Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.22.393108v1?rss=1 Authors: Loewenthal, G., Rapoport, D., Avram, O., Moshe, A., Itzkovitch, A., Israeli, O., Azouri, D., Cartwright, R. A., Mayrose, I., Pupko, T. 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 (ABC) based parameter estimation; (3) We develop a neural-network model-selection scheme to test whether the richer model better fits biological data compared to 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 indel model better fits a large number of empirical datasets and that, for the majority of these datasets, the deletion rate is higher than the insertion rate. Finally, we demonstrate that indel rates are negatively correlated to the effective population size across various phylogenomic clades. Copy rights belong to original authors. Visit the link for more info