Deep neural networks implemented in TensorFlow & Python for predicting whether transcription factors will bind to given DNA sequences
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Updated
Dec 9, 2016 - Python
Deep neural networks implemented in TensorFlow & Python for predicting whether transcription factors will bind to given DNA sequences
Reconstruct a Transcriptional Regulatory Network using the principle of Maximum Entropy.
Screen tf motif within provided regions.
MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets
Build SQLite tables of microRNAs and Transcription Factors-gene Correlations
MONKEY is a set of programs designed to search alignments of non-coding DNA sequence for matches to matrices representing the sequence specificity of transcription factors
Read HOMER motif analysis output in R.
🐛 How to use CENTIPEDE to determine if a transcription factor is bound.
ATAC-Seq Transcription Factor Footprint Discovery and Analysis
Patterns of Binding Targets
Unofficial fork of the DeepSEA deep learning genomics project
Motif discovery for DNA sequences using multiobjective optimization and genetic programming.
Code and resources related to the olfactory regeneration project
CMTCN: A web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks
Build the database file for cRegulome package
🎯 Human transcription factor target genes from 6 databases in convenient R format.
Parse TF motifs from public databases, read into R, and scan using 'rtfbs'.
7C: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs
Code for the PeerJ paper (cRegulome: an R package for accessing microRNA and transcription factor-gene expression correlations in cancer)
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