pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.

A repository with exploration into using transformers to predict DNA ↔ transcription factor binding

pyJASPAR: A Pythonic interface to JASPAR transcription factor motifs

Python script to quickly extract promoter and terminator regions with the NCBI API. Search for the presence of individual pattern or transcription factor responsive elements with manual sequence (IUPAC) or JASPAR API.

A motif discovery tool to detect the occurrences of known motifs

Deep neural networks implemented in TensorFlow & Python for predicting whether transcription factors will bind to given DNA sequences

MYB transcription factors are one of the largest gene family in plants and control many processes. This repository provides additional background to the #MYB_Monday tweets

Bioinformatic approach to identify functional transcription factor binding motifs

Pipeline for predicting ChIP-seq peaks in novel cell types using chromatin accessibility

An ensemble method for predicting transcription factor in protein sequences

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

Published in GigaScience. Web app for post-GWAS/QTL analysis that performs a slew of novel bioinformatics analyses to cross-reference GWAS/QTL mapping results with a host of publicly available rice databases

Python package to generate prior gene regulatory networks.

Unofficial fork of the DeepSEA deep learning genomics project

Patterns of Binding Targets

Bioinformatics study on tfbs and their association with specific tissues.

An integrative toolkit for detecting cell type-specific regulators

Scans TF binding sites based on motifs.

The app visualizes a 3D graph of gene regulatory networks, allowing users to select a transcription factor and explore its neighboring interactions.