Evidence map›Paper›PMID 20657661›Full record

ArticlePLoS computational biology2010

The construction and use of log-odds substitution scores for multiple sequence alignment.

Stephen F Altschul, John C Wootton, Elena Zaslavsky, Yi-Kuo Yu

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Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

37 citing papers in PubMed.

  1. Article
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  4. Review
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  12. Outlier detection in BLAST hits.Algorithms for molecular biology : AMB · 2018
    Article
  13. Frontiers in microbiology · 2017
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Log-odds sequence logos.Bioinformatics (Oxford, England) · 2015
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Stephen F AltschulNational Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America. altschul@ncbi.nlm.nih.gov
John C Wootton
Elena Zaslavsky
Yi-Kuo Yu

Funding

Statistics of Sequence ComparisonZIALM000014 · NLM · NATIONAL LIBRARY OF MEDICINE · PI ALTSCHUL, STEPHEN F · 2009 to 2020
$3.0M
Computer Analysis Of Low-complexity Amino Acid And Nucleotide SequencesZIALM000025 · NLM · NATIONAL LIBRARY OF MEDICINE · PI WOOTTON, JOHN C. · 2009 to 2010
$561k
Intramural NIH HHSPHS HHS HHSN266200500021C
6 · The paper itself

Abstract

Most pairwise and multiple sequence alignment programs seek alignments with optimal scores. Central to defining such scores is selecting a set of substitution scores for aligned amino acids or nucleotides. For local pairwise alignment, substitution scores are implicitly of log-odds form. We now extend the log-odds formalism to multiple alignments, using Bayesian methods to construct "BILD" ("Bayesian Integral Log-odds") substitution scores from prior distributions describing columns of related letters. This approach has been used previously only to define scores for aligning individual sequences to sequence profiles, but it has much broader applicability. We describe how to calculate BILD scores efficiently, and illustrate their uses in Gibbs sampling optimization procedures, gapped alignment, and the construction of hidden Markov model profiles. BILD scores enable automated selection of optimal motif and domain model widths, and can inform the decision of whether to include a sequence in a multiple alignment, and the selection of insertion and deletion locations. Other applications include the classification of related sequences into subfamilies, and the definition of profile-profile alignment scores. Although a fully realized multiple alignment program must rely upon more than substitution scores, many existing multiple alignment programs can be modified to employ BILD scores. We illustrate how simple BILD score based strategies can enhance the recognition of DNA binding domains, including the Api-AP2 domain in Toxoplasma gondii and Plasmodium falciparum.

Indexed as

Models, StatisticalAlgorithmsAmino Acid SequenceBase SequenceBayes TheoremComputational BiologyConsensus SequenceDatabases, GeneticDNA-Binding ProteinsPattern Recognition, AutomatedPlasmodiumProtein Structure, TertiarySequence AlignmentToxoplasmaDNA-Binding Proteins

Identifiers

PMID20657661
PMCPMC2904766

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.