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PASTA: Ultra-Large Multiple Sequence Alignment for Nucleotide and Amino-Acid Sequences

2014· article· en· 463 citations· W2056251063 on OpenAlex· 10.1089/cmb.2014.0156

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Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.

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Machine scores (provisional)

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Opus teacher head0.017
GPT teacher head0.270
Teacher spread
0.253 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

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.

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The record

Venue
Journal of Computational Biology
Topic
Genomics and Phylogenetic Studies
Field
Biochemistry, Genetics and Molecular Biology
Canadian institutions
Funders
National Institute of General Medical SciencesNational Human Genome Research InstituteHoward Hughes Medical InstituteNational Science FoundationNational Institutes of HealthNational Institute on AgingUniversity of AlbertaPennsylvania Department of Health
Keywords
ScalabilityMultiple sequence alignmentParallelizable manifoldComputer scienceSequence (biology)Alignment-free sequence analysisSequence alignmentTree (set theory)AlgorithmComputational biologyData miningBiologyMathematicsPeptide sequenceGenetics
Has abstract in OpenAlex
yes