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 (US)
Pages (from-to)5769-5781
Number of pages13
JournalMolecular biology and evolution
Volume38
Issue number12
DOIs
StatePublished - 2021
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics

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