SparkGC: Spark based genome compression for large collections of genomes

Abstract Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention the...
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Autor*in:	Haichang Yao [verfasserIn] Guangyong Hu [verfasserIn] Shangdong Liu [verfasserIn] Houzhi Fang [verfasserIn] Yimu Ji [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Genome compression Reference-based compression Spark Distributed parallel

Übergeordnetes Werk:	In: BMC Bioinformatics - BMC, 2003, 23(2022), 1, Seite 21
Übergeordnetes Werk:	volume:23 ; year:2022 ; number:1 ; pages:21

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12859-022-04825-5

Katalog-ID:	DOAJ035621281

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allfields	10.1186/s12859-022-04825-5 doi (DE-627)DOAJ035621281 (DE-599)DOAJ04e1c74c25ee429e940b4a64d779814e DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Haichang Yao verfasserin aut SparkGC: Spark based genome compression for large collections of genomes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark’s in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://github.com/haichangyao/SparkGC . Genome compression Reference-based compression Spark Distributed parallel Computer applications to medicine. Medical informatics Biology (General) Guangyong Hu verfasserin aut Shangdong Liu verfasserin aut Houzhi Fang verfasserin aut Yimu Ji verfasserin aut In BMC Bioinformatics BMC, 2003 23(2022), 1, Seite 21 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:23 year:2022 number:1 pages:21 https://doi.org/10.1186/s12859-022-04825-5 kostenfrei https://doaj.org/article/04e1c74c25ee429e940b4a64d779814e kostenfrei https://doi.org/10.1186/s12859-022-04825-5 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 21
spelling	10.1186/s12859-022-04825-5 doi (DE-627)DOAJ035621281 (DE-599)DOAJ04e1c74c25ee429e940b4a64d779814e DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Haichang Yao verfasserin aut SparkGC: Spark based genome compression for large collections of genomes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark’s in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://github.com/haichangyao/SparkGC . Genome compression Reference-based compression Spark Distributed parallel Computer applications to medicine. Medical informatics Biology (General) Guangyong Hu verfasserin aut Shangdong Liu verfasserin aut Houzhi Fang verfasserin aut Yimu Ji verfasserin aut In BMC Bioinformatics BMC, 2003 23(2022), 1, Seite 21 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:23 year:2022 number:1 pages:21 https://doi.org/10.1186/s12859-022-04825-5 kostenfrei https://doaj.org/article/04e1c74c25ee429e940b4a64d779814e kostenfrei https://doi.org/10.1186/s12859-022-04825-5 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 21
allfields_unstemmed	10.1186/s12859-022-04825-5 doi (DE-627)DOAJ035621281 (DE-599)DOAJ04e1c74c25ee429e940b4a64d779814e DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Haichang Yao verfasserin aut SparkGC: Spark based genome compression for large collections of genomes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark’s in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://github.com/haichangyao/SparkGC . Genome compression Reference-based compression Spark Distributed parallel Computer applications to medicine. Medical informatics Biology (General) Guangyong Hu verfasserin aut Shangdong Liu verfasserin aut Houzhi Fang verfasserin aut Yimu Ji verfasserin aut In BMC Bioinformatics BMC, 2003 23(2022), 1, Seite 21 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:23 year:2022 number:1 pages:21 https://doi.org/10.1186/s12859-022-04825-5 kostenfrei https://doaj.org/article/04e1c74c25ee429e940b4a64d779814e kostenfrei https://doi.org/10.1186/s12859-022-04825-5 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 21
allfieldsGer	10.1186/s12859-022-04825-5 doi (DE-627)DOAJ035621281 (DE-599)DOAJ04e1c74c25ee429e940b4a64d779814e DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Haichang Yao verfasserin aut SparkGC: Spark based genome compression for large collections of genomes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark’s in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://github.com/haichangyao/SparkGC . Genome compression Reference-based compression Spark Distributed parallel Computer applications to medicine. Medical informatics Biology (General) Guangyong Hu verfasserin aut Shangdong Liu verfasserin aut Houzhi Fang verfasserin aut Yimu Ji verfasserin aut In BMC Bioinformatics BMC, 2003 23(2022), 1, Seite 21 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:23 year:2022 number:1 pages:21 https://doi.org/10.1186/s12859-022-04825-5 kostenfrei https://doaj.org/article/04e1c74c25ee429e940b4a64d779814e kostenfrei https://doi.org/10.1186/s12859-022-04825-5 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 21
allfieldsSound	10.1186/s12859-022-04825-5 doi (DE-627)DOAJ035621281 (DE-599)DOAJ04e1c74c25ee429e940b4a64d779814e DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Haichang Yao verfasserin aut SparkGC: Spark based genome compression for large collections of genomes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark’s in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://github.com/haichangyao/SparkGC . Genome compression Reference-based compression Spark Distributed parallel Computer applications to medicine. Medical informatics Biology (General) Guangyong Hu verfasserin aut Shangdong Liu verfasserin aut Houzhi Fang verfasserin aut Yimu Ji verfasserin aut In BMC Bioinformatics BMC, 2003 23(2022), 1, Seite 21 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:23 year:2022 number:1 pages:21 https://doi.org/10.1186/s12859-022-04825-5 kostenfrei https://doaj.org/article/04e1c74c25ee429e940b4a64d779814e kostenfrei https://doi.org/10.1186/s12859-022-04825-5 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2022 1 21
language	English
source	In BMC Bioinformatics 23(2022), 1, Seite 21 volume:23 year:2022 number:1 pages:21
sourceStr	In BMC Bioinformatics 23(2022), 1, Seite 21 volume:23 year:2022 number:1 pages:21
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Genome compression Reference-based compression Spark Distributed parallel Computer applications to medicine. Medical informatics Biology (General)
isfreeaccess_bool	true
container_title	BMC Bioinformatics
authorswithroles_txt_mv	Haichang Yao @@aut@@ Guangyong Hu @@aut@@ Shangdong Liu @@aut@@ Houzhi Fang @@aut@@ Yimu Ji @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	326644814
id	DOAJ035621281
language_de	englisch
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callnumber-first	R - Medicine
author	Haichang Yao
spellingShingle	Haichang Yao misc R858-859.7 misc QH301-705.5 misc Genome compression misc Reference-based compression misc Spark misc Distributed parallel misc Computer applications to medicine. Medical informatics misc Biology (General) SparkGC: Spark based genome compression for large collections of genomes
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topic_title	R858-859.7 QH301-705.5 SparkGC: Spark based genome compression for large collections of genomes Genome compression Reference-based compression Spark Distributed parallel
topic	misc R858-859.7 misc QH301-705.5 misc Genome compression misc Reference-based compression misc Spark misc Distributed parallel misc Computer applications to medicine. Medical informatics misc Biology (General)
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title	SparkGC: Spark based genome compression for large collections of genomes
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title_sort	sparkgc: spark based genome compression for large collections of genomes
callnumber	R858-859.7
title_auth	SparkGC: Spark based genome compression for large collections of genomes
abstract	Abstract Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark’s in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://github.com/haichangyao/SparkGC .
abstractGer	Abstract Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark’s in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://github.com/haichangyao/SparkGC .
abstract_unstemmed	Abstract Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark’s in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://github.com/haichangyao/SparkGC .
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title_short	SparkGC: Spark based genome compression for large collections of genomes
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