Supplementary Material – README
================================

This supplementary archive contains the notebook and supporting files used
for the computational analyses presented in the manuscript:

“Long-range sequence memory and regulatory architecture across human chromosomes”

Author: Khushboo Rani  
Contact: khushira2244@gmail.com  
ORCID: 0009-0006-7000-026X

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1. Contents of this supplementary archive
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The ZIP file includes the following:

1. notebook/
   ├── chr1-and-chr-2_analysis.ipynb
   ├── chr22-analysis.ipynb
   

2. sample_data/
   ├── geneome
   
   (Small example subsets for demonstration.  
    Full datasets are very large and therefore available upon request.)

3. readme.txt (this file)

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2. Required data (human genome & regulatory tracks)
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Full datasets are **publicly available** but not included in this archive due
to their size. They can be obtained from:

• Human genome reference (GRCh38/hg38):
  UCSC Genome Browser – https://hgdownload.soe.ucsc.edu/downloads.html

• ENCODE regulatory tracks for chr22:
  ENCODE Portal – https://www.encodeproject.org/

**If reviewers require the exact full FASTA files, BED annotations,  
or the complete processed feature matrices used in the study,  
they are available from the author on request.**

Please contact:
Email: **khushira2244@gmail.com**

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3. How to run the notebook
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1. Upload the notebook folder (`analysis_chr1_chr2.ipynb`
   or `analysis_chr22.ipynb`) into Google Colab, JupyterLab,
   or any Python 3.10+ environment.

2. Install required Python libraries:
   numpy, pandas, scipy, scikit-learn, matplotlib, seaborn, statsmodels

3. Place the necessary FASTA and BED files in your working directory or
   update their paths in the first cell of the notebook.

4. Run all cells sequentially.  
   The notebook performs:
   • windowing (50 kb windows, 10 kb step)  
   • memory exponent computation (DFA Hurst)  
   • entropy, autocorrelation & run-length features  
   • Balanced Random Forest classification  
   • generation of figures for the manuscript  

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4. Reproducibility information
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All analyses were performed using:
• Python 3.10  
• scikit-learn 1.3+  
• numpy 1.24+  
• pandas 2.0+  
• matplotlib 3.7+  

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5. Contact
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For access to full datasets or replication support, please contact:

**khushira2244@gmail.com**

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End of README.
