Detecting amino acid preference shifts with codon-level mutation-selection mixture models View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

S. Omar Kazmi, Nicolas Rodrigue

ABSTRACT

BACKGROUND: In recent years, increasing attention has been placed on the development of phylogeny-based statistical methodologies for uncovering site-specific changes in amino acid fitness profiles over time. The few available random-effects approaches, modelling across-site variation in amino acid profiles as random variables drawn from a statistical law, either lack a mechanistic codon-level formulation, or pose significant computational challenges. RESULTS: Here, we bring together a few existing ideas to explore a simple and fast method based on a predefined finite mixture of amino acid profiles within a codon-level substitution model following the mutation-selection formulation. Our study is focused on the detection of site-specific shifts in amino acid profiles over a known sub-clade of a tree, using simulations with and without shifts over the sub-clade to study the properties of the method. Through modifications of the values of the amino acid profiles, our simulations show different levels of reliability under different forms of finite mixture models. Sites identified by our method in a real data set show obvious overlap with those identified using previous methods, with some notable differences. CONCLUSION: Overall, our results show that when a site-specific shift in amino acid profile is strongly pronounced, involving two clearly different sets of profiles, the method performs very well; but shifts between profiles that share many features are difficult to correctly identify, highlighting the challenging nature of the problem. More... »

PAGES

62

Journal

TITLE

BMC Evolutionary Biology

ISSUE

1

VOLUME

19

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12862-019-1358-7

DOI

http://dx.doi.org/10.1186/s12862-019-1358-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112388626

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30808289


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