Estimating amino acid substitution models from genome datasets : a simulation study on the performance of estimated models
© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Evolutionary Biology. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:37 |
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| Enthalten in: |
Journal of evolutionary biology - 37(2024), 2 vom: 14. Feb., Seite 256-265 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tinh, Nguyen Huy [VerfasserIn] |
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| Links: |
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| Themen: |
Amino acid substitution models |
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| Anmerkungen: |
Date Completed 19.02.2024 Date Revised 19.02.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/jeb/voad017 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Evolutionary Biology. All rights reserved. For permissions, please e-mail: journals.permissionsoup.com. | ||
| 520 | |a 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 | ||
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| 700 | 1 | |a Vinh, Le Sy |e verfasserin |4 aut | |
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