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Yeni Nesil Dizileme Verilerinin Analizinde Bulut Teknolojisi

Year 2022, Volume: 11 Issue: 1, 1 - 10, 13.06.2022

Sema Karabudak Meryem Sena Akkuş

https://doi.org/10.17100/nevbiltek.1005534
Cited By: 2

Abstract

Yeni nesil dizileme (YND) araçları, büyük miktarda veri üretme kapasitesine sahiptir ancak dizileme sonrası büyük ölçekli veri analizi için yeterli olmayan hesaplama ve depolama kapasitesi ile donatılmışlardır. Bulut bilişim altyapılarını kullanmak YND verilerinin analizi, depolanması ve aktarılması ile ilgili sorunlara alternatif bir seçenek olmuştur. Bulut bilişim, kullanıcılara dizileme verilerinin analizi için gerekli hesaplama kapasitesi ve bilişim altyapılarına erişim sunmakta ve biyoinformatik altyapıları için gerekli olan ön sermaye harcamalarının çoğunu ortadan kaldırmaktadır. Yapılan bu çalışmada yeni nesil dizileme yöntemi ve dizileme verilerinin analizinde kullanılan bulut bilişim platformaları hakkında bilgi verilmiştir.

Keywords

Veri Analizi Yeni Nesil Dizileme Biyoinformatik bulut bilişim

References

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  • [2] Barba, M., Czosnek, H., & Hadidi, A.” Historical perspective, development and applications of next-generation sequencing in plant virology,” Viruses, 6, 106–136, 2014 . [3] Goodwin, S., McPherson, J. & McCombie, W., “Coming of age: ten years of next-generation sequencing Technologies,” Nat Rev Genet, 17, 333–351, 2016.
  • [4] Kwon T., Yoo W. G., W.-J. Lee W.J., Kim W., Kim D.W., "Next-generation sequencing data analysis on cloud computing," Genes & Genomics, 37, 489-501, 2015.
  • [5] Pereira M., Malta F., Freire M., and Couto P., "Application of Next-Generation Sequencing in the Era of Precision Medicine. In Applications of RNA-Seq and Omics Strategies – From Microorganisms to Human Health", Intech Open, 2017.
  • [6] Celesti F., Celesti A., Carnevale L., Galletta A., Campo S., Romano A., "Big data analytics in genomics: The point on Deep Learning solutions," 22nd IEEE Symposium on Computers and Communications (ISCC), Abstract Book, 306-309, 2017.
  • [7] Schmidt B. , Hildebrandt A., "Next-generation sequencing: Big data meets high performance computing," Drug Discovery Today, 22, 712-717, 2017.
  • [8] Zhao S., Watrous K., Zhang C., and Zhang B., "Cloud Computing for Next-Generation Sequencing Data Analysis," InTechOpen, 29–51, 2017 . [9] Thakur R., Bandopadhyay R., Chaudhary B., Chatterjee S., "Now and next-generation sequencing techniques: Future of sequence analysis using cloud computing," Frontiers in Genetics, 3, 280-280, 2012.
  • [10] Langmead B. and Nellore A., "Cloud computing for genomic data analysis and collaboration," Nature Reviews Genetics, 19, 208-219, 2018.
  • [11] Baker Q. B., Al-Rashdan W., and Jararweh Y., "Cloud-Based Tools for Next-Generation Sequencing Data Analysis," 2018 5th International Conference on Social Networks Analysis, Management and Security (SNAMS), Abstract Book 99-105s, Valencia-Spain, 2018.
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  • [14] Goyal S., "Public vs private vs hybrid vs community - cloud computing: A critical review," International Journal of Computer Network and Information Security, 6, 20-29, 2014.
  • [15] Zhao S., Prenger K., Smith L., Messina T., Fan H., Jaeger E., "Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing," BMC Genomics, 14, 425-425, 2013.
  • [16] Wang, D., Song, L., Singh, V., Rao, S., An, L., Madhavan, S., “SNP2Structure: a public and versatile resource for mapping and three-dimensional modeling of missense SNPs on human protein structures,” Computational and structural biotechnology journal, 13, 514-519, 2015.
  • [17] Oh, J., Choi, C. H., Park, M. K., Kim, B. K., Hwang, K., Lee, S. H.,Kim, K. M., “Clustom-cloud: In-memory data grid-based software for clustering 16s rrna sequence data in the cloud environment,” PloS one, 11, e0151064, (2016).
  • [18] Chae, H., Rhee, S., Nephew, K. P., Kim, S., ”BioVLAB-MMIA-NGS: microRNA–mRNA integrated analysis using high-throughput sequencing data,” Bioinformatics, 31, 265-267, 2015.
  • [19] White, J., Arze, C., Matalka, M., Team, T. C., Angiuoli, S., Fricke, W. F., “CloVR-Metagenomics: Functional and taxonomic microbial community characterization from metagenomic whole-genome shotgun (WGS) sequences–standard operating procedure,” Nature Precedings, 1, 1-1, 2011.
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  • [24] Dai, L., Gao, X., Guo, Y., Xiao, J., Zhang, Z., “Bioinformatics clouds for big data manipulation,” Biology direct, 7, 1-7, 2012.
  • [25] Wang,D., Song,L., Singh,V., Rao,S., An,L., Madhavan,S., ‘’ SNP2Structure: A Public and Versatile Resource for Mapping and Three-Dimensional Modeling of Missense SNPs on Human Protein Structures,’’ Computational and structural biotechnology journal, 13, 514-519, 2015.
  • [26] Schatz,M., ‘’CloudBurst: highly sensitive read mapping with MapReduce’’ Bioinformatics, 25,1363-1369, 2009.
  • [27] Habegger, L., Balasubramanian, S., Chen, DZ., Khurana,E., Sboner,A., Harmanci,A., Rozowsky,J., Clarke,D., Snyder,M., Gerstein,M., “VAT: a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment,’’ Bioinformatics. 28,2267-2269,2012.
  • [28] Langmead B., Hansen K., Leek J., "Cloud-scale RNA-sequencing differential expression analysis with Myrna," Genome Biology, 11, R83, 2010.
  • [29] Schatz, Michael C. "BlastReduce: high performance short read mapping with MapReduce." University of Maryland, http://cgis. cs. umd. edu/Grad/scholarlypapers/papers/MichaelSchatz. pdf, 2008.
  • [30] Pireddu, L., Leo, S., Zanetti, G., “SEAL: a distributed short read mapping and duplicate removal tool,” Bioinformatics, 27, 2159-2160, 2011.
  • [31] Chang, Y. J., Chen, C. C., Ho, J. M., & Chen, C. L., “De novo assembly of high-throughput sequencing data with cloud computing and new operations on string graphs,” In 2012 IEEE Fifth International Conference on Cloud Computing IEEE, 155-161, 2012.
  • [32] Schönherr, S., Forer, L., Weißensteiner, H., Kronenberg, F., Specht, G., & Kloss-Brandstätter, A., “Cloudgene: a graphical execution platform for MapReduce programs on private and public clouds,” BMC bioinformatics, 13, 1-9, 2012.
  • [33] Li, Bo, Gould, J., Yang, Y., Sarkizova, S., Tabaka, M., Ashenberg, O., Regev, A. "Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq." Nature methods, 17, 793-798, 2020.
  • [34] Nordberg H., Bhatia K., Wang K., Wang Z., "Biopig: a Hadoop-based analytic toolkit for large-scale sequence data," Bioinformatics, 29, 23, 2013.
  • [35] Challis, D., Yu, J., Evani, U. S., Jackson, A. R., Paithankar, S., Coarfa, C., Yu, F., “An integrative variant analysis suite for whole exome next-generation sequencing data,” BMC bioinformatics, 13, 1-12, 2012.
  • [36] Lu W., Jackson J., Barga R., "AzureBlast: A case study of developing science applications on the cloud," 2010. Conference: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC 2010, 21-25 June 2010, 413-420, Chicago, Illinois, USA, 2010.
  • [37] Zhang L., Gu S., Liu Y., Wang B., Azuaje F., "Gene set analysis in the cloud," Bioinformatics, 28, 294-295, 2012.
  • [38] Karczewski, K. J., Fernald, G. H., Martin, A. R., Snyder, M., Tatonetti, N. P., Dudley, J. T.,” STORMSeq: an open-source, user-friendly pipeline for processing personal genomics data in the cloud,” PloS one, 9, e84860, 2014.
  • [39] Zhao, S., Prenger, K., Smith, L., Stormbow: a cloud-based tool for reads mapping and expression quantification in large-scale RNA-Seq studies,” ISRN Bioinformatics, 2013, 1-8, 2013.
  • [40] Zhao, S., Prenger, K., Smith, L., Stormbow: a cloud-based tool for reads mapping and expression quantification in large-scale RNA-Seq studies,” ISRN Bioinformatics, 2013, 1-8, 2013.
  • [41] Li, Y., Zhong, S., “SeqMapReduce: software and web service for accelerating sequence mapping,” Critical Assessment of Massive Data Anaysis (CAMDA), 2009, 1-5, 2009.
  • [42] Gurtowski J., Schatz M. C., Langmead B., "Genotyping in the cloud with Crossbow," Current Protocols in Bioinformatics, 15, Unit15.3, 2012.
  • [43] Jourdren L., Bernard M., Dillies M.-A., Crom S. Le, "Eoulsan: A cloud computing-based framework facilitating high throughput sequencing analyses," Bioinformatics, 28, 1542-1543, 2012.
  • [44] Blankenberg, D., Hillman-Jackson, J., “Analysis of next-generation sequencing data using Galaxy,” In Stem cell transcriptional networks, Humana Press, New York, 21-43, 2014.
  • [45] Afgan, E., Baker, D., Coraor, N., Goto, H., Paul, I. M., Makova, K. D.,Taylor, J., “Harnessing cloud computing with Galaxy Cloud,” Nature biotechnology, 29, 972-974, 2011.
  • [46] Wiewiórka M. S., Messina A., Pacholewska A., Maffioletti S., Gawrysiak P., Okoniewski M. J., "SparkSeq: fast, scalable and cloud-ready tool for the interactive genomic data analysis with nucleotide precision," Bioinformatics, 30, 2652-2653, 2014.
  • [47] Krampis K., Booth T., Chapman B., Tiwari B., Bicak M., "Cloud BioLinux: Pre-configured and on-demand bioinformatics computing for the genomics community," BMC Bioinformatics, 13, 42, 2012. [48] Afgan, E., Chapman, B., Taylor, J., ”CloudMan as a platform for tool, data, and analysis distribution,” BMC bioinformatics, 13, 1-7, 2012.
  • [49] Oh, J., Choi, C. H., Park, M. K., Kim, B. K., Hwang, K., Lee, S. H., Kim, K. M., “Clustom-cloud: In-memory data grid-based software for clustering 16s rRNA sequence data in the cloud environment,” PloS one, 11, e0151064, 2016.
  • [50] Schumacher A., Pireddu L., Niemenmaa M., Kallio A., Korpelainen E., Zanetti G., "SeqPig: simple and scalable scripting for large sequencing data sets in Hadoop," Bioinformatics, 30(1), 119-120, 2013.
  • [51] Navale V., Bourne P. E., "Cloud computing applications for biomedical science: A perspective," PLoS Computational Biology, 14,1006144, 2018.
  • [52] Nordberg H., Bhatia K., Wang K., Wang Z., "Biopig: a Hadoop-based analytic toolkit for large-scale sequence data," Bioinformatics, 29, 23, 2013.
  • [53] Zhao, Y., Tang, H., Ye, Y.,”RAPSearch2: a fast and memory-efficient protein similarity search tool for next-generation sequencing data,” Bioinformatics, 28, 125-126, 2012.
  • [54] Afgan, E., Baker, D., Coraor, N., Chapman, B., Nekrutenko, A., Taylor, J., “Galaxy CloudMan: delivering cloud compute clusters,” BMC bioinformatics, 11, 1-6, 2010.
  • [55] Nguyen T., Shi W., Ruden D., "CloudAligner: A fast and full-featured MapReduce based tool for sequence mapping," BMC Research Notes, 4, 171, 2011.
  • [56] McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., DePristo, M. A., “The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data,” Genome research, 20, 1297-1303, 2010.
  • [57] Huson, D. H., Weber, N., “Microbial community analysis using MEGAN,” Methods in enzymology, 531, 465-485, 2013.
  • [58] Keegan, K. P., Glass, E. M., Meyer, F., “MG-RAST, a metagenomics service for analysis of microbial community structure and function. Microbial environmental genomics,” Humana Press, New York, 207-233, 2016.
  • [59] Stewart, A. C., Osborne, B., Read, T. D., “DIYA: a bacterial annotation pipeline for any genomics lab.,” Bioinformatics, 25, 962-963, 2009.

Year 2022, Volume: 11 Issue: 1, 1 - 10, 13.06.2022

Sema Karabudak Meryem Sena Akkuş

https://doi.org/10.17100/nevbiltek.1005534
Cited By: 2

Abstract

References

  • [1]Behjati S., Tarpey P. S., "What is next generation sequencing?," Archives of Disease in Childhood. Education and Practice Edition, 98, 236-238, 2013.
  • [2] Barba, M., Czosnek, H., & Hadidi, A.” Historical perspective, development and applications of next-generation sequencing in plant virology,” Viruses, 6, 106–136, 2014 . [3] Goodwin, S., McPherson, J. & McCombie, W., “Coming of age: ten years of next-generation sequencing Technologies,” Nat Rev Genet, 17, 333–351, 2016.
  • [4] Kwon T., Yoo W. G., W.-J. Lee W.J., Kim W., Kim D.W., "Next-generation sequencing data analysis on cloud computing," Genes & Genomics, 37, 489-501, 2015.
  • [5] Pereira M., Malta F., Freire M., and Couto P., "Application of Next-Generation Sequencing in the Era of Precision Medicine. In Applications of RNA-Seq and Omics Strategies – From Microorganisms to Human Health", Intech Open, 2017.
  • [6] Celesti F., Celesti A., Carnevale L., Galletta A., Campo S., Romano A., "Big data analytics in genomics: The point on Deep Learning solutions," 22nd IEEE Symposium on Computers and Communications (ISCC), Abstract Book, 306-309, 2017.
  • [7] Schmidt B. , Hildebrandt A., "Next-generation sequencing: Big data meets high performance computing," Drug Discovery Today, 22, 712-717, 2017.
  • [8] Zhao S., Watrous K., Zhang C., and Zhang B., "Cloud Computing for Next-Generation Sequencing Data Analysis," InTechOpen, 29–51, 2017 . [9] Thakur R., Bandopadhyay R., Chaudhary B., Chatterjee S., "Now and next-generation sequencing techniques: Future of sequence analysis using cloud computing," Frontiers in Genetics, 3, 280-280, 2012.
  • [10] Langmead B. and Nellore A., "Cloud computing for genomic data analysis and collaboration," Nature Reviews Genetics, 19, 208-219, 2018.
  • [11] Baker Q. B., Al-Rashdan W., and Jararweh Y., "Cloud-Based Tools for Next-Generation Sequencing Data Analysis," 2018 5th International Conference on Social Networks Analysis, Management and Security (SNAMS), Abstract Book 99-105s, Valencia-Spain, 2018.
  • [12] Zhang Q., Cheng L., and Boutaba R., "Cloud Computing: State-of-the-art and research challenges," Journal of Internet Services and Applications, 1, 7-18, 2010.
  • [13] Dai, L., Gao, X., Guo, Y., Xiao, J., Zhang, Z., “Bioinformatics clouds for big data manipulation,” Biology direct, 7, 1-7, 2012.
  • [14] Goyal S., "Public vs private vs hybrid vs community - cloud computing: A critical review," International Journal of Computer Network and Information Security, 6, 20-29, 2014.
  • [15] Zhao S., Prenger K., Smith L., Messina T., Fan H., Jaeger E., "Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing," BMC Genomics, 14, 425-425, 2013.
  • [16] Wang, D., Song, L., Singh, V., Rao, S., An, L., Madhavan, S., “SNP2Structure: a public and versatile resource for mapping and three-dimensional modeling of missense SNPs on human protein structures,” Computational and structural biotechnology journal, 13, 514-519, 2015.
  • [17] Oh, J., Choi, C. H., Park, M. K., Kim, B. K., Hwang, K., Lee, S. H.,Kim, K. M., “Clustom-cloud: In-memory data grid-based software for clustering 16s rrna sequence data in the cloud environment,” PloS one, 11, e0151064, (2016).
  • [18] Chae, H., Rhee, S., Nephew, K. P., Kim, S., ”BioVLAB-MMIA-NGS: microRNA–mRNA integrated analysis using high-throughput sequencing data,” Bioinformatics, 31, 265-267, 2015.
  • [19] White, J., Arze, C., Matalka, M., Team, T. C., Angiuoli, S., Fricke, W. F., “CloVR-Metagenomics: Functional and taxonomic microbial community characterization from metagenomic whole-genome shotgun (WGS) sequences–standard operating procedure,” Nature Precedings, 1, 1-1, 2011.
  • [20] Fricke, W., White, J., Arze, Matalka,M., White,O., Angiuoli,S., ‘’CloVR-Metagenomics: Functional and taxonomic microbial community characterization from metagenomic whole-genome shotgun (WGS) sequences – standard operating procedure, version 1.0.’’ Nature Precedings, 1,1-1,2011.
  • [21] White, O., Angiuoli, S., Fricke, W. F., Galens, K., White, J., Arze, C., Team, T. C., “CloVR-Microbe: Assembly, gene finding and functional annotation of raw sequence data from single microbial genome projects–standard operating procedure,” Nature Precedings, 1, 1-1, 2011.
  • [22] http://clovr.org/methods/clovr-search/
  • [23] Orvis, J., Crabtree, J., Galens, K., Gussman, A., Inman, J. M., Lee, E., Angiuoli, S. V., “Ergatis: a web interface and scalable software system for bioinformatics workflows,” Bioinformatics, 26, 1488-1492, 2010.
  • [24] Dai, L., Gao, X., Guo, Y., Xiao, J., Zhang, Z., “Bioinformatics clouds for big data manipulation,” Biology direct, 7, 1-7, 2012.
  • [25] Wang,D., Song,L., Singh,V., Rao,S., An,L., Madhavan,S., ‘’ SNP2Structure: A Public and Versatile Resource for Mapping and Three-Dimensional Modeling of Missense SNPs on Human Protein Structures,’’ Computational and structural biotechnology journal, 13, 514-519, 2015.
  • [26] Schatz,M., ‘’CloudBurst: highly sensitive read mapping with MapReduce’’ Bioinformatics, 25,1363-1369, 2009.
  • [27] Habegger, L., Balasubramanian, S., Chen, DZ., Khurana,E., Sboner,A., Harmanci,A., Rozowsky,J., Clarke,D., Snyder,M., Gerstein,M., “VAT: a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment,’’ Bioinformatics. 28,2267-2269,2012.
  • [28] Langmead B., Hansen K., Leek J., "Cloud-scale RNA-sequencing differential expression analysis with Myrna," Genome Biology, 11, R83, 2010.
  • [29] Schatz, Michael C. "BlastReduce: high performance short read mapping with MapReduce." University of Maryland, http://cgis. cs. umd. edu/Grad/scholarlypapers/papers/MichaelSchatz. pdf, 2008.
  • [30] Pireddu, L., Leo, S., Zanetti, G., “SEAL: a distributed short read mapping and duplicate removal tool,” Bioinformatics, 27, 2159-2160, 2011.
  • [31] Chang, Y. J., Chen, C. C., Ho, J. M., & Chen, C. L., “De novo assembly of high-throughput sequencing data with cloud computing and new operations on string graphs,” In 2012 IEEE Fifth International Conference on Cloud Computing IEEE, 155-161, 2012.
  • [32] Schönherr, S., Forer, L., Weißensteiner, H., Kronenberg, F., Specht, G., & Kloss-Brandstätter, A., “Cloudgene: a graphical execution platform for MapReduce programs on private and public clouds,” BMC bioinformatics, 13, 1-9, 2012.
  • [33] Li, Bo, Gould, J., Yang, Y., Sarkizova, S., Tabaka, M., Ashenberg, O., Regev, A. "Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq." Nature methods, 17, 793-798, 2020.
  • [34] Nordberg H., Bhatia K., Wang K., Wang Z., "Biopig: a Hadoop-based analytic toolkit for large-scale sequence data," Bioinformatics, 29, 23, 2013.
  • [35] Challis, D., Yu, J., Evani, U. S., Jackson, A. R., Paithankar, S., Coarfa, C., Yu, F., “An integrative variant analysis suite for whole exome next-generation sequencing data,” BMC bioinformatics, 13, 1-12, 2012.
  • [36] Lu W., Jackson J., Barga R., "AzureBlast: A case study of developing science applications on the cloud," 2010. Conference: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC 2010, 21-25 June 2010, 413-420, Chicago, Illinois, USA, 2010.
  • [37] Zhang L., Gu S., Liu Y., Wang B., Azuaje F., "Gene set analysis in the cloud," Bioinformatics, 28, 294-295, 2012.
  • [38] Karczewski, K. J., Fernald, G. H., Martin, A. R., Snyder, M., Tatonetti, N. P., Dudley, J. T.,” STORMSeq: an open-source, user-friendly pipeline for processing personal genomics data in the cloud,” PloS one, 9, e84860, 2014.
  • [39] Zhao, S., Prenger, K., Smith, L., Stormbow: a cloud-based tool for reads mapping and expression quantification in large-scale RNA-Seq studies,” ISRN Bioinformatics, 2013, 1-8, 2013.
  • [40] Zhao, S., Prenger, K., Smith, L., Stormbow: a cloud-based tool for reads mapping and expression quantification in large-scale RNA-Seq studies,” ISRN Bioinformatics, 2013, 1-8, 2013.
  • [41] Li, Y., Zhong, S., “SeqMapReduce: software and web service for accelerating sequence mapping,” Critical Assessment of Massive Data Anaysis (CAMDA), 2009, 1-5, 2009.
  • [42] Gurtowski J., Schatz M. C., Langmead B., "Genotyping in the cloud with Crossbow," Current Protocols in Bioinformatics, 15, Unit15.3, 2012.
  • [43] Jourdren L., Bernard M., Dillies M.-A., Crom S. Le, "Eoulsan: A cloud computing-based framework facilitating high throughput sequencing analyses," Bioinformatics, 28, 1542-1543, 2012.
  • [44] Blankenberg, D., Hillman-Jackson, J., “Analysis of next-generation sequencing data using Galaxy,” In Stem cell transcriptional networks, Humana Press, New York, 21-43, 2014.
  • [45] Afgan, E., Baker, D., Coraor, N., Goto, H., Paul, I. M., Makova, K. D.,Taylor, J., “Harnessing cloud computing with Galaxy Cloud,” Nature biotechnology, 29, 972-974, 2011.
  • [46] Wiewiórka M. S., Messina A., Pacholewska A., Maffioletti S., Gawrysiak P., Okoniewski M. J., "SparkSeq: fast, scalable and cloud-ready tool for the interactive genomic data analysis with nucleotide precision," Bioinformatics, 30, 2652-2653, 2014.
  • [47] Krampis K., Booth T., Chapman B., Tiwari B., Bicak M., "Cloud BioLinux: Pre-configured and on-demand bioinformatics computing for the genomics community," BMC Bioinformatics, 13, 42, 2012. [48] Afgan, E., Chapman, B., Taylor, J., ”CloudMan as a platform for tool, data, and analysis distribution,” BMC bioinformatics, 13, 1-7, 2012.
  • [49] Oh, J., Choi, C. H., Park, M. K., Kim, B. K., Hwang, K., Lee, S. H., Kim, K. M., “Clustom-cloud: In-memory data grid-based software for clustering 16s rRNA sequence data in the cloud environment,” PloS one, 11, e0151064, 2016.
  • [50] Schumacher A., Pireddu L., Niemenmaa M., Kallio A., Korpelainen E., Zanetti G., "SeqPig: simple and scalable scripting for large sequencing data sets in Hadoop," Bioinformatics, 30(1), 119-120, 2013.
  • [51] Navale V., Bourne P. E., "Cloud computing applications for biomedical science: A perspective," PLoS Computational Biology, 14,1006144, 2018.
  • [52] Nordberg H., Bhatia K., Wang K., Wang Z., "Biopig: a Hadoop-based analytic toolkit for large-scale sequence data," Bioinformatics, 29, 23, 2013.
  • [53] Zhao, Y., Tang, H., Ye, Y.,”RAPSearch2: a fast and memory-efficient protein similarity search tool for next-generation sequencing data,” Bioinformatics, 28, 125-126, 2012.
  • [54] Afgan, E., Baker, D., Coraor, N., Chapman, B., Nekrutenko, A., Taylor, J., “Galaxy CloudMan: delivering cloud compute clusters,” BMC bioinformatics, 11, 1-6, 2010.
  • [55] Nguyen T., Shi W., Ruden D., "CloudAligner: A fast and full-featured MapReduce based tool for sequence mapping," BMC Research Notes, 4, 171, 2011.
  • [56] McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., DePristo, M. A., “The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data,” Genome research, 20, 1297-1303, 2010.
  • [57] Huson, D. H., Weber, N., “Microbial community analysis using MEGAN,” Methods in enzymology, 531, 465-485, 2013.
  • [58] Keegan, K. P., Glass, E. M., Meyer, F., “MG-RAST, a metagenomics service for analysis of microbial community structure and function. Microbial environmental genomics,” Humana Press, New York, 207-233, 2016.
  • [59] Stewart, A. C., Osborne, B., Read, T. D., “DIYA: a bacterial annotation pipeline for any genomics lab.,” Bioinformatics, 25, 962-963, 2009.
There are 56 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Derleme Makalesi/Review Article
Authors

Sema Karabudak 0000-0002-3646-0442

Meryem Sena Akkuş 0000-0003-2550-550X

Early Pub Date June 13, 2022
Publication Date June 13, 2022
Acceptance Date January 25, 2022
Published in Issue Year 2022 Volume: 11 Issue: 1

Cite

APA Karabudak, S., & Akkuş, M. S. (2022). Yeni Nesil Dizileme Verilerinin Analizinde Bulut Teknolojisi. Nevşehir Bilim Ve Teknoloji Dergisi, 11(1), 1-10. https://doi.org/10.17100/nevbiltek.1005534
AMA Karabudak S, Akkuş MS. Yeni Nesil Dizileme Verilerinin Analizinde Bulut Teknolojisi. Nevşehir Bilim ve Teknoloji Dergisi. June 2022;11(1):1-10. doi:10.17100/nevbiltek.1005534
Chicago Karabudak, Sema, and Meryem Sena Akkuş. “Yeni Nesil Dizileme Verilerinin Analizinde Bulut Teknolojisi”. Nevşehir Bilim Ve Teknoloji Dergisi 11, no. 1 (June 2022): 1-10. https://doi.org/10.17100/nevbiltek.1005534.
EndNote Karabudak S, Akkuş MS (June 1, 2022) Yeni Nesil Dizileme Verilerinin Analizinde Bulut Teknolojisi. Nevşehir Bilim ve Teknoloji Dergisi 11 1 1–10.
IEEE S. Karabudak and M. S. Akkuş, “Yeni Nesil Dizileme Verilerinin Analizinde Bulut Teknolojisi”, Nevşehir Bilim ve Teknoloji Dergisi, vol. 11, no. 1, pp. 1–10, 2022, doi: 10.17100/nevbiltek.1005534.
ISNAD Karabudak, Sema - Akkuş, Meryem Sena. “Yeni Nesil Dizileme Verilerinin Analizinde Bulut Teknolojisi”. Nevşehir Bilim ve Teknoloji Dergisi 11/1 (June 2022), 1-10. https://doi.org/10.17100/nevbiltek.1005534.
JAMA Karabudak S, Akkuş MS. Yeni Nesil Dizileme Verilerinin Analizinde Bulut Teknolojisi. Nevşehir Bilim ve Teknoloji Dergisi. 2022;11:1–10.
MLA Karabudak, Sema and Meryem Sena Akkuş. “Yeni Nesil Dizileme Verilerinin Analizinde Bulut Teknolojisi”. Nevşehir Bilim Ve Teknoloji Dergisi, vol. 11, no. 1, 2022, pp. 1-10, doi:10.17100/nevbiltek.1005534.
Vancouver Karabudak S, Akkuş MS. Yeni Nesil Dizileme Verilerinin Analizinde Bulut Teknolojisi. Nevşehir Bilim ve Teknoloji Dergisi. 2022;11(1):1-10.

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