Conference Paper2017Cited by 2

Cloud-POA: A cloud-based map only implementation of PO-MSA on Amazon multi-node EC2 Hadoop Cluster

Nafis Neehal, Dewan Ziaul Karim, Ashraful Islam

2017 20th International Conference of Computer and Information Technology (ICCIT)

IEEE, pp. 1–6

DOI: 10.1109/iccitechn.2017.8281808URL

Abstract

Sequence alignment in bioinformatics and compu-tational biology has always been a challenging task. With Next Generation Sequencing (NGS) techniques in hand, researchers are now capable of studying biological systems at a level never been possible before. Scientists now have billions of bytes of biological data to work with, trillions of sequences to align. But this comes at a cost of requiring computing machines having a tremendous amount of computational and analytical power. Purchasing this huge amount of hardware and setting up a standalone infrastructure would not only cost an unnecessarily massive amount of money and labor but also would become troublesome to maintain. Moreover, for aligning a huge number of DNA or Protein sequences a scalable multiple sequence alignment (MSA) algorithms is needed with decent accuracy. In such context, this paper presents a novel implementation of Partial Order Alignment (POA) algorithm on a multi-node Hadoop Cluster running on MapReduce framework. The implementation was done in Amazon AWS platform with multiple EC2 instances. It is a map-only implementation with Hadoop Streaming. The result of this implementation shows a drastic reduction in runtime with no accuracy degradation.

Keywords

  • multiple sequence alignment
  • Amazon EC2
  • MapReduce
  • Cloud computing
  • Hadoop
  • Amazon Web Services
  • Scalability
  • execution time reduction
  • sequence alignment

CCDS Authors

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

References

  1. 1.Julie Thompson, Desmond G. Higgins, Toby J. Gibson. (1994). CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research, 22(22), 4673–4680[10.1093/nar/22.22.4673]
  2. 2.R. C. Edgar. (2004). MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research, 32(5), 1792–1797[10.1093/nar/gkh340]
  3. 3.Jay B. Dean, Sanjay Ghemawat. (2008). MapReduce. Communications of the ACM, 51(1), 107–113[10.1145/1327452.1327492]
  4. 4.R. Chenna. (2003). Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Research, 31(13), 3497–3500[10.1093/nar/gkg500]
  5. 5.Desmond G. Higgins, Paul M. Sharp. (1988). CLUSTAL: a package for performing multiple sequence alignment on a microcomputer. Gene, 73(1), 237–244[10.1016/0378-1119(88)90330-7]
  6. 6.Christopher Lee, Catherine S. Grasso, Mark Sharlow. (2002). Multiple sequence alignment using partial order graphs. Bioinformatics, 18(3), 452–464[10.1093/bioinformatics/18.3.452]
  7. 7.Michael C. Schatz. (2009). CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics, 25(11), 1363–1369[10.1093/bioinformatics/btp236]
  8. 8.Pedro Romero. (2004). Bioinformatics: Sequence and Genome Analysis. Briefings in Bioinformatics, 5(4), 393–396[10.1093/bib/5.4.393-a]
  9. 9.Paulien Hogeweg, B. Hesper. (1984). The alignment of sets of sequences and the construction of phyletic trees: An integrated method. Journal of Molecular Evolution, 20(2), 175–186[10.1007/bf02257378]
  10. 10.Burkhard Morgenstern. (2004). DIALIGN: multiple DNA and protein sequence alignment at BiBiServ. Nucleic Acids Research, 32(Web Server), W33–W36[10.1093/nar/gkh373]
  11. 11.Tung Nguyen, Weisong Shi, Douglas M. Ruden. (2011). CloudAligner: A fast and full-featured MapReduce based tool for sequence mapping. BMC Research Notes, 4(1), 171[10.1186/1756-0500-4-171]
  12. 12.Osamu Gotoh. (1996). Significant Improvement in Accuracy of Multiple Protein Sequence Alignments by Iterative Refinement as Assessed by Reference to Structural Alignments. Journal of Molecular Biology, 264(4), 823–838[10.1006/jmbi.1996.0679]
  13. 13.Makoto Hirosawa, Yasushi Totoki, Masaki Hoshida, Masato Ishikawa. (1995). Comprehensive study on iterative algorithms of multiple sequence alignment. Computer applications in the biosciences, 11(1), 13–18[10.1093/bioinformatics/11.1.13]
  14. 14.Christopher J. Lee. (2003). Generating consensus sequences from partialorder multiple sequence alignment graphs. Bioinformatics, 19(8), 999–1008[10.1093/bioinformatics/btg109]
  15. 15.Yu-Jung Chang, Chien-Chih Chen, Chuen-Liang Chen, Jan-Ming Ho. (2012). A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing framework. BMC Genomics, 13(S7), S28[10.1186/1471-2164-13-s7-s28]
  16. 16.Serita M. Nelesen, Keechul Jung Qiaoge Liu, Donggao Zhao, C. Randal Linder, Tandy Warnow. (2007). THE EFFECT OF THE GUIDE TREE ON MULTIPLE SEQUENCE ALIGNMENTS AND SUBSEQUENT PHYLOGENETIC ANALYSES. , 25–36[10.1142/9789812776136_0004]

Cited By

  1. 1.Faisal Arafat, Thaharim Khan, Atanu Das Bapon, Md. Ibrahim Khan, Sheak Rashed Haider Noori. (2021). A Deep Learning Approach to Predict Chronic Kidney Disease in Human. 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 1010–1015[10.1109/iemcon53756.2021.9623101]
  2. 2.Sergio H. Almanza-Ruiz, Arturo Chavoya, Hector A. Duran-Limon. (2022). Parallel protein multiple sequence alignment approaches: a systematic literature review. The Journal of Supercomputing, 79(2), 1201–1234[10.1007/s11227-022-04697-9]
← All publications