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HIVE-Hexagon: High-Performance, Parallelized Sequence Alignment for Next-Generation Sequencing Data Analysis

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  • Luis Santana-Quintero
  • Hayley Dingerdissen
  • Jean Thierry-Mieg
  • Raja Mazumder
  • Vahan Simonyan

Abstract

: Due to the size of Next-Generation Sequencing data, the computational challenge of sequence alignment has been vast. Inexact alignments can take up to 90% of total CPU time in bioinformatics pipelines. High-performance Integrated Virtual Environment (HIVE), a cloud-based environment optimized for storage and analysis of extra-large data, presents an algorithmic solution: the HIVE-hexagon DNA sequence aligner. HIVE-hexagon implements novel approaches to exploit both characteristics of sequence space and CPU, RAM and Input/Output (I/O) architecture to quickly compute accurate alignments. Key components of HIVE-hexagon include non-redundification and sorting of sequences; floating diagonals of linearized dynamic programming matrices; and consideration of cross-similarity to minimize computations. Availability: https://hive.biochemistry.gwu.edu/hive/

Suggested Citation

  • Luis Santana-Quintero & Hayley Dingerdissen & Jean Thierry-Mieg & Raja Mazumder & Vahan Simonyan, 2014. "HIVE-Hexagon: High-Performance, Parallelized Sequence Alignment for Next-Generation Sequencing Data Analysis," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-11, June.
    • Handle: RePEc:plo:pone00:0099033
    DOI: 10.1371/journal.pone.0099033
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    Cited by:

    1. Trent J Bosma & Konstantinos Karagiannis & Luis Santana-Quintero & Natalia Ilyushina & Tatiana Zagorodnyaya & Svetlana Petrovskaya & Majid Laassri & Raymond P Donnelly & Steven Rubin & Vahan Simonyan , 2019. "Identification and quantification of defective virus genomes in high throughput sequencing data using DVG-profiler, a novel post-sequence alignment processing algorithm," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-34, May.

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