BigMPI4py: Python Module for Parallelization of Big Data Objects Discloses Germ Layer Specific DNA Demethylation Motifs

Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than <inline-formula><tex-math notation="LaTeX">2^{31}</tex-math> <mml:math><mml:msup><mml:mn>2</mml...

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Veröffentlicht in:IEEE/ACM transactions on computational biology and bioinformatics Jg. 19; H. 3; S. 1507 - 1522
Hauptverfasser: Ascension, Alex M., Arauzo-Bravo, Marcos J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Big Data
Bioinformatics
Bisulfite
Computational biology
computational epigenomics
CpG islands
Demethylation
Deoxyribonucleic acid
DNA
DNA - metabolism
DNA Demethylation
DNA methylation
DNA Methylation - genetics
DNA methylation motifs
DNA methylation sequencing
DNA sequencing
Ectoderm
Endoderm
Genomes
Genomics
Germ Layers - metabolism
high performance computing (HPC)
Human tissues
Humans
Mesoderm
Message passing
message passing interface (MPI)
Modules
parallelization
Python
Sequence Analysis, DNA - methods
Sequential analysis
Syntactics
Tissues
whole genome bisulfite sequencing (WGBS)
Workstations
ISSN:1545-5963, 1557-9964, 1557-9964
Online-Zugang:Volltext
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Zusammenfassung:Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than <inline-formula><tex-math notation="LaTeX">2^{31}</tex-math> <mml:math><mml:msup><mml:mn>2</mml:mn><mml:mn>31</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="ascension-ieq1-3043979.gif"/> </inline-formula> bytes, we developed BigMPI4py, a Python module that wraps mpi4py, supporting object sizes beyond this boundary. BigMPI4py automatically determines the optimal object distribution strategy, and uses vectorized methods, achieving higher parallelization efficiency. BigMPI4py facilitates the implementation of Python for Big Data applications in multicore workstations and High Performance Computer systems. We use BigMPI4py to speed-up the search for germ line specific de novo DNA methylated/unmethylated motifs from the 59 whole genome bisulfite sequencing DNA methylation samples from 27 human tissues of the ENCODE project. We developed a parallel implementation of the Kruskall-Wallis test to find CpGs with differential methylation across germ layers. The parallel evaluation of the significance of 55 million CpG achieved a 22x speedup with 25 cores allowing us an efficient identification of a set of hypermethylated genes in ectoderm and mesoderm-related tissues, and another set in endoderm-related tissues and finally, the discovery of germ layer specific DNA demethylation motifs. Our results point out that DNA methylation signal provide a higher degree of information for the demethylated state than for the methylated state. BigMPI4py is available at https://https://www.arauzolab.org/tools/bigmpi4py and https://gitlab.com/alexmascension/bigmpi4py and the Jupyter Notebook with WGBS analysis at https://gitlab.com/alexmascension/wgbs-analysis .
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2020.3043979