chromatin-interaction

There are 17 repositories under chromatin-interaction topic.

  • cLoops

    YaqiangCao/cLoops

    Accurate and flexible loops calling tool for 3D genomic data.

    Language:Python109152917

  • YaqiangCao/cLoops2

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

    Language:Python443137

  • aryeelab/hichipper

    A preprocessing and QC pipeline for HiChIP data

    Language:HTML3479012

  • bioinfomaticsCSU/LoopPredictor

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

    Language:Python14324

  • tzeitim/genome-blender

    Scripts to create cartoons of 3D genomes

    Language:Python13205

  • ibn-salem/sevenC

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

    Language:R12312

  • Linhua-Sun/Ath_Heat_Hi-C

    Bioinformatic analysis of RNA-Seq, WGBS, and Hi-C in Arabidopsis

    Language:R6102

  • 3DGenomes/MethodsMolBiol

    Language:Jupyter Notebook51301
  • UMI4Cats

    Pasquali-lab/UMI4Cats

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

    Language:R52153

  • sirusb/R3CPET

    3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process

    Language:R4301

  • Gabrielstav/mastercode

    Code for my Master project

    Language:Python3201

  • 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:Python3201

  • hanjunlee21/Lee-et-al-2023

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

    Language:Jupyter Notebook1100

  • tanlabcode/IM-PET

    Language:HTML110

  • younglab/origami

    Statistical Modeling of ChIA-PET interactions

    Language:Shell1784

  • yufanzhouonline/HiSIF

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

    Language:C++1230

  • tanlabcode/rGMAP

    Language:R10