Estimating amino acid substitution models from genome datasets: a simulation study on the performance of estimated models

Author:

Tinh Nguyen Huy1,Dang Cuong Cao1,Vinh Le Sy1ORCID

Affiliation:

1. Faculty of Information Technology, University of Engineering and Technology, Vietnam National University , Hanoi , Vietnam

Abstract

Abstract Estimating parameters of amino acid substitution models is a crucial task in bioinformatics. The maximum likelihood (ML) approach has been proposed to estimate amino acid substitution models from large datasets. The quality of newly estimated models is normally assessed by comparing with the existing models in building ML trees. Two important questions remained are the correlation of the estimated models with the true models and the required size of the training datasets to estimate reliable models. In this article, we performed a simulation study to answer these two questions based on simulated data. We simulated genome datasets with different numbers of genes/alignments based on predefined models (called true models) and predefined trees (called true trees). The simulated datasets were used to estimate amino acid substitution model using the ML estimation methods. Our experiments showed that models estimated by the ML methods from simulated datasets with more than 100 genes have high correlations with the true models. The estimated models performed well in building ML trees in comparison with the true models. The results suggest that amino acid substitution models estimated by the ML methods from large genome datasets are a reliable tool for analyzing amino acid sequences.

Funder

Vietnam National Foundation for Science and Technology Development

Publisher

Oxford University Press (OUP)

Subject

Ecology, Evolution, Behavior and Systematics

Reference19 articles.

1. FastMG: A simple, fast, and accurate maximum likelihood procedure to estimate amino acid replacement rate matrices from large data sets;Dang,2014

2. NQMaker: Estimating time nonreversible amino acid substitution models;Dang,2022

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4. Bayesian analysis of amino acid substitution models;Huelsenbeck,2008

5. Phylogenomic analyses data of the avian phylogenomics project;Jarvis,2015

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