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

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dc.contributor.author Neehal, Nafis
dc.contributor.author Karim, Dewan Ziaul
dc.contributor.author Islam, Ashraful
dc.date.accessioned 2019-05-16T06:52:09Z
dc.date.available 2019-05-16T06:52:09Z
dc.date.issued 2018-02-08
dc.identifier.isbn 978-1-5386-1151-7
dc.identifier.uri http://hdl.handle.net/123456789/69
dc.description.abstract Sequence alignment in bioinformatics and computational 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. en_US
dc.language.iso en_US en_US
dc.publisher IEEE en_US
dc.subject Cloud computing en_US
dc.subject Clustering algorithms en_US
dc.subject Runtime en_US
dc.subject Proteins en_US
dc.subject Software algorithms en_US
dc.title Cloud-POA: A cloud-based map only implementation of PO-MSA on Amazon multi-node EC2 Hadoop Cluster en_US
dc.type Other en_US


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