PSAR: Measuring multiple sequence alignment reliability by probabilistic sampling (Extended abstract)

Jaebum Kim, Jian Ma

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

Abstract

Multiple sequence alignment (MSA), which is of fundamental importance for comparative genomics, is a difficult problem and error-prone. Therefore, it is essential to measure the reliability of the alignments and incorporate it into downstream analyses. Many studies have been conducted to find the extent, cause and effect of the alignment errors [4], and to heuristically estimate the quality of alignments without using the true alignment, which is unknown [2]. However, it is still unclear whether the heuristically chosen measures are general enough to take into account all alignment errors. In this paper, we present a new alignment reliability score, called PSAR (Probabilistic Sampling-based Alignment Reliability) score.

Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology - 15th Annual International Conference, RECOMB 2011, Proceedings
Pages134-135
Number of pages2
DOIs
StatePublished - Apr 4 2011
Event15th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2011 - Vancouver, BC, Canada
Duration: Mar 28 2011Mar 31 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6577 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2011
CountryCanada
CityVancouver, BC
Period3/28/113/31/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Kim, J., & Ma, J. (2011). PSAR: Measuring multiple sequence alignment reliability by probabilistic sampling (Extended abstract). In Research in Computational Molecular Biology - 15th Annual International Conference, RECOMB 2011, Proceedings (pp. 134-135). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6577 LNBI). https://doi.org/10.1007/978-3-642-20036-6_14