1 - 17 of 17 results

MoSBAT / Motif Similarity Based on Affinity of Targets

An approach for measuring the similarity of motifs by computing their affinity profiles across a large number of random sequences. We show that MoSBAT successfully associates de novo ChIP-seq motifs with their respective transcription factors (TFs), accurately identifies motifs that are obtained from the same TF in different in vitro assays, and quantitatively reflects the similarity of in vitro binding preferences for pairs of TFs.


Compares one or more motifs against a database of known motifs. Tomtom ranks the motifs in the database and produces an alignment for each significant match. The tool is part of the MEME Suite online platform. It not only provides a numeric score for the match between two motifs, but also an estimate of the statistical significance of the score. Tomtom only supports DNA motifs. To compute the scores, the tool needs to know the frequencies of the letters of the sequence alphabet in the database being searched.


A freely available, extensible, and user-friendly R package for visualizing motif differences. DiffLogo is capable of showing differences between DNA motifs as well as protein motifs in a pair-wise manner resulting in publication-ready figures. In case of more than two motifs, DiffLogo is capable of visualizing pair-wise differences in a tabular form. Here, the motifs are ordered by similarity, and the difference logos are colored for clarity. It enables the illustration and investigation of differences between highly similar motifs such as binding patterns of transcription factors for different cell types, treatments, and algorithmic approaches.


Designs 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. STAMP also automatically builds multiple alignments, familial binding profiles and similarity trees when more than one motif is inputted.

GeMSE / GenoMetric Space Explorer

Allows analysis and visualization of interval-based genomic data. GeMSE implements a set of abstractions for data analysis, exploration and visualization. The software supports primitives for data explorations spanning from select, sort, and discretize clustering, and pattern extraction. It enables interactive analytics (IA), an approach suggested for evaluating processing results and for designing and adapting next-generation sequencing (NGS) data analysis pipelines.

MACRO-APE / Matrix Comparison by Approximate P-value Estimation

Allows computing the Jaccard similarity measure for a pair of Position Weight Matrices (PWMs) with given threshold values. MACRO-APE allows scanning a given collection of matrices for PWMs similar to a given query at given score thresholds or P-value levels. It provides basic utilities to estimate a PWM threshold for a given P-value and vice versa. A two-pass scanning tool, which quickly filters out dissimilar entries and then carefully processes a smaller set of candidate models was implemented in this software. This tool offers a command line version and a web app version.

SPIC / Similarity with Position Information Contents

Serves for column-to-column motif comparison. SPIC is a similarity metric based on column information contents. It calculates a score between the position-specific scoring matrix (PSSM) multiplied by the information content (IC) of one column and the position frequency matrix (PFM) of the other column, and vice versa. It is especially useful for recovering motifs in a database, grouping relevant motifs, merging sub-motifs or redundant motifs, or digging true motifs out of chaos.


Finds neighbouring pairs of specified features in one or more input sequences. twofeat is an Emboss tool. Each feature may be specified by type, name, sense, score, tag/value pairs etc. Their relationship, e.g. their sequence separation or overlap, relative sense and order may also be specified. It writes a standard EMBOSS report file with any such feature pairs identified. By default each pair is written as a single feature but (optionally) they are written in their original form.