Find putative transcription factor binding domains

Repozitorijoje saugomi failai, implementuojantys R Shiny aplikaciją ir leidžiantys vertinti įkeltų genominių duomenų kokybę bei vykdyti biologines analizes.

Simple Python parser for MotEvo.

Transcription Factor (TF) binding preference prediction using deep neural networks.

GIMSAN: motif-finder with biologically realistic and reliable statistical significance analysis

Prediction of transcription factor binding based on DNA sequence

Motif discovery for DNA sequences using multiobjective optimization and genetic programming.

Threshold and p-value computations for Position Weight Matrices

Predicting transcription factor-DNA binding from sequence

Code for the BMC Genomics paper (Integrating binding and expression data to predict transcription factors combined function)

Gibbs 3.2 formerly located at http://ccmbweb.ccv.brown.edu/gibbs/gibbs.html

Scripts for motif assessment for HOCOMOCO v10/v11.

A simple genetic algorithm for finding consensus binding sties in DNA sequences in Drosophila

Ⓜ️ Tool for motif conservation analysis

ChIP-seq analysis pipeline encompassing data processing, quality control, alignment, peak calling, annotation and motif analysis.

Pipeline for integration different models of transcription factor binding sites

BiasAway will improve TFBS enrichment analyses and the applied analysis of ChIP-Seq data, particularly for the annotation of reliable TFBSs within ChIP-Seq peaks.

Implementation of BPNet, a base-resolution convolutional neural network for transcription-factor binding prediction, in PyTorch.

Dual threshold optimization for identifying convergent evidence: TF binding locations and TF perturbation responses.