hic-matrix-qc
This skill performs standardized quality control (QC) on Hi-C contact matrices stored in .mcool or .cool format. It computes coverage and cis/trans ratios, distance-dependent contact decay (P(s) curves), covera...
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Skills indexés
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3Les étoiles d’un dépôt peuvent se répéter entre ses skills.
This skill performs standardized quality control (QC) on Hi-C contact matrices stored in .mcool or .cool format. It computes coverage and cis/trans ratios, distance-dependent contact decay (P(s) curves), covera...
This skill provides a complete pipeline for integrating CpG methylation data with chromatin features such as ATAC-seq signal, H3K27ac, H3K4me3, or other histone marks/TF signals.
This skill performs A/B compartment shift analysis between two Hi-C samples.
The differential-region-analysis pipeline identifies genomic regions exhibiting significant differences in signal intensity between experimental conditions using a count-based framework and DESeq2. It supports...
This skill generates normalized BigWig (.bw) tracks (and/or fold-change tracks) from BAM files for ATAC-seq and ChIP-seq visualization. It handles normalization (RPM or fold-change) and Tn5 offset correction au...
This skill identifies novel transcription factor binding motifs in the promoter regions of genes, or directly from genomic regions of interest such as ChIP-seq peaks, ATAC-seq accessible sites, or differentiall...
This skill performs transcription factor (TF) footprint analysis using TOBIAS on ATAC-seq data. It corrects Tn5 sequence bias, quantifies TF occupancy at motif sites, generates footprint scores, and optionally...
The TF-differential-binding pipeline performs differential transcription factor (TF) binding analysis from ChIP-seq datasets (TF peaks) using the DiffBind package in R. It identifies genomic regions where TF bi...
This pipeline performs genome-wide segmentation of CpG methylation profiles to identify Unmethylated Regions (UMRs), Low-Methylated Regions (LMRs), and Partially Methylated Domains (PMDs) using whole-genome bis...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.