A Probabilistic Model for Indel Evolution: Differentiating Insertions from Deletions
| Autor: | Tal Pupko, Omer Israeli, Oren Avram, Itay Mayrose, Dana Azouri, Elya Wygoda, Alon Itzkovitch, Reed A. Cartwright, Asher Moshe, Gil Loewenthal, Dana Rapoport |
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| Rok vydání: | 2021 |
| Předmět: |
Computational complexity theory
Inference Biology AcademicSubjects/SCI01180 Evolution Molecular evolutionary models approximate Bayesian computation INDEL Mutation Methods Genetics Indel Molecular Biology Phylogeny Ecology Evolution Behavior and Systematics Biological data Models Statistical molecular evolution Estimation theory AcademicSubjects/SCI01130 Probabilistic logic alignments Bayes Theorem Statistical model indels Approximate Bayesian computation Algorithm |
| Zdroj: | Molecular Biology and Evolution |
| ISSN: | 1537-1719 |
| Popis: | Insertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here, we introduce several improvements to indel modeling: 1) While previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here we propose a richer model that explicitly distinguishes between the two; 2) we introduce numerous summary statistics that allow approximate Bayesian computation-based parameter estimation; 3) we develop a method to correct for biases introduced by alignment programs, when inferring indel parameters from empirical data sets; and 4) using a model-selection scheme, we test whether the richer model better fits biological data compared with the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed richer model better fits a large number of empirical data sets and that, for the majority of these data sets, the deletion rate is higher than the insertion rate. |
| Databáze: | OpenAIRE |
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