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

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Algorithms and Architectures for Parallel Processing (ICA3PP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

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Notes

  1. 1.

    Given a MSA containing three sequences x, y, and z, if position \(x_i\) aligns with position \(z_k\) and position \(z_k\) aligns with \(y_j\) in the projected x-z and z-y alignments, then to be consistent the \(x_i\) must align with \(y_j\) in the projected x-y alignment.

  2. 2.

    PPCAS is available on https://github.com/jllados/PPCAS.

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Acknowledgments

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

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Authors and Affiliations

  1. INSPIRES Research Center, Universitat de Lleida, Jaume II, 69, 25001, Lleida, Spain

    Jordi Lladós, Fernando Guirado & Fernando Cores

Authors
  1. Jordi Lladós
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  2. Fernando Guirado
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  3. Fernando Cores
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Corresponding author

Correspondence to Jordi Lladós .

Editor information

Editors and Affiliations

  1. Inria, Rennes, France

    Shadi Ibrahim

  2. University of Texas at San Antonio, San Antonio, Texas, USA

    Kim-Kwang Raymond Choo

  3. Aalto University, Espoo, Finland

    Zheng Yan

  4. University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

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Lladós, J., Guirado, F., Cores, F. (2017). PPCAS: Implementation of a Probabilistic Pairwise Model for Consistency-Based Multiple Alignment in Apache Spark. In: Ibrahim, S., Choo, KK., Yan, Z., Pedrycz, W. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2017. Lecture Notes in Computer Science(), vol 10393. Springer, Cham. https://doi.org/10.1007/978-3-319-65482-9_45

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  • DOI: https://doi.org/10.1007/978-3-319-65482-9_45

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  • Online ISBN: 978-3-319-65482-9

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