MetaPar: Metagenomic sequence assembly via iterative reclassification

Minji Kim, Jonathan G. Ligo, Amin Emad, Farzad Farnoud, Olgica Milenkovic, Venugopal V. Veeravalli

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

Abstract

We introduce a parallel algorithmic architecture for metagenomic sequence assembly, termed MetaPar, which allows for significant reductions in assembly time and consequently enables the processing of large genomic datasets on computers with low memory usage. The gist of the approach is to iteratively perform read (re)classification based on phylogenetic marker genes and assembler outputs generated from random subsets of metagenomic reads. Once a sufficiently accurate classification within genera is performed, de novo metagenomic assemblers (such as Velvet or IDBA-UD) or reference based assemblers may be used for contig construction. We analyze the performance of MetaPar on synthetic data consisting of 15 randomly chosen species from the NCBI database [18] through the effective gap and effective coverage metrics.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages43-46
Number of pages4
DOIs
StatePublished - 2013
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
Country/TerritoryUnited States
CityAustin, TX
Period12/3/1312/5/13

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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