Honolulu, HI, USA USA
June 24, 2012 to June 29, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CLOUD.2012.34
In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. This information can effectively be used for medical and biological research only if one can extract functional insight from it. To obtain functional insight the factors to be considered while aligning sequences are: optimized querying of sequences, high speed matching and accuracy of alignment. The FAST-All (FASTA) for both proteins and nucleotides program considers all these factors and follows a largely heuristic method, which contributes to the high speed of its execution. The program initially observes the pattern of word hits, word-to-word matches of a given length, and marks potential matches rather than performing a more time-consuming, optimized search using a Smith-Waterman type of algorithm. In this paper, we propose an optimized approach to sequence alignment using FASTA algorithm, which incorporates high speed word-to-word matching. In the current scenario where data growth is in petabytes a day and processing requires state of the art technologies, Greenplum Massively Parallel Processing (MPP) database and Hadoop are emerging parallel technologies which form the backbone of this proposal. The complex nature of the algorithm, coupled with data and computational parallelism of Hadoop grid and Massively Parallel Processing database for querying from big datasets containing petabytes of sequences, improves the accuracy, speed of sequence alignment and optimizes querying from big datasets. Bioinformatics labs and centers across the globe today upload enormous amount of data and sequences in a central location for the scientific analysis. The transfer of such large datasets can also be simplified with Cloud approaches. So, Cloud Computing Technology is used in our implementation for the ease of gathering such sequences and data from various sources like medical research centers, scientists and biomedical labs around the globe. A plan for the final "publicly consumable" form of the program is to make it web-based and running on the Cloud.
Green products, Cloud computing, Bioinformatics, Genomics, Parallel processing, Distributed databases, FASTA, Computational Biology, Sequence Alignment, Cloud Computing, Hadoop, Greenplum MPP Database
Senthilkumar Vijayakumar, Anjani Bhargavi, Uma Praseeda, Syed Azar Ahamed, "Optimizing Sequence Alignment in Cloud Using Hadoop and MPP Database", CLOUD, 2012, 2013 IEEE Sixth International Conference on Cloud Computing, 2013 IEEE Sixth International Conference on Cloud Computing 2012, pp. 819-827, doi:10.1109/CLOUD.2012.34