SEPA: Approximate non-subjective empirical p-value estimation for nucleotide sequence alignment

Ofer Gill, Bud Mishra

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

Abstract

In the bioinformatics literature, pairwise sequence alignment methods appear with many variations and diverse applications. With this abundance, comes not only an emphasis on speed and memory efficiency, but also a need for assigning confidence to the computed alignments through p-value estimation, especially for important segment pairs within an alignment. This paper examines an empirical technique, called SEPA, for approximate p-value estimation based on statistically large number of observations over randomly generated sequences. Our empirical studies show that the technique remains effective in identifying biological correlations even in sequen es of low similarities and large expected gaps, and the experimental results shown here point to many interesting insights and features.

Original languageEnglish (US)
Title of host publicationComputational Science - ICCS 2006
Subtitle of host publication6th International Conference, Proceedings
PublisherSpringer Verlag
Pages638-645
Number of pages8
ISBN (Print)3540343814, 9783540343813
DOIs
StatePublished - 2006
EventICCS 2006: 6th International Conference on Computational Science - Reading, United Kingdom
Duration: May 28 2006May 31 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3992 LNCS - II
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherICCS 2006: 6th International Conference on Computational Science
CountryUnited Kingdom
CityReading
Period5/28/065/31/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Gill, O., & Mishra, B. (2006). SEPA: Approximate non-subjective empirical p-value estimation for nucleotide sequence alignment. In Computational Science - ICCS 2006: 6th International Conference, Proceedings (pp. 638-645). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3992 LNCS - II). Springer Verlag. https://doi.org/10.1007/11758525_87