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dc.contributor.authorNeuwald, Andrew F.
dc.contributor.authorKolaczkowski, Bryan D.
dc.contributor.authorAltschul, Stephen F.
dc.date.accessioned2022-08-05T11:13:57Z
dc.date.available2022-08-05T11:13:57Z
dc.date.issued2021-10-15
dc.identifier.urihttp://hdl.handle.net/10713/19516
dc.description.abstractMotivation: Detecting subtle biologically relevant patterns in protein sequences often requires the construction of a large and accurate multiple sequence alignment (MSA). Methods for constructing MSAs are usually evaluated using benchmark alignments, which, however, typically contain very few sequences and are therefore inappropriate when dealing with large numbers of proteins. Results: eCOMPASS addresses this problem using a statistical measure of relative alignment quality based on direct coupling analysis (DCA): to maintain protein structural integrity over evolutionary time, substitutions at one residue position typically result in compensating substitutions at other positions. eCOMPASS computes the statistical significance of the congruence between high scoring directly coupled pairs and 3D contacts in corresponding structures, which depends upon properly aligned homologous residues. We illustrate eCOMPASS using both simulated and realMSAs. © 2021 The Author(s).en_US
dc.description.sponsorshipNational Science Foundationen_US
dc.description.urihttps://doi.org/10.1093/bioinformatics/btab374en_US
dc.language.isoenen_US
dc.publisherOxford University Pressen_US
dc.relation.ispartofBioinformaticsen_US
dc.titleeCOMPASS: evaluative comparison of multiple protein alignments by statistical scoreen_US
dc.typeArticleen_US
dc.identifier.doi10.1093/bioinformatics/btab374
dc.source.journaltitleBioinformatics
dc.source.volume37
dc.source.issue20
dc.source.beginpage3456
dc.source.endpage3463


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