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ParticleChromo3D - A Particle Swarm Optimization Algorithm for Chromosome 3D Structure Prediction from Hi-C Data

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OluwadareLab, University of Colorado, Colorado Springs

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Developers:
              David Vadnais
              Department of Computer Science
              University of Colorado, Colorado Springs
              Email: dvadnais@uccs.edu

              Michael Middleton
              Department of Computer Science
              University of Colorado, Colorado Springs
              Email: mmiddlet@uccs.edu

Contact:
              Oluwatosin Oluwadare, PhD
              Department of Computer Science
              University of Colorado, Colorado Springs
              Email: ooluwada@uccs.edu

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0. Follow-on:

This code has continued development in the follow on ParticleChromo3D+. As part of that process we released a containerized web gui with restful endpoints that can be accessed here. Additionally, we released a PyPi project which we will describe below.

ParticleChromo3D from PyPi

NOTE: This usage is underconstruction.
We released a pypi package.

Simply run : pip install ParticleChromo3D

Then :

from ParticleChromo3D import Ps
import numpy as np

fout = Ps.strip_file("exampleIfs/chr22_matrix.txt")

# this will all get moved into a self contained function soon
theseAlphas = np.array([0.1, 2.0, 0.1]) * 100
theAlphas = ( np.array(range(int(theseAlphas[0]), int(theseAlphas[1]), int(theseAlphas[2]))) / 100)

outputOfSwarm = Ps.Full_List(fout, "this_pdb", theseAlphas)[0]

bestSpearm = outputOfSwarm[1]
bestCost = outputOfSwarm[2]
bestAlpha = theAlphas[outputOfSwarm[4]]
bestPearsonRHO = outputOfSwarm[0]

print(f"Convert factor:: {bestAlpha}")
print(f"SSE at best spearman : {bestCost}")
print(f"Best Spearman correlation Dist vs. Reconstructed Dist  : {bestSpearm}")
print(f"Best Pearson correlation Dist vs. Reconstructed Dist: {bestPearsonRHO}")

Ps.Write_Log(
    "this_run.log", fout, bestAlpha, bestCost, bestSpearm, bestPearsonRHO
)

Additional usage documentation can be found in ParticleChromo3D+. Click here to view

1. Content of folders:

• src: Python Source Code and utility's used.
  
• input-and-models: Synthetic and Real Hi-C datasets used.
  
• Results: Output Structions generated for all the experiments performed.
  

2. Hi-C Data used in this study:

In our study, we used the synthetic dataset from Adhikari, et al. The contact maps, the original models and their reconstructed models used in this study were downloaded from here

The GM12878 cell Hi-C dataset, GEO Accession number GSE63525, was downloaded from GSDB with GSDB ID: OO7429SF

3. Input matrix file format:

Square Matrix Input format: The square matrix is a space seperated N by N intra-chromosomal contact matrix derived from Hi-C data, where N is the number of equal-sized regions of a chromosome.

4. Dependencies Installation:

Install env with anaconda from spec-file:
conda create --name ParticleChromo3D --file ParticleChromo3d_env.txt

Or install dependencies individually

Dependencies:
    Biopython - 1.7.8
    scipy - 1.5.2
    numpy - 1.19.2

5. Usage:

usage: python3 Ps.py infile [-h] [-ss SWARMSIZE] [-itt ITTCOUNT] [-t THRESHOLD] [-rr RANDRANGE] [-lf LOSSFUNCTION]

• positional arguments:
      infile: Input file in the format of a matrix of contacts
  

• optional arguments:
      -h, --help show this help message and exit
      -ss SWARMSIZE, --swarmSize SWARMSIZE
        Number of particles in system [Default 15]
      -itt ITTCOUNT, --ittCount ITTCOUNT
         Maximum number of iterations before stop [Default 30000]
      -t THRESHOLD, --threshold THRESHOLD
        Error threshold before stoping [Default 0.000001]
      -rr RANDRANGE, --randRange RANDRANGE
        Range of x,y,z starting coords. Random value bewtween -randRange,randRange [Default 1]
      -o OUTFILE, --outfile OUTFILE
         Filename of the output pdb model [Default ./chr.pdb]
      -lf LOSSFUNCTION, --lossFunction LOSSFUNCTION
        0 = SSE, 1 = MSE, 2 = RMSE, 3 = Huber [Default 2]
  

6. Output:

A pdb file and log file.

7. Publication:

Vadnais, D., Middleton, M. & Oluwadare, O. ParticleChromo3D: a Particle Swarm Optimization algorithm for chromosome 3D structure prediction from Hi-C data. BioData Mining 15, 19 (2022). https://doi.org/10.1186/s13040-022-00305-x

Vadnais, David, and Oluwatosin Oluwadare. "ParticleChromo3D+: A Web Server for ParticleChromo3D Algorithm for 3D Chromosome Structure Reconstruction." Current Issues in Molecular Biology 45.3 (2023): 2549-2560.