This repository contains the code and data for investigating three-dimensional (3D) genome organization in Mycobacterium tuberculosis (Mtb). The analyses integrate Hi-C, RNA-seq, and ChIP-seq data to examine chromatin architecture remodeling under different physiological conditions (WT, Hypoxia, Latent, NapM-KO, NapM-KO-Hypoxia).
cooler,cooltools,coolbox- Hi-C analysispandas,numpy- Data processingmatplotlib,seaborn- Visualizationscikit-learn- Machine learningnetworkx- Network analysisbioframe- Genomics utilities
ggplot2,dplyr- Data visualization and manipulationclusterProfiler- Functional enrichment analysis
.
├── script/ # Analysis scripts
│ ├── hicdiff.ipynb # Differential Hi-C visualization
│ ├── moran's_shannon.ipynb # Structural order metrics (Shannon entropy, Moran's I)
│ ├── operon-select_entropymoran's.ipynb # Operon hub identification
│ ├── network_formation.ipynb # Interaction network construction
│ ├── napmkolatentpre_comparedrsmd.ipynb # Counterfactual prediction
│ ├── ABcompartment.ipynb # A/B compartment analysis
│ ├── chipandrna.ipynb # ChIP-seq and RNA-seq integration
│ ├── homologous.ipynb # Homology analysis
│ ├── hiccompared_randomcidsloops.ipynb # CID/loop statistical comparison
│ ├── four_zone_volcano.ipynb # Volcano plot visualization
│ ├── regression_demo.ipynb # Regression analysis
│ ├── Rv0047c_statistics.R # Statistical analysis for NapM targets
│ ├── go_and_kegg.R # GO/KEGG enrichment analysis
│ ├── avg.R # Average calculation utilities
│ ├── random_pairs.R # Random pair generation
│ ├── venn.R # Venn diagram visualization
│ └── bulk RNA.Rmd # Bulk RNA-seq analysis
├── 3d_model_pdbfile/ # 3D structure models (PDB format, 5 conditions)
├── DI_Score_results/ # Directionality Index scores (5 conditions)
├── hic_file/ # Hi-C contact matrices (.hic format)
├── cool_file/ # Hi-C matrices in cooler format (.cool, .mcool)
├── Interaction_network/ # Multi-layer network data
│ ├── full_nodes.csv # DNA and protein nodes
│ └── full_edges.csv # Interaction edges (DNA-DNA, DNA-Protein, Protein-Protein)
├── operon_metadata/ # Operon annotations and bin coverage
├── chip_seq_data/ # NapM (Rv0047c) ChIP-seq data
│ └── Rv0047c.bedgraph # ChIP-seq signal track
├── cid_boundary/ # CID boundary coordinates (BED format, 5 conditions)
├── loops/ # Chromatin loop calls (BEDPE format)
│ ├── wt-loops.bedpe
│ └── napm_ko-loops.bedpe
└── DEG/ # Differential gene expression results
├── KOvsWT_supo_DEG_limma.csv
├── hypoxiaWTvsWT_supo_DEG_limma.csv
└── LatentvsWT_supo_DEG_limma.csv
Visualize and compare Hi-C contact maps across conditions using differential analysis.
Script: hicdiff.ipynb
from coolbox.api import *
wt = DotHiC("wt.hic", style='triangular')
ko = DotHiC("napm_ko.hic", style='triangular')
frame = XAxis() + \
HiCDiff(wt, ko, normalize='expect', diff_method='diff',
style='triangular', cmap='RdBu') + \
MinValue(-2) + MaxValue(2)
frame.plot("Chromosome:1250000-1450000")The Directionality Index (DI) quantifies the bias of chromatin interactions toward upstream or downstream regions. It is used to identify chromatin interaction domain (CID) boundaries.
Where A and B are upstream and downstream interaction counts, and E = (A + B)/2.
Data: Pre-computed DI scores available in DI_Score_results/
3D chromosome structures were modeled using LorDG (Low-rank Distance Geometry) with Hi-C contact frequencies as distance constraints.
Output: PDB files in 3d_model_pdbfile/
Visualization: Use PyMOL, Chimera, or other molecular visualization tools
Construct multi-layer interaction networks integrating:
- DNA-DNA: Hi-C loops
- DNA-Protein: ChIP-seq binding
- Protein-Protein: Known interactions
Scripts:
network_formation.ipynb- Network construction and visualizationoperon-select_entropymoran's.ipynb- Hub identification
Quantify chromatin structural organization using Shannon entropy and Moran's I on DI score distributions.
Script: moran's_shannon.ipynb
Shannon Entropy (lower = more organized):
Moran's I (higher = more structured):
Where
Predict the NapM-KO latent Hi-C contact matrix using SVD-based counterfactual learning on observed conditions.
Script: napmkolatentpre_comparedrsmd.ipynb
Method:
- Feature encoding: genotype (WT/KO) and environment (normal/hypoxia/latent)
- SVD-based imputation with ridge regularization
Analyze conservation of Rv0047c (NapM) across bacterial species.
Script: homologous.ipynb
blastp -query rv0047c.faa \
-db bacterial_proteins.fa \
-out rv0047c_homologs.txt \
-outfmt 6 \
-evalue 1e-5 \
-max_target_seqs 10000Visualization: Manhattan plot of -log10(p-value) colored by genus
Identify A/B compartments using the first eigenvector of the Hi-C correlation matrix with GC bias correction.
Script: ABcompartment.ipynb
import cooltools
import bioframe
gc_track = bioframe.frac_gc(bins, genome)
cis_eigs = cooltools.eigs_cis(clr, gc_track, n_eigs=3)
eigenvector_track = cis_eigs[1][['chrom','start','end','E1']]Statistical analysis: Bootstrap 95% confidence intervals for compartment strength comparison.
Key analysis scripts for main figures:
| Analysis | Script | Input Data |
|---|---|---|
| Hi-C differential maps | hicdiff.ipynb |
hic_file/*.hic |
| DI and structural metrics | moran's_shannon.ipynb |
DI_Score_results/*.csv |
| Operon interaction network | network_formation.ipynb |
Interaction_network/*.csv |
| 3D structure comparison | napmkolatentpre_comparedrsmd.ipynb |
3d_model_pdbfile/*.pdb |
| NapM homology | homologous.ipynb |
BLASTP output |
| A/B compartments | ABcompartment.ipynb |
.cool files |
| ChIP-RNA integration | chipandrna.ipynb |
BigWig/BedGraph |
| GO/KEGG enrichment | go_and_kegg.R |
Gene lists |
Statistical Analysis: All significance tests use Benjamini-Hochberg FDR correction (α = 0.05). Bootstrap resampling (n=1000) for confidence intervals. See individual scripts for details.
If you use this code or data, please cite:
Establishment and remodeling of the Mycobacterium tuberculosis 3D genome Orchestrate Context-Dependent Transcriptional Program. (Manuscript in preparation)
For questions, please open an issue on GitHub: https://github.com/wikk-chy/mtb_hic/issues
MIT License
Copyright (c) 2026 wikk-chy
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.