PASTA: Ultra-large multiple sequence alignment for nucleotide and amino-acid sequences

Siavash Mirarab, Nam Nguyen, Sheng Guo, Li San Wang, Junhyong Kim, Tandy Warnow

Research output: Contribution to journalArticle

Abstract

We introduce PASTA, a new multiple sequence alignment algorithm. PASTA uses a new technique to produce an alignment given a guide tree that enables it to be both highly scalable and very accurate. We present a study on biological and simulated data with up to 200,000 sequences, showing that PASTA produces highly accurate alignments, improving on the accuracy and scalability of the leading alignment methods (including SATé). We also show that trees estimated on PASTA alignments are highly accurate - slightly better than SATé trees, but with substantial improvements relative to other methods. Finally, PASTA is faster than SATé, highly parallelizable, and requires relatively little memory.

Original languageEnglish (US)
Pages (from-to)377-386
Number of pages10
JournalJournal of Computational Biology
Volume22
Issue number5
DOIs
StatePublished - May 1 2015

Keywords

  • algorithms
  • metagenomics
  • molecular evolution
  • multiple alignment
  • phylogenetic trees

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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