A Probabilistic Model for Indel Evolution: Differentiating Insertions from Deletions

Gil Loewenthal, Dana Rapoport, Oren Avram, Asher Moshe, Elya Wygoda, Alon Itzkovitch, Omer Israeli, Dana Azouri, Reed A. Cartwright, Itay Mayrose, Tal Pupko

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)5769-5781
Number of pages13
JournalMolecular Biology and Evolution
Volume38
Issue number12
DOIs
StatePublished - 2021

Funding

FundersFunder number
Dalia and Eli Hurvits foundation
Edmond J. Safra Center for Bioinformatics
United States-Israel Binational Science Foundation2015247
Israel Science Foundation802/16, 2818/21
Tel Aviv University

    Keywords

    • Alignments
    • Approximate Bayesian computation
    • Evolutionary models
    • Indels
    • Molecular evolution

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