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Use bioconductor-hicdcplus to Outline genomic feature preparation for [genome version, restriction enzyme and bin size], including feature generation and expansion to a gi_list. Check chromosome naming and note that differential analysis needs biological replicates; do not claim interactions have already been called. Retain the documented package and R/Bioconductor version prerequisites and source limitations. Return an analysis plan or explanation only; do not install packages, run code, or claim results that have not been observed.
Required inputs
[genome version, restriction enzyme and bin size]
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SKILL.md
name: bioconductor-hicdcplusdescription: Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw conwhen_to_use: Use when: Analyzing Hi-C and HiChIP data with HiCDCPlus; Data normalization and preprocessing (HiCDCPlus). Not for: Requires biological replicates for statistical analysis; Requires R ≥ 4.0.0 and Bioconductor ≥ 3.16user-invocable: false
HiCDCPlus
Workflows
Standard Workflow
Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw con
library(HiCDCPlus)
outdir <- tempdir()
# Generate genomic features (GC, mappability, effective length)
construct_features(
output_path = paste0(outdir, "/hg19_50kb_GATC"),
gen = "Hsapiens",
gen_ver = "hg19",
sig = "GATC",
bin_type = "Bins-uniform",
binsize = 50000,
chrs = "chr21"
)
# Initialize gi_list from the bintolen file
gi_list <- generate_bintolen_gi_list(
bintolen_path = paste0(outdir, "/hg19_50kb_GATC_bintolen.txt.gz")
)
# Expand 1D features to 2D for modeling
gi_list <- expand_1D_features(gi_list)
Input: Genome version, restriction enzyme pattern, and bin size; Output: A gi_list object containing 2D genomic features ready for interaction calling.
When to Use
Chromatin Interaction Calling: To call significant 3D chromatin interactions from Hi-C or HiChIP data using HiCDCPlus or HiCDCPlus_parallel.
Differential Interaction Analysis: To perform differential interaction analysis across conditions with replicates using hicdcdiff.
Feature Generation: To generate genomic features (GC content, mappability, effective length) using construct_features.
TAD Calling: To find Topologically Associating Domains (TADs) using gi_list_topdom.
Compartment Analysis: To extract A/B compartments (eigenvectors) from .hic files using extract_hic_eigenvectors.
When NOT to Use
1D Peak Calling: For standard 1D ChIP-seq peak calling, use MACS2 or epigraHMM.
RNA-seq Differential Expression: For RNA-seq differential expression, use standard DESeq2 or edgeR directly.
Data Requirements
Hi-C Formats: Hi-C data in .hic, .matrix, or .allValidPairs formats.
Enzyme Patterns: Restriction enzyme cut site patterns (e.g., "GATC").
Reference Genome: Reference genome (e.g., "hg19", "hg38") and chromosome names.
Key Parameters
bin_type ("Bins-uniform"): Type of binning, either "Bins-uniform" or "Bins-RE-sites".
binsize (50000): Size of genomic windows or number of restriction fragments to merge.
sig ("GATC"): Restriction enzyme recognition sequence.
ssize (0.1): Downsampling rate of rows for modeling in HiCDCPlus.
fitType ("mean"): Fit type for dispersion in hicdcdiff.
Best Practices
Feature Generation: Generate genomic features using construct_features before initializing the gi_list to ensure GC and length biases are modeled.
Feature Expansion: Expand 1D features to 2D using expand_1D_features prior to running the negative binomial regression.
Parallelization: Use HiCDCPlus_parallel for efficient genome-wide interaction calling across multiple chromosomes.
TAD Normalization: For TAD calling, perform ICE normalization first using hic2icenorm_gi_list before running gi_list_topdom.
Common Pitfalls
Missing Feature Expansion: Forgetting to expand 1D features to 2D before running HiCDCPlus; always call expand_1D_features first.
Chromosome Name Mismatch: Mismatched chromosome names between the feature files and count files; ensure consistent naming (e.g., "chr21" vs "21").
Insufficient Replicates: Insufficient replicates for differential analysis; use hicdcdiff with biological replicates to ensure robust statistical power.
Alternatives
diffHic: For differential interaction analysis using sliding windows.
HiTC: For basic Hi-C data manipulation and ICE normalization.
FitHiC: For calling significant interactions.
Citations
Sahin, M., Wong, W., Zhan, Y., Van Deyze, K., Koche, R., and Leslie, C. S. (2021) HiC-DC+: systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. Nature Communications, 12(3366).