Statistical significance of normalized global alignment
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Título
Statistical significance of normalized global alignmentFecha de publicación
2015-07-01xmlui.dri2xhtml.METS-1.0.item-edition
PostprintISSN
1066-5277Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://online.liebertpub.com/doi/abs/10.1089/cmb.2012.0167Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
The comparison of homologous proteins from different species is a first step toward a function assignment and a reconstruction of the species evolution. Though local alignment is mostly used for this purpose, global ... [+]
The comparison of homologous proteins from different species is a first step toward a function assignment and a reconstruction of the species evolution. Though local alignment is mostly used for this purpose, global alignment is important for constructing multiple alignments or phylogenetic trees. However, statistical significance of global alignments is not completely clear, lacking a specific statistical model to describe alignments or depending on computationally expensive methods like Z-score. Recently we presented a normalized global alignment, defined as the best compromise between global alignment cost and length, and showed that this new technique led to better classification results than Z-score at a much lower computational cost. However, it is necessary to analyze the statistical significance of the normalized global alignment in order to be considered a completely functional algorithm for protein alignment.
Experiments with unrelated proteins extracted from the SCOP ASTRAL database showed that normalized global alignment scores can be fitted to a log-normal distribution. This fact, obtained without any theoretical support, can be used to derive statistical significance of normalized global alignments. Results are summarized in a table with fitted parameters for different scoring schemes. [-]
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Journal of Computational Biology. March 2014, 21(3): 257-268Derechos de acceso
Copyright©2014 Mary Ann Liebert, Inc. publishers.
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info:eu-repo/semantics/openAccess
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