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An Overview of Multiple Sequence Alignments and Cloud Computing in Bioinformatics
Author(s) -
Jurate Daugelaite,
Aisling O’Driscoll,
Roy D. Sleator
Publication year - 2013
Publication title -
isrn biomathematics
Language(s) - English
Resource type - Journals
ISSN - 2090-7702
DOI - 10.1155/2013/615630
Subject(s) - bottleneck , cloud computing , computer science , sequence (biology) , sequence database , computer cluster , dna sequencing , alignment free sequence analysis , data mining , multiple sequence alignment , bioinformatics , sequence alignment , data science , computational biology , distributed computing , biology , dna , peptide sequence , genetics , gene , embedded system , operating system
Multiple sequence alignment (MSA) of DNA, RNA, and protein sequences is one of the most essential techniques in the fields of molecular biology, computational biology, and bioinformatics. Next-generation sequencing technologies are changing the biology landscape, flooding the databases with massive amounts of raw sequence data. MSA of ever-increasing sequence data sets is becoming a significant bottleneck. In order to realise the promise of MSA for large-scale sequence data sets, it is necessary for existing MSA algorithms to be run in a parallelised fashion with the sequence data distributed over a computing cluster or server farm. Combining MSA algorithms with cloud computing technologies is therefore likely to improve the speed, quality, and capability for MSA to handle large numbers of sequences. In this review, multiple sequence alignments are discussed, with a specific focus on the ClustalW and Clustal Omega algorithms. Cloud computing technologies and concepts are outlined, and the next generation of cloud base MSA algorithms is introduced.

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