PPCAS: Implementation of a Probabilistic Pairwise Model for Consistency-Based Multiple Alignment in Apache Spark

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10393)

Abstract

Large-scale data processing techniques, currently known as Big-Data, are used to manage the huge amount of data that are generated by sequencers. Although these techniques have significant advantages, few biological applications have adopted them. In the Bioinformatic scientific area, Multiple Sequence Alignment (MSA) tools are widely applied for evolution and phylogenetic analysis, homology and domain structure prediction. Highly-rated MSA tools, such as MAFFT, ProbCons and T-Coffee (TC), use the probabilistic consistency as a prior step to the progressive alignment stage in order to improve the final accuracy. In this paper, a novel approach named PPCAS (Probabilistic Pairwise model for Consistency-based multiple alignment in Apache Spark) is presented. PPCAS is based on the MapReduce processing paradigm in order to enable large datasets to be processed with the aim of improving the performance and scalability of the original algorithm.

Keywords

Multiple Sequence Alignment Consistency Spark MapReduce 
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Notes

Acknowledgments

This work was supported by the MEyC-Spain [contract TIN2014-53234-C2-2-R].

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.INSPIRES Research CenterUniversitat de LleidaLleidaSpain

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