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Making sense of score statistics for sequence alignments

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Bibliographic Details
Title: Making sense of score statistics for sequence alignments
Authors: Pagni, M., Jongeneel, C. V.
Publication Year: 2025
Collection: Université de Lausanne (UNIL): Serval - Serveur académique lausannois
Subject Terms: Amino Acid Sequence Animals Computational Biology Databases, Factual Humans Markov Chains Models, Statistical Molecular Sequence Data Proteins/genetics Sequence Alignment/*statistics & numerical data Sequence Homology, Amino Acid
Description: The search for similarity between two biological sequences lies at the core of many applications in bioinformatics. This paper aims to highlight a few of the principles that should be kept in mind when evaluating the statistical significance of alignments between sequences. The extreme value distribution is first introduced, which in most cases describes the distribution of alignment scores between a query and a database. The effects of the similarity matrix and gap penalty values on the score distribution are then examined, and it is shown that the alignment statistics can undergo an abrupt phase transition. A few types of random sequence databases used in the estimation of statistical significance are presented, and the statistics employed by the BLAST, FASTA and PRSS programs are compared. Finally the different strategies used to assess the statistical significance of the matches produced by profiles and hidden Markov models are presented.
Document Type: article in journal/newspaper
Language: unknown
ISSN: 1467-5463
Relation: Briefings in Bioinformatics; https://iris.unil.ch/handle/iris/204414; serval:BIB_CD62AA985FEC
Availability: https://iris.unil.ch/handle/iris/204414
Accession Number: edsbas.13AF5E2D
Database: BASE
Description
Abstract:The search for similarity between two biological sequences lies at the core of many applications in bioinformatics. This paper aims to highlight a few of the principles that should be kept in mind when evaluating the statistical significance of alignments between sequences. The extreme value distribution is first introduced, which in most cases describes the distribution of alignment scores between a query and a database. The effects of the similarity matrix and gap penalty values on the score distribution are then examined, and it is shown that the alignment statistics can undergo an abrupt phase transition. A few types of random sequence databases used in the estimation of statistical significance are presented, and the statistics employed by the BLAST, FASTA and PRSS programs are compared. Finally the different strategies used to assess the statistical significance of the matches produced by profiles and hidden Markov models are presented.
ISSN:14675463