GitHub topics: chromatin-interaction

Repositories

aryeelab/hichipper

A preprocessing and QC pipeline for HiChIP data

Language: HTML - Size: 224 MB - Last synced at: 13 days ago - Pushed at: almost 3 years ago - Stars: 37 - Forks: 13

YaqiangCao/cLoops2

Enhanced and elegant flexible peak/loop/domain -calling and analysis tool for 1D/3D genomic data.

Language: Python - Size: 251 MB - Last synced at: 11 days ago - Pushed at: about 1 month ago - Stars: 46 - Forks: 8

bioinfomaticsCSU/LoopPredictor

LoopPredictor: Predicting unknown enhancer-mediated genome topology by an ensemble machine learning model

Language: Python - Size: 62 MB - Last synced at: about 1 month ago - Pushed at: almost 4 years ago - Stars: 14 - Forks: 4

hanjunlee21/Lee-et-al-2023

RB represses cohesin-dependent loop formation and safeguards E2F-independent transcription

Language: Jupyter Notebook - Size: 70.4 MB - Last synced at: about 1 month ago - Pushed at: 7 months ago - Stars: 1 - Forks: 0

Gabrielstav/mastercode

Code for my Master project

Language: Python - Size: 1.55 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 3 - Forks: 1

yufanzhouonline/HiSIF

HiSIF: Genome-wide chromatin interactions identify characteristic promoter-distal loops

Language: C++ - Size: 140 MB - Last synced at: about 2 months ago - Pushed at: 7 months ago - Stars: 1 - Forks: 0

ibn-salem/sevenC

7C: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs

Language: R - Size: 26.8 MB - Last synced at: 12 months ago - Pushed at: over 6 years ago - Stars: 12 - Forks: 2

Pasquali-lab/UMI4Cats

An R package for analyzing UMI-4C chromatin contact data.

Language: R - Size: 37.4 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 4 - Forks: 2

hanjunlee21/StructuralSimilarity

Identification of dynamic changes in chromatin conformation is a fundamental task in genetics. In 2020, Galan et al. presented CHESS (Comparison of Hi-C Experiments using Structural Similarity), a novel computational algorithm designed for systematic identification of structural differences in chromatin-contact maps. Using CHESS, the same group recently reported that chromatin organization is largely maintained across tissues during dorsoventral patterning of fruit fly embryos despite tissue-specific chromatin states and gene expression. However, here we show that the primary outputs of CHESS–namely, the structural similarity index (SSIM) profiles–are nearly identical regardless of the input matrices, even when query and reference reads were shuffled to destroy any significant differences. This issue stems from the dominance of the regional counting noise arising from stochastic sampling in chromatin-contact maps, reflecting a fundamentally incorrect assumption of the CHESS algorithm. Therefore, biological interpretation of SSIM profiles generated by CHESS requires considerable caution.

Language: Python - Size: 94 MB - Last synced at: 23 days ago - Pushed at: almost 2 years ago - Stars: 3 - Forks: 1