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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Vydáno v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 19; číslo 3; s. 1507 - 1522
Hlavní autoři: Ascension, Alex M., Arauzo-Bravo, Marcos J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Abstract 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 .
AbstractList Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than [Formula Omitted] 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 .
Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than 2 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.
Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than 231 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.Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than 231 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.
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 .
Author Arauzo-Bravo, Marcos J.
Ascension, Alex M.
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/33301409$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1007/s00428-019-02604-x
10.1530/JME-13-0159
10.3324/haematol.2017.185603
10.1007/s12020-011-9582-y
10.1371/journal.pone.0049086
10.1111/febs.12130
10.1172/JCI130379
10.1002/cbin.11096
10.3892/mmr.2018.8938
10.1016/j.jpdc.2005.03.010
10.1126/science.1200970
10.1002/hep.30631
10.1101/gr.155960.113
10.1159/000104774
10.1073/pnas.1920880117
10.7554/elife.32532
10.1051/0004-6361/201732493
10.1089/big.2013.0016
10.1089/gtmb.2012.0435
10.1093/nar/gku696
10.1371/journal.pbio.1002195
10.1016/j.stemcr.2019.10.013
10.1186/s13148-016-0195-4
10.1242/dev.029355
10.1093/nar/gkx302
10.1093/hmg/ddt001
10.1016/j.clcc.2018.10.003
10.1186/s12864-017-4353-7
10.1159/000494647
10.25080/Majora-7b98e3ed-013
10.1109/ExaMPI.2014.5
10.1093/nar/gky1131
10.1007/s00436-017-5423-0
10.5555/3291168.3291210
10.1016/j.molcel.2010.05.004
10.1016/j.bbrc.2015.10.086
10.1002/dvdy.20373
10.1038/s41598-018-23733-4
10.1016/j.cell.2012.06.053
10.3390/ijms18020412
10.4049/jimmunol.1500250
10.1002/jcp.28831
10.1371/journal.pone.0019778
10.1523/JNEUROSCI.18-14-05389.1998
10.1038/nature07391
10.1016/j.celrep.2019.09.035
10.1002/dvdy.10290
10.1093/bioinformatics/btw633
10.1186/gb-2013-14-10-r115
10.1093/toxsci/68.2.403
10.1089/scd.2006.0077
10.1152/ajpgi.00421.2003
10.1002/dvdy.22639
10.1002/jcp.29179
10.1093/nar/gku1055
10.2217/epi-2016-0120
10.1093/hmg/9.9.1443
10.1145/2642769.2642802
10.3389/fnins.2011.00035
10.2217/epi-2017-0098
10.1002/jcb.29149
10.1158/1078-0432.CCR-17-1347
10.1111/imm.12535
10.1038/bjc.2015.341
10.1007/978-3-540-30218-6_19
10.1074/jbc.M114.557546
10.1074/mcp.M113.035600
10.1155/2016/2574152
10.1186/s12885-019-5294-0
10.1038/srep21534
10.1186/s12859-017-1698-5
10.1038/nature11247
10.1152/ajpgi.00394.2005
10.1038/s41598-018-23158-z
10.1038/nrg1655
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References ref13
ref57
ref56
ref15
ref59
ref14
ref58
ref53
ref52
ref11
ref55
ref10
ref54
ref18
Lohr (ref1)
ref51
ref50
Group (ref12)
Yang (ref36) 2017; 24
ref46
ref45
ref48
ref47
ref42
ref86
ref41
ref85
ref44
ref88
ref43
ref87
Lukinov (ref17)
(ref79) 2020
ref49
ref8
ref7
ref9
ref4
ref6
Abadi (ref83) 2015
ref5
ref82
ref81
ref40
ref80
ref35
ref34
ref78
Chollet (ref84) 2015
ref37
ref31
ref75
ref30
ref74
ref33
ref77
ref32
ref76
ref2
ref39
ref38
Besnard (ref65) 2011
Marr (ref3) 2018
ref71
ref70
ref73
ref72
ref24
ref68
ref23
ref67
ref26
ref25
ref69
ref20
ref64
ref63
ref22
ref66
ref21
ref28
ref27
ref29
Hammond (ref19) 2018
Nardelli (ref16) 2016
ref60
ref62
ref61
References_xml – ident: ref34
  doi: 10.1007/s00428-019-02604-x
– ident: ref50
  doi: 10.1530/JME-13-0159
– ident: ref63
  doi: 10.3324/haematol.2017.185603
– year: 2015
  ident: ref83
  article-title: TensorFlow: Large-scale machine learning on heterogeneous systems
– ident: ref30
  doi: 10.1007/s12020-011-9582-y
– ident: ref88
  doi: 10.1371/journal.pone.0049086
– ident: ref76
  doi: 10.1111/febs.12130
– ident: ref87
  doi: 10.1172/JCI130379
– ident: ref12
  article-title: About us
– ident: ref41
  doi: 10.1002/cbin.11096
– ident: ref42
  doi: 10.3892/mmr.2018.8938
– ident: ref15
  doi: 10.1016/j.jpdc.2005.03.010
– ident: ref2
  doi: 10.1126/science.1200970
– year: 2015
  ident: ref84
  article-title: Keras
– ident: ref52
  doi: 10.1002/hep.30631
– ident: ref9
  article-title: Documentation
– ident: ref22
  doi: 10.1101/gr.155960.113
– ident: ref74
  doi: 10.1159/000104774
– ident: ref59
  doi: 10.1073/pnas.1920880117
– ident: ref69
  doi: 10.7554/elife.32532
– ident: ref81
  doi: 10.1051/0004-6361/201732493
– ident: ref10
  doi: 10.1089/big.2013.0016
– ident: ref60
  doi: 10.1089/gtmb.2012.0435
– ident: ref72
  doi: 10.1093/nar/gku696
– ident: ref7
  doi: 10.1371/journal.pbio.1002195
– ident: ref71
  doi: 10.1016/j.stemcr.2019.10.013
– ident: ref26
  doi: 10.1186/s13148-016-0195-4
– ident: ref48
  doi: 10.1242/dev.029355
– ident: ref46
  doi: 10.1093/nar/gkx302
– ident: ref55
  doi: 10.1093/hmg/ddt001
– ident: ref32
  doi: 10.1016/j.clcc.2018.10.003
– ident: ref85
  doi: 10.1186/s12864-017-4353-7
– ident: ref35
  doi: 10.1159/000494647
– ident: ref82
  doi: 10.25080/Majora-7b98e3ed-013
– ident: ref80
  article-title: Pyspark: Python API for spark
– ident: ref18
  doi: 10.1109/ExaMPI.2014.5
– ident: ref78
  doi: 10.1093/nar/gky1131
– year: 2020
  ident: ref79
  article-title: Joblib: Running python functions as pipeline jobs
– ident: ref25
  doi: 10.1007/s00436-017-5423-0
– year: 2018
  ident: ref3
  article-title: How much data do we create every day? The mind-blowing stats everyone should read
– ident: ref13
  doi: 10.5555/3291168.3291210
– ident: ref43
  doi: 10.1016/j.molcel.2010.05.004
– ident: ref67
  doi: 10.1016/j.bbrc.2015.10.086
– ident: ref56
  doi: 10.1002/dvdy.20373
– ident: ref29
  doi: 10.1038/s41598-018-23733-4
– year: 2018
  ident: ref19
  article-title: BigMPI
– ident: ref53
  doi: 10.1016/j.cell.2012.06.053
– ident: ref8
  doi: 10.3390/ijms18020412
– ident: ref70
  doi: 10.4049/jimmunol.1500250
– ident: ref39
  doi: 10.1002/jcp.28831
– ident: ref64
  doi: 10.1371/journal.pone.0019778
– ident: ref51
  doi: 10.1523/JNEUROSCI.18-14-05389.1998
– ident: ref58
  doi: 10.1038/nature07391
– ident: ref86
  doi: 10.1016/j.celrep.2019.09.035
– ident: ref75
  doi: 10.1002/dvdy.10290
– ident: ref5
  doi: 10.1093/bioinformatics/btw633
– ident: ref23
  doi: 10.1186/gb-2013-14-10-r115
– ident: ref45
  doi: 10.1093/toxsci/68.2.403
– ident: ref47
  doi: 10.1089/scd.2006.0077
– ident: ref61
  doi: 10.1152/ajpgi.00421.2003
– ident: ref1
  article-title: The origins of ‘Big Data’: An etymological detective Story
– ident: ref17
  article-title: OverflowError: Integer 2559182040 does not fit in ’int’
– ident: ref44
  doi: 10.1002/dvdy.22639
– ident: ref57
  doi: 10.1002/jcp.29179
– year: 2016
  ident: ref16
  article-title: OverflowError: Integer 2768896564 does not fit in ’int’
  publication-title: Bitbucket
– ident: ref62
  doi: 10.1093/nar/gku1055
– ident: ref38
  doi: 10.2217/epi-2016-0120
– ident: ref68
  doi: 10.1093/hmg/9.9.1443
– ident: ref11
  doi: 10.1145/2642769.2642802
– volume-title: Front. Neurosci.
  year: 2011
  ident: ref65
  article-title: Elk-1 a transcription factor with multiple facets in the brain
  doi: 10.3389/fnins.2011.00035
– ident: ref21
  doi: 10.2217/epi-2017-0098
– ident: ref40
  doi: 10.1002/jcb.29149
– ident: ref37
  doi: 10.1158/1078-0432.CCR-17-1347
– ident: ref77
  doi: 10.1111/imm.12535
– ident: ref4
  doi: 10.1038/bjc.2015.341
– ident: ref14
  doi: 10.1007/978-3-540-30218-6_19
– ident: ref33
  doi: 10.1074/jbc.M114.557546
– ident: ref49
  doi: 10.1074/mcp.M113.035600
– ident: ref73
  doi: 10.1155/2016/2574152
– ident: ref31
  doi: 10.1186/s12885-019-5294-0
– ident: ref66
  doi: 10.1038/srep21534
– ident: ref6
  doi: 10.1186/s12859-017-1698-5
– ident: ref28
  doi: 10.1038/nature11247
– volume: 24
  start-page: 75
  issue: 131
  year: 2017
  ident: ref36
  article-title: miR-200b regulates epithelial-mesenchymal transition of chemo-resistant breast cancer cells by targeting FN1
  publication-title: Discov. Med.
– ident: ref54
  doi: 10.1152/ajpgi.00394.2005
– ident: ref24
  doi: 10.1038/s41598-018-23158-z
– ident: ref20
  doi: 10.1101/gr.155960.113
– ident: ref27
  doi: 10.1038/nrg1655
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Snippet Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than...
Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than 2...
Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than 231...
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SubjectTerms 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
Title BigMPI4py: Python Module for Parallelization of Big Data Objects Discloses Germ Layer Specific DNA Demethylation Motifs
URI https://ieeexplore.ieee.org/document/9290413
https://www.ncbi.nlm.nih.gov/pubmed/33301409
https://www.proquest.com/docview/2672805592
https://www.proquest.com/docview/2470628453
Volume 19
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