There are 1 repository under transcription-factors topic.
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.
[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome
Single-cell Transcriptome and Regulome Analysis Pipeline
:dart: Human transcription factor target genes from 6 databases in convenient R format.
A repository with exploration into using transformers to predict DNA ↔ transcription factor binding
Netbooks is a JupyterHub catalog of use cases in gene regulatory network inference using netZoo methods..
BITFAM is a Bayesian approach and platform to infer transcription factor activities within individual cells using single cell RNA-sequencing data. Please see Gao S et al., Genome Research (2021) https://genome.cshlp.org/content/31/7/1296 for details.
:bug: How to use CENTIPEDE to determine if a transcription factor is bound.
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.
Deep neural networks implemented in TensorFlow & Python for predicting whether transcription factors will bind to given DNA sequences
Bioinformatics pipeline to identify differentially active transcription factors between conditions using expression and epigenetic data
A “data light” TF-network mapping algorithm using only gene expression and genome sequence data.
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
7C: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs
Codebase for the domain adaptation (cross-species TF binding prediction) project.
Bioinformatics pipeline that makes use of expression and open chromatin data to identify differentially active transcription factors across conditions.
Pipeline for predicting ChIP-seq peaks in novel cell types using chromatin accessibility
Bioinformatic approach to identify functional transcription factor binding motifs
DEbPeak - Analyze and integrate multi-omics to unravel the regulation of gene expression.
Identification and structural characterization of transcription factors based on supervised machine learning
Motif discovery for DNA sequences using multiobjective optimization and genetic programming.
Network Regression Embeddings reveal cell-type Transcription Factor coordination for target gene (TG) regulation
An R package designed to integrate and visualize various levels of epigenomic information, including but not limited to: ChIP, Histone, ATAC, and RNA sequencing. epiRomics is also designed to identify enhancer and enhanceosome regions from these data.
An ensemble method for predicting transcription factor in protein sequences
All code generated for Loupe et al. 2023
Analysis of Single Molecule Footprinting (SMF) data for the analysis of DNA methylation, chromatin accessibility and TF binding. The repository contains all primary code to reproduce the main analyses for the publication "Single molecule footprinting identifies context-dependent regulation of enhancers by DNA methylation" (Kreibich et al., 2023)