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Codes and functions for the mycobacterium tuberculosis Hi-C data analysis

This repository contains the code and data used in the paper: The goal of this project is to analysis mycobacterium tuberculosis Hi-C data.The codes presented here should allow to reproduce the different graphs and figures from the main text and the supplementary data.

Table of contents

Dependencies

Scripts and codes can be run on OS X and other Unix-based systems, and necessitate:

Python (>=3.1)

  • Coolbox
  • Pandas
  • Numpy
  • Matplotlib
  • Scipy
  • Seaborn
  • Sklearn

External programs

Raw data extraction and alignment

Data extraction

Alignment

We use the H37Rv reference genome ( NCBI:txid83332, total length 4411532).

Building contacts map

To build the contact map , we use

Scalogram

The scale map tool can visualize the dispersion of contact signals along spatial scales.This function is implemented by the code.

Directionality index

The Directionality Index (DI) quantifies the bias of chromatin interactions toward upstream or downstream regions. It is widely used to identify TAD boundaries in 3D genome organization.we use the code to calculate.

Construct the 3D genome model

To study the three-dimensional structure of chromosomes, we use [method] to directly simulate the positional relationships between nucleotides.

Find cooperative operon hubs

Search for homologous genes

We obtained all homologous genes of Rv0047c at the bacterial level from orthoDBthen we use ncbi-blast to calculate homology relationship.

blastp -query protein.faa -out rv0047c.txt -db fasta.fa -outfmt 6 -evalue 1e-5 -num_threads 2 -max_target_seqs 10000

Measure the chromatin order and the structural plasticity

To Measure the chromatin order and the structural plasticity,we selected Shannon entropy and Moran’s I as the evaluation metrics.

Shannon entropy

Moran’s I

Predict KO-latent Hi-C contact matrix

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mycobacterium tuberculosis Hi-C data analysis

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