The Journal of Supercomputing

, Volume 73, Issue 4, pp 1467–1483 | Cite as

An effective extension of the applicability of alignment-free biological sequence comparison algorithms with Hadoop

  • Giuseppe Cattaneo
  • Umberto Ferraro Petrillo
  • Raffaele Giancarlo
  • Gianluca Roscigno
Article

Abstract

Alignment-free methods are one of the mainstays of biological sequence comparison, i.e., the assessment of how similar two biological sequences are to each other, a fundamental and routine task in computational biology and bioinformatics. They have gained popularity since, even on standard desktop machines, they are faster than methods based on alignments. However, with the advent of Next-Generation Sequencing Technologies, datasets whose size, i.e., number of sequences and their total length, is a challenge to the execution of alignment-free methods on those standard machines are quite common. Here, we propose the first paradigm for the computation of k-mer-based alignment-free methods for Apache Hadoop that extends the problem sizes that can be processed with respect to a standard sequential machine while also granting a good time performance. Technically, as opposed to a standard Hadoop implementation, its effectiveness is achieved thanks to the incremental management of a persistent hash table during the map phase, a task not contemplated by the basic Hadoop functions and that can be useful also in other contexts.

Keywords

Alignment-free sequence comparison and analysis Distributed computing MapReduce Hadoop 

Notes

Acknowledgments

We would like to thank the Department of Statistical Sciences of University of Rome-La Sapienza for computing time on the TeraStat cluster and Nicola Segata for providing the meta-genomic dataset. We also would like to thank the referees for comments that helped in the presentation of our results.

Compliance with ethical standards

Funding

MIUR PRIN Project: 2010RTFWBH_003 “Data-Centric Genomic Computing (GenData 2020)” and Unipa Progetto di Ateneo 2012-ATE-0298 “Metodi Formali ed Algoritmici per la Bioinformatica su Scala Genomica”.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Giuseppe Cattaneo
    • 1
  • Umberto Ferraro Petrillo
    • 2
  • Raffaele Giancarlo
    • 3
  • Gianluca Roscigno
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di SalernoFiscianoItaly
  2. 2.Dipartimento di Scienze StatisticheUniversità di Roma “Sapienza”RomeItaly
  3. 3.Dipartimento di Matematica ed InformaticaUniversità degli Studi di PalermoPalermoItaly

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