GIMSAN: motif-finder with biologically realistic and reliable statistical significance analysis
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Updated
Jan 11, 2016 - C
GIMSAN: motif-finder with biologically realistic and reliable statistical significance analysis
HSEARCH: fast and accurate protein sequence motif search and clustering
Classify time series data using motifs discovered from Sequitur processing of SAX discretized data.
XXmotif: eXhaustive, weight matriX-based motif discovery in nucleotide sequences
Yet Another Model Using Neural Networks for Predicting Binding Preferences of for Test DNA Sequences
Classify transcriptional factor motifs into true motifs and false motifs
Fast Motif Finder
Details application of UniDip to the problem of biologic motif discovery. We find that UniDip is able to preprocess DNA sequences such that MEME is able to find motifs 70% faster.
Motif discovery for DNA sequences using multiobjective optimization and genetic programming.
modified exact discovery of time series motifs
Yet Another Motif Discovery Algorithm
Biplots, Volcano plots, PCA plots, Heatmaps and more Computational Genomics data created and visualized during University of Pittsburgh course, Computational Biology (BIOSC1540), with Dr. Miler Lee
Transceiver Framework: A framework for concurrent multi stage processing of data & MDP: An online motif detector and predictor embedded in the transceiver framework.
A tool to search for motifs within the whole genome or regions of interest
🐳 💻 Docker recipe for meme-suite
An R package for de novo discovery of enriched DNA motifs (e.g. TFBS)
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