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Free Drops from Cloud in Bioinformatics

Year 2015, Volume: 1 Issue: 1, 29 - 38, 22.03.2016

Murat Gezer Serra Çelik Çiğdem Selçukcan Erol

Abstract

The need for the benefits of the cloud technology is in almost every discipline, which data size is gradually increasing. Bioinformatics is a field that can produce more data every passing day as a result of emerging scientific advances (high-throughput technologies, etc.). Processing and sharing data is as much important as storing data which can produce results affecting all creatures, particularly human being. In today’s technologies, the road to the light passes through cloud. It is seen that many cloud solutions special to bioinformatics have been created in recent years. These can emerge as software, platform, or infrastructure solutions. In this study, it is aimed to determine positive and negative sides by comparing free cloud infrastructure systems used for bioinformatics data. For this purpose, cloud solutions that can meet the needs of bioinformatics field will be briefly mentioned by giving information about cloud information technologies and free infrastructure solutions will be compared. Consequently, the infrastructure to be established should have support through web in order to make a selection between compared systems. Apart from this, if the software needed in bioinformatics is found as predefined, this will be seen as an important reason for preference for the cloud infrastructure system to be used.

Keywords

Cloud Computing Bioinformatics Cloud Biolinux Cloudman CloVR

References

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  • Afgan E, Baker D, Coraor N, Goto H, Paul IM, Makova KD, Nekrutenko A, Taylor J, (2011), Harnessing cloud computing with Galaxy Cloud. Nat Biotechnol, 29(11), 972–974.
  • Afgan, E., Chapman, B., Jadan, M., Franke, V., Taylor, J. (2012). Using cloud computing infrastructure with CloudBioLinux, CloudMan, and Galaxy. Current Protocols in Bioinformatics, 11-9.
  • Angiuoli SV, Matalka M, Gussman A, Galens K, Vangala M, Riley DR, Arze C, White JR, White O, Fricke WF, (2011), CloVR: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing. BMC Bioinformatics, 12, 356.
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  • Bradshaw, R., Desai, N., Freeman, T., Keahey, K ., (2007), “A scalable approach to deploying and managi ng appliances”, In: TeraGrid Conference 2007
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  • Chetty, M., Buyya, R., (2002), Weaving Computational Grids: How Analogous Are They with Electrical Grids?, Computing in Science and Engineering, 4(4), 61-71.
  • Dai, L., Gao, X., Guo, Y., Xiao, J., Zhang, Z., (2012), “Bioinformatics clouds for big data manipulation”, Biology Direct, 7, 43.
  • Feng X, Grossman R, Stein L (2011), PeakRanger: a cloud-enabled peak caller for ChIPseq data. BMC Bioinformatics, 12, 139.
  • Goncalves, A., A. Tikhonov, A. Brazma, and M. Kapushesky, (2011) “A pipeline for RNA-seq data processing and quality assessment,” Bioinformatics, 27(6), 867–869.
  • Habegger L, Balasubramanian S, Chen DZ, Khurana E, Sboner A, Harmanci A, Rozowsky J, Clarke D, Snyder M, Gerstein M, (2012), VAT: a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment. Bioinformatics. Epub ahead of print.
  • Hong D, Rhie A, Park SS, Lee J, Ju YS, Kim S, Yu SB, Bleazard T, Park HS, Rhee H, (2012), FX: an RNA-Seq analysis tool on the cloud. Bioinformatics, 28(5), 721–723.
  • Jourdren L, Bernard M, Dillies M-A, Le Crom S (2012), Eoulsan: a cloud computingbased framework facilitating high throughput sequencing analyses. Bioinformatics. doi:2010.1093/bioinformatics/bts2165.
  • Kelley, D.R., M.C.Schatz,and S.L.Salzberg, (2010) ,“Quake:quality-aware detection and correction of sequencing errors,” Genome Biology, 11(11), article R116.
  • Krampis K, Booth T, Chapman B, Tiwari B, Bicak M, Field D, Nelson K, (2012), Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community. BMC Bioinformatics, 13(1), 42.
  • Langmead B, Hansen KD, Leek JT, (2010), Cloud-scale RNA-sequencing differential expression analysis with Myrna. Genome Biol, 11(8), R83.
  • Langmead B, Schatz MC, Lin J, Pop M, Salzberg SL, (2009), Searching for SNPs with cloud computing. Genome Biol, 10(11), R134.
  • Lee, H., Y. Yang, H. Chae, (2012) “BioVLAB-MMIA: a cloud environment for microRNA and mRNA integrated analysis (MMIA) on Amazon EC2,” IEEE Transactions on Nanobio-science, 11(3), 266–272. Lin Y-C, Yu, C-S, Lin, Y-J, (2013), “Enabling Large-Scale Biomedical Analysis in the Cloud”, BioMed Research International, 1-6, http://dx.doi.org/10.1155/2013/185679
  • Matsunaga A, Tsugawa M, Fortes J, (2008), Combining MapReduce and Virtualization on Distributed Resources for Bioinformatics Applications.In Fourth IEEE International Conference on eScience, 222–229.
  • McDonald, K. T., (2010), Above the Clouds Managing Risk in the World of Cloud Computing, IT Governance Publishing.
  • McKenna, A., M. Hanna, E. Banks, (2010) “genome analysis toolkit: aMapReduce framework for analyzing next-generation DNA sequencing data,” Genome Research, 20(9), 1297–1303.
  • Nguyen T, Shi W, Ruden D, (2011), CloudAligner: a fast and full-featured MapReduce based tool for sequence mapping. BMC Res Notes, 4, 171.
  • Niemenmaa, M. A. Kallio, A. Schumacher, P. Klemel¨ a, E. Korpelainen, and K. Heljanko, (2012) “Hadoop-BAM: directly manipulating next generation sequencing data in the cloud,” Bioinformatics, 28(6), 876–877.
  • O’Connor, B.D., B.Merriman, and S. F.Nelson, (2010) “SeqWareQuery Engine: storing and searching sequence data in the cloud,” BMC Bioinformatics, 11(12), articleS2.
  • Ostermann, S., Iosup, A., Yigitbasi, N., Prod, R., Fahringer, T., Eperna, D., Avresky, D. R., et al., (2009), A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing, Cloudcomp 2009, LNICST, 34, 115- 131.
  • Pireddu, L., S. Leo, and G. Zanetti, (2011), “Seal:adistributedshortread mapping and duplicate removal tool,” Bioinformatics, 27(15), 2159–2160.
  • Rimal, B. P., Choi, E., Lumb, I., (2009), “A Taxonomy and Survey of Cloud Computing Systems”, Fifth International Joint Conference on INC, IMS and IDC.
  • Schatz MC, (2009), CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics, 25(11), 1363–1369.
  • Schatz, M.C., A.L.Delcher, and S.L.Salzberg, (2010), “Assembly of large genomes using second-generation sequencing,” Genome Research, 20(9), 1165–1173.
  • Tommaso, P.di, M.Orobitg,F.Guirado,F.Cores,T.Espinosa, and C.Notredame, (2010), “Cloud-Cofee: implementation of a parallel consistency-basedmultiple alignment algorithmin the T-cofee package and its benchmarking on the Amazon Elastic-Cloud,” Bioinformatics, 26(15), 1903–1904.
  • Wang Z, Wang Y, Tan KL, Wong L, Agrawal D, (2011), eCEO: an efficient Cloud Epistasis cOmputing model in genome-wide association study. Bioinformatics, 27(8), 1045–1051.
  • Weiss A., (2007), Computing in the Clouds. netWorker, 11(4), 16-25
  • Zhang L, Gu S, Liu Y, Wang B, Azuaje F, (2012), Gene set analysis in the cloud. Bioinformatics, 28(2), 294–295.
  • URL-1: AWS Free Usage Tier (2014). Retrieved April 6,2014,from http://aws.amazon. com/free/
  • URL-2: CloudLinux Included Open Source Software Packages (2013). Retrieved April 6, 2014, from http://www.jcvi.org/cms/research/projects/jcvi-cloud-biolinux/includedsoftware/
  • URL-3: ClovVR Edition Comparison (2013). Retrieved April 6, 2014, from http://clovr.org/developers/edition-comparison/

Year 2015, Volume: 1 Issue: 1, 29 - 38, 22.03.2016

Murat Gezer Serra Çelik Çiğdem Selçukcan Erol

Abstract

References

  • Afgan E, Baker D, Coraor N, Chapman B, Nekrutenko A, Taylor J, (2010), Galaxy CloudMan: delivering cloud compute clusters. BMC Bioinformatics, 11, S4.
  • Afgan E, Baker D, Coraor N, Goto H, Paul IM, Makova KD, Nekrutenko A, Taylor J, (2011), Harnessing cloud computing with Galaxy Cloud. Nat Biotechnol, 29(11), 972–974.
  • Afgan, E., Chapman, B., Jadan, M., Franke, V., Taylor, J. (2012). Using cloud computing infrastructure with CloudBioLinux, CloudMan, and Galaxy. Current Protocols in Bioinformatics, 11-9.
  • Angiuoli SV, Matalka M, Gussman A, Galens K, Vangala M, Riley DR, Arze C, White JR, White O, Fricke WF, (2011), CloVR: a virtual machine for automated and portable sequence analysis from the desktop using cloud computing. BMC Bioinformatics, 12, 356.
  • Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H., Konwinski, A., Lee, G., Patterson, D. A., Rabkin, A., Stoica, I., Zaharia, M., (2009), Above the Clouds: A Berkeley View of Cloud Computing, Electrical Engineering and Computer Sciences University of California at Berkeley, Technical Report No. UCB/EECS-2009-28 http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.html February 10, 2009.
  • Bradshaw, R., Desai, N., Freeman, T., Keahey, K ., (2007), “A scalable approach to deploying and managi ng appliances”, In: TeraGrid Conference 2007
  • Buyya R., Yeo C. S., Venugopal S, Broberg J, Brandic, (2009), “Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility”, Future Gener Comp Sy 2009, 25(6), 599–616.
  • Chetty, M., Buyya, R., (2002), Weaving Computational Grids: How Analogous Are They with Electrical Grids?, Computing in Science and Engineering, 4(4), 61-71.
  • Dai, L., Gao, X., Guo, Y., Xiao, J., Zhang, Z., (2012), “Bioinformatics clouds for big data manipulation”, Biology Direct, 7, 43.
  • Feng X, Grossman R, Stein L (2011), PeakRanger: a cloud-enabled peak caller for ChIPseq data. BMC Bioinformatics, 12, 139.
  • Goncalves, A., A. Tikhonov, A. Brazma, and M. Kapushesky, (2011) “A pipeline for RNA-seq data processing and quality assessment,” Bioinformatics, 27(6), 867–869.
  • Habegger L, Balasubramanian S, Chen DZ, Khurana E, Sboner A, Harmanci A, Rozowsky J, Clarke D, Snyder M, Gerstein M, (2012), VAT: a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment. Bioinformatics. Epub ahead of print.
  • Hong D, Rhie A, Park SS, Lee J, Ju YS, Kim S, Yu SB, Bleazard T, Park HS, Rhee H, (2012), FX: an RNA-Seq analysis tool on the cloud. Bioinformatics, 28(5), 721–723.
  • Jourdren L, Bernard M, Dillies M-A, Le Crom S (2012), Eoulsan: a cloud computingbased framework facilitating high throughput sequencing analyses. Bioinformatics. doi:2010.1093/bioinformatics/bts2165.
  • Kelley, D.R., M.C.Schatz,and S.L.Salzberg, (2010) ,“Quake:quality-aware detection and correction of sequencing errors,” Genome Biology, 11(11), article R116.
  • Krampis K, Booth T, Chapman B, Tiwari B, Bicak M, Field D, Nelson K, (2012), Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community. BMC Bioinformatics, 13(1), 42.
  • Langmead B, Hansen KD, Leek JT, (2010), Cloud-scale RNA-sequencing differential expression analysis with Myrna. Genome Biol, 11(8), R83.
  • Langmead B, Schatz MC, Lin J, Pop M, Salzberg SL, (2009), Searching for SNPs with cloud computing. Genome Biol, 10(11), R134.
  • Lee, H., Y. Yang, H. Chae, (2012) “BioVLAB-MMIA: a cloud environment for microRNA and mRNA integrated analysis (MMIA) on Amazon EC2,” IEEE Transactions on Nanobio-science, 11(3), 266–272. Lin Y-C, Yu, C-S, Lin, Y-J, (2013), “Enabling Large-Scale Biomedical Analysis in the Cloud”, BioMed Research International, 1-6, http://dx.doi.org/10.1155/2013/185679
  • Matsunaga A, Tsugawa M, Fortes J, (2008), Combining MapReduce and Virtualization on Distributed Resources for Bioinformatics Applications.In Fourth IEEE International Conference on eScience, 222–229.
  • McDonald, K. T., (2010), Above the Clouds Managing Risk in the World of Cloud Computing, IT Governance Publishing.
  • McKenna, A., M. Hanna, E. Banks, (2010) “genome analysis toolkit: aMapReduce framework for analyzing next-generation DNA sequencing data,” Genome Research, 20(9), 1297–1303.
  • Nguyen T, Shi W, Ruden D, (2011), CloudAligner: a fast and full-featured MapReduce based tool for sequence mapping. BMC Res Notes, 4, 171.
  • Niemenmaa, M. A. Kallio, A. Schumacher, P. Klemel¨ a, E. Korpelainen, and K. Heljanko, (2012) “Hadoop-BAM: directly manipulating next generation sequencing data in the cloud,” Bioinformatics, 28(6), 876–877.
  • O’Connor, B.D., B.Merriman, and S. F.Nelson, (2010) “SeqWareQuery Engine: storing and searching sequence data in the cloud,” BMC Bioinformatics, 11(12), articleS2.
  • Ostermann, S., Iosup, A., Yigitbasi, N., Prod, R., Fahringer, T., Eperna, D., Avresky, D. R., et al., (2009), A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing, Cloudcomp 2009, LNICST, 34, 115- 131.
  • Pireddu, L., S. Leo, and G. Zanetti, (2011), “Seal:adistributedshortread mapping and duplicate removal tool,” Bioinformatics, 27(15), 2159–2160.
  • Rimal, B. P., Choi, E., Lumb, I., (2009), “A Taxonomy and Survey of Cloud Computing Systems”, Fifth International Joint Conference on INC, IMS and IDC.
  • Schatz MC, (2009), CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics, 25(11), 1363–1369.
  • Schatz, M.C., A.L.Delcher, and S.L.Salzberg, (2010), “Assembly of large genomes using second-generation sequencing,” Genome Research, 20(9), 1165–1173.
  • Tommaso, P.di, M.Orobitg,F.Guirado,F.Cores,T.Espinosa, and C.Notredame, (2010), “Cloud-Cofee: implementation of a parallel consistency-basedmultiple alignment algorithmin the T-cofee package and its benchmarking on the Amazon Elastic-Cloud,” Bioinformatics, 26(15), 1903–1904.
  • Wang Z, Wang Y, Tan KL, Wong L, Agrawal D, (2011), eCEO: an efficient Cloud Epistasis cOmputing model in genome-wide association study. Bioinformatics, 27(8), 1045–1051.
  • Weiss A., (2007), Computing in the Clouds. netWorker, 11(4), 16-25
  • Zhang L, Gu S, Liu Y, Wang B, Azuaje F, (2012), Gene set analysis in the cloud. Bioinformatics, 28(2), 294–295.
  • URL-1: AWS Free Usage Tier (2014). Retrieved April 6,2014,from http://aws.amazon. com/free/
  • URL-2: CloudLinux Included Open Source Software Packages (2013). Retrieved April 6, 2014, from http://www.jcvi.org/cms/research/projects/jcvi-cloud-biolinux/includedsoftware/
  • URL-3: ClovVR Edition Comparison (2013). Retrieved April 6, 2014, from http://clovr.org/developers/edition-comparison/
There are 37 citations in total.

Details

Primary Language English
Journal Section ARTICLES
Authors

Murat Gezer

Serra Çelik This is me

Çiğdem Selçukcan Erol This is me

Publication Date March 22, 2016
Published in Issue Year 2015 Volume: 1 Issue: 1

Cite

APA Gezer, M., Çelik, S., & Selçukcan Erol, Ç. (2016). Free Drops from Cloud in Bioinformatics. Istanbul Journal of Innovation in Education, 1(1), 29-38.
AMA Gezer M, Çelik S, Selçukcan Erol Ç. Free Drops from Cloud in Bioinformatics. Istanbul Journal of Innovation in Education. March 2016;1(1):29-38.
Chicago Gezer, Murat, Serra Çelik, and Çiğdem Selçukcan Erol. “Free Drops from Cloud in Bioinformatics”. Istanbul Journal of Innovation in Education 1, no. 1 (March 2016): 29-38.
EndNote Gezer M, Çelik S, Selçukcan Erol Ç (March 1, 2016) Free Drops from Cloud in Bioinformatics. Istanbul Journal of Innovation in Education 1 1 29–38.
IEEE M. Gezer, S. Çelik, and Ç. Selçukcan Erol, “Free Drops from Cloud in Bioinformatics”, Istanbul Journal of Innovation in Education, vol. 1, no. 1, pp. 29–38, 2016.
ISNAD Gezer, Murat et al. “Free Drops from Cloud in Bioinformatics”. Istanbul Journal of Innovation in Education 1/1 (March 2016), 29-38.
JAMA Gezer M, Çelik S, Selçukcan Erol Ç. Free Drops from Cloud in Bioinformatics. Istanbul Journal of Innovation in Education. 2016;1:29–38.
MLA Gezer, Murat et al. “Free Drops from Cloud in Bioinformatics”. Istanbul Journal of Innovation in Education, vol. 1, no. 1, 2016, pp. 29-38.
Vancouver Gezer M, Çelik S, Selçukcan Erol Ç. Free Drops from Cloud in Bioinformatics. Istanbul Journal of Innovation in Education. 2016;1(1):29-38.
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