Alignment-Free Sequence Comparison over Hadoop for Computational Biology

Raffaele Giancarlo, Gianluca Roscigno, Umberto Ferraro Petrillo, Giuseppe Cattaneo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Sequence comparison i.e., The assessment of how similar two biological sequences are to each other, is a fundamental and routine task in Computational Biology and Bioinformatics. Classically, alignment methods are the de facto standard for such an assessment. In fact, considerable research efforts for the development of efficient algorithms, both on classic and parallel architectures, has been carried out in the past 50 years. Due to the growing amount of sequence data being produced, a new class of methods has emerged: Alignment-free methods. Research in this ares has become very intense in the past few years, stimulated by the advent of Next Generation Sequencing technologies, since those new methods are very appealing in terms of computational resources needed and biological relevance. Despite such an effort and in contrast with sequence alignment methods, no systematic investigation of how to take advantage of distributed architectures to speed up alignment-free methods, has taken place. We provide a contribution of that kind, by evaluating the possibility of using the Hadoop distributed framework to speed up the running times of these methods, compared to their original sequential formulation
Original languageEnglish
Title of host publicationProceedings of the International Conference on Parallel Processing Workshops
Pages184-192
Number of pages9
Publication statusPublished - 2015

All Science Journal Classification (ASJC) codes

  • Software
  • General Mathematics
  • Hardware and Architecture

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