yupenghe/REPTILE

Predicting regulatory DNA elements based on epigenomic signatures

Language: Python - Size: 84.5 MB - Last synced at: 29 days ago - Pushed at: 11 months ago - Stars: 28 - Forks: 4

EngreitzLab/gene_network_evaluation

Evaluation framework for computationally inferred gene networks from single-cell data.

Language: Jupyter Notebook - Size: 173 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 10 - Forks: 7

asntech/improse

Integrated workflow to predict super-enhancers and compute feature importance

Language: Python - Size: 5.68 MB - Last synced at: 10 days ago - Pushed at: about 7 years ago - Stars: 8 - Forks: 4

Danko-Lab/tfTarget

Identify transcription factor-enhancer/promoter-gene network from run-on sequencing data

Language: R - Size: 27.7 MB - Last synced at: 6 months ago - Pushed at: over 2 years ago - Stars: 7 - Forks: 3

iatahmid/chilenpred

Language: Jupyter Notebook - Size: 7.77 MB - Last synced at: almost 2 years ago - Pushed at: over 6 years ago - Stars: 5 - Forks: 2

slrvv/CENTRE

Language: R - Size: 434 MB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 2 - Forks: 3

Huising-Lab/epiRomics

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.

Language: HTML - Size: 32.6 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 2 - Forks: 2

psaisan/PEAKDIFF

Differential peak analysis, genomic sequences associated with distinct experimental conditions

Language: Jupyter Notebook - Size: 6.68 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 0 - Forks: 0

aron0093/gene_network_evaluation

Evaluation framework for computationally inferred gene networks from single-cell data.

Language: Python - Size: 7.4 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0