site motifs in nucleotide sequences. DME identifies motifs, represented as position weight matrices, that are overrepresented in one set of sequences relative to another set. The ability to directly optimize relative overrepresentation is a unique feature of DME, making DME an ideal tool for analyzing promoters of transcripts found to have differential expression in a particular context. The optimization procedure is based on an enumerative algorithm that is guaranteed to identify optimal motifs from a discrete space of matrices with a specific lower bound on information content. This strategy scales very well with the number and length of the sequences used, and is well-suited to analyzing very large data sets.
CREAD / Comprehensive Regulatory Element Analysis and Discovery
A framework for studying regulatory elements in a genome. Currently focusing on patterns involved in transcriptional regulation, CREAD includes efficient tools for performing fundamental tasks in motif discovery and regulatory sequence analysis. CREAD also includes code libraries to facilitate the implementation of new tools. In addition to fundamental tools, CREAD includes an implementation of the MARS machine learning algorithm, and a Suffix Tree implementation designed for repeated searching of large amounts of sequence data using position-weight matrices, a common representation for transcription-factor binding-sites.
TRAP / Transcription factor Affinity Prediction
Determines the total affinity of a sequence for a given transcription factor, thus removing the need for a threshold value. TRAP ranks all promoter sequences of a genome on the basis of their overall affinity for that factor to proceed. It can serve to estimate the most enriched factor into a given sequence, the sequences with the highest affinity for a factor of interest, or the binding sites of a factor affected by the given single nucleotide polymorphisms (SNPs).
Melina II
A web-based tool for promoter analysis. Melina II shows potential DNA motifs in promoter regions with a combination of several available programs, Consensus, MEME, Gibbs sampler, MDscan and Weeder, as well as several parameter settings. It allows running a maximum of four programs simultaneously, and comparing their results with graphical representations. In addition, users can build a weight matrix from a predicted motif and apply it to upstream sequences of several typical genomes (human, mouse, S. cerevisiae, E. coli, B. subtilis or A. thaliana) or to public motif databases (JASPAR or DBTBS) in order to find similar motifs.
A de novo motif discovery method that is able to directly optimize the statistical significance of PWMs. XXmotif can also score conservation and positional clustering of motifs. The XXmotif server provides (i) a list of significantly overrepresented motif PWMs with web logos and E-values; (ii) a graph with color-coded boxes indicating the positions of selected motifs in the input sequences; (iii) a histogram of the overall positional distribution for selected motifs and (iv) a page for each motif with all significant motif occurrences, their P-values for enrichment, conservation and localization, their sequence contexts and coordinates.
Decomposes a set of DNA sequences into the most probable dictionary of motifs or words. This method is applicable to any set of DNA sequences: for example, all upstream regions in a genome or all genes expressed under certain conditions. Identification of words is based on a probabilistic segmentation model in which the significance of longer words is deduced from the frequency of shorter ones of various lengths, eliminating the need for a separate set of reference data to define probabilities.
Finds conserved sequence motifs within coding regions. SiteSifter is based on the assumption that DNA sequences with a regulatory function should be evolutionarily conserved at the nucleotide sequence level over and above any conservation required to maintain the amino acid sequence of the encoded proteins. The software scores each instance of a motif on the basis of the chance that its constituent codons are conserved over and above that required for amino acid conservation.
Virtual Footprint
Allows recognition of single or composite DNA patterns. Virtual Footprint is a pattern matching tool that enables users to predict genome-based regulons and to analyze of individual promoter regions. The software offers searches of complex DNA patterns in whole bacterial genomes. Results are directly linked to the genome browser GBpro and can thus be visualized in their genomic context. Virtual Footprint, which is a PRODORIC subtool, is also available as part of PRODORIC2, but only the most essential options are offered.
Co-Bind / COperative BINDing
An algorithm for discovering DNA target sites for cooperatively acting transcription factors. Co-Bind utilizes a Gibbs sampling strategy to model the cooperativity between two transcription factors and defines position weight matrices for the binding sites. Sequences from both the training set and the entire genome are taken into account, in order to discriminate against commonly occurring patterns in the genome, and produce patterns which are significant only in the training set.
CSTP / Condition-Specific Target Prediction
A tool to predict condition-specific targets for transcription factors (TFs) from expression data measured by either microarray or RNA-seq. Based on the philosophy of guilt by association, CSTP infers the regulators of each studied gene by recovering the regulators of its co-expressed genes. In contrast to the currently used methods, CSTP does not insist on binding sites of TFs in the promoter of the target genes. CSTP was applied to three independent biological processes for evaluation purposes.
Pattern locator
Finds local sequence patterns in long DNA sequences. Pattern Locator uses an intuitive syntax for pattern description. It allows combinations of specific nucleotide sequences, direct and inverted repeats, variable length tandem repeats of subpatterns, and a specified number of errors in any part of the pattern. The tool is not intended for finding distant direct or inverted repeats. Pattern Locator emphasizes the ease of use and utilizes an intuitive syntax for pattern description.
Contains a lot of sequence analysis algorithms, including methods for 1) motif statistics, e.g. compute the exact occurrence count distribution of a motif, 2) exact motif discovery: extraction of motifs with provably optimal p-value, 3) analysis of pattern matching algorithms: compute (for given algorithm and pattern) the exact distribution of the number of character accesses caused by searching a random text, 4) statistics of fragment masses resulting from proteolytic cleavage of proteins, 5) computing the expectated read length of sequencing reads for a given dispensation order (for 454 or IonTorrent) and 6) analysing sensitivity of spaced alignment seeds.
Tmod / Toolbox of Motif Discovery
Provides a unified interface to ease the use of these programs and help users to understand the tuning parameters. The current version of Tmod integrates 12 widely used motif discovery programs: MDscan, BioProspector, AlignACE, Gibbs Motif Sampler, MEME, CONSENSUS, MotifRegressor, GLAM, MotifSampler, SeSiMCMC, Weeder and YMF. It allows plug-in motif-finding programs to run either separately or in a batch mode with predetermined parameters, and provides a summary comprising of outputs from multiple programs.
Provides a platform for de novo regulatory motif detection using a stochastic motif detection algorithm with various motif assessment tools. The suite revolves around MotifSampler, a de novo motif detection tool based on Gibbs sampling that searches for an overrepresented motif in a set of coregulated input sequences. In addition to the core sampler, MotifSuite provides tools to automatically merge the results of multiple stochastic sampling runs and to perform downstream analyses.
A transcription factor (TF)-generalized classifier based on local DNA shape parameters that improves PWM-based transcription factor binding site (TFBS) prediction. regshape predicts whether a short (8–32 bp) DNA sequence from the noncoding genome is a TFBS for any TF, or whether it is a non-binding-site sequence. This generic classifier is based on a novel procedure for extracting sequence length-independent features from bp-level DNA shape parameters within the binding site.
Supports both alignment-free and alignment-based motif discovery in the promoter sequences of related species. Putative motifs are exhaustively enumerated as words over the IUPAC alphabet and screened for conservation using the branch length score. Additionally, a confidence score is established in a genome-wide fashion. In order to take advantage of a cloud computing infrastructure, the MapReduce programming model is adopted. The method is applied to four monocotyledon plant species and it is shown that high-scoring motifs are significantly enriched for open chromatin regions in Oryza sativa and for transcription factor binding sites inferred through protein-binding microarrays in Oryza sativa and Zea mays.
A motif finding method which constructs a subspace based on the covariance of numerical DNA sequences. When a candidate sequence is projected into the modeled subspace, a threshold in the Q-residuals confidence allows us to predict whether this sequence is a binding site. Using the TRANSFAC and JASPAR databases, we compared our Q-residuals detector with existing PSSM methods. In most of the studied TF binding sites, the Q-residuals detector performs significantly better and faster than MATCH and MAST. As compared with Motifscan, a method which takes into account interdependences, the performance of the Q-residuals detector is better when the number of available sequences is small.
PARSEC / PAtteRn Search and Contextualization
An open source platform for guided discovery, allowing localization and biological characterization of short genomic sites in entire eukaryotic genomes. PARSEC can search for a sequence or a degenerated pattern. The retrieved set of genomic sites can be characterized in terms of (i) conservation in model organisms, (ii) genomic context (proximity to genes) and (iii) function of neighboring genes. These modules allow the user to explore, visualize, filter and extract biological knowledge from a set of short genomic regions such as transcription factor binding sites.
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