hanjunlee21 Goto Github PK

bedgraph2cluster

MATLAB Function for the k-Means Clustering of ChIP-seq bedGraph Data

hicshuffle

FASTQ Shuffling Tool For Sanity Check in Hi-C Differential Contact Analysis

lee-et-al-2023

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

panchip

Pan-ChIP-seq Analysis of Peak Sets

run_matlab

Command Line MATLAB Function Caller with Automatic MATLAB Compiler Runtime (MCR) Installation

sanidas-et-al-2021

serohet

SeroHet is a MATLAB-based software to study signatures of interpatient seroprotein heterogeneity in cancer patients.

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.