The DiNAMO software implements an exhaustive algorithm to detect over-represented IUPAC motifs in a set of DNA sequences. It has two modes: scanning mode, where all windows are parsed, or fixed-position mode, where only motifs occurring at a specific position in the sequences are taken into account.
COMET (Cluster Of Motifs E-value Tool) finds statistically significant clusters of motifs in a DNA sequence. The motifs are represented using 4 x L matrices, which record the frequencies of the nucleotides A, C, G, and T at each position in the motif.
The project AhoPro was created to seach for overrepresentation of given motifs in DNA sequences and to search for motif cooccurrence. This could discover the synergy of transcription factors (TF), which usually takes place in regulatory modules of genes.
CAGEd-oPOSSUM is a web-based tool used to detect the over-representation of transcription factor binding sites (TFBS) in regions containing transcription start sites (TSS) derived from Cap Analysis of Gene Expression (CAGE) peaks. It applies the methods of over-representation analysis from our oPOSSUM tool to CAGE derived TSSs.
ACME (Advanced parallel motif extractor) is an advanced parallel motif extractor. ACME arranges the search space in contiguous blocks that take advantage of the cache hierarchy in modern architectures, and achieves almost an order of magnitude performance gain in serial execution.
Given a set of input genomic regions, MotifScan scans the sequences to detect the occurrences of known motifs. It can also applies a statistical test on each motif to check whether the motif is significantly over- or under-represented (enriched or depleted) in the input genomic regions compared to another set of control regions.
Haystack is a suite of computational tools implemented in a Python 2.7 package called haystack_bio to study epigenetic variability, cross-cell-type plasticity of chromatin states and transcription factors (TFs) motifs providing mechanistic insights into chromatin structure, cellular identity and gene regulation.
HMS (hybrid motif sampler) implements a novel computational algorithm specifically designed for transcription factor binding sites (TFBS) motif discovery using ChIP-Seq data. HMS combines stochastic sampling with determinstic greedy search to achieve rapid and accurate motif pattern identification. In addition, it can identify non-ignorable inter-position dependency inside TFBS motifs.