scholarly journals A probabilistic model for indel evolution: differentiating insertions from deletions

2021 ◽  
Author(s):  
Gil Loewenthal ◽  
Dana Rapoport ◽  
Oren Avram ◽  
Asher Moshe ◽  
Elya Wygoda ◽  
...  
Keyword(s):  
Model Selection ◽  
Approximate Bayesian Computation ◽  
Probabilistic Models ◽  
Simulated Data ◽  
Selection Procedure ◽  
Biological Data ◽  
Selection Scheme ◽  
Insertions And Deletions ◽  
Deletion Rate ◽  
Approximate Bayesian

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 method to correct for biases introduced by alignment programs, when inferring indel parameters from empirical datasets; (4) Using a model-selection scheme we 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 richer 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.

scholarly journals A probabilistic model for indel evolution: differentiating insertions from deletions

2020 ◽  
Author(s):  
Gil Loewenthal ◽  
Dana Rapoport ◽  
Oren Avram ◽  
Asher Moshe ◽  
Alon Itzkovitch ◽  
...  
Keyword(s):  
Model Selection ◽  
Probabilistic Models ◽  
Simulated Data ◽  
Selection Procedure ◽  
Biological Data ◽  
Effective Population ◽  
Selection Scheme ◽  
Insertions And Deletions ◽  
Deletion Rate ◽  
Approximate Bayesian

AbstractInsertions 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.

scholarly journals A Revised Model of Anatomically Modern Human Expansions Out of Africa through a Machine Learning Approximate Bayesian Computation Approach

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1510
Author(s):  
Maria Teresa Vizzari ◽  
Andrea Benazzo ◽  
Guido Barbujani ◽  
Silvia Ghirotto
Keyword(s):  
Approximate Bayesian Computation ◽  
Simulated Data ◽  
Modern Human ◽  
Genomic Variation ◽  
Population History ◽  
Bayesian Computation ◽  
Dispersal Pattern ◽  
High Coverage ◽  
Out Of Africa ◽  
Approximate Bayesian

There is a wide consensus in considering Africa as the birthplace of anatomically modern humans (AMH), but the dispersal pattern and the main routes followed by our ancestors to colonize the world are still matters of debate. It is still an open question whether AMH left Africa through a single process, dispersing almost simultaneously over Asia and Europe, or in two main waves, first through the Arab Peninsula into southern Asia and Australo-Melanesia, and later through a northern route crossing the Levant. The development of new methodologies for inferring population history and the availability of worldwide high-coverage whole-genome sequences did not resolve this debate. In this work, we test the two main out-of-Africa hypotheses through an Approximate Bayesian Computation approach, based on the Random-Forest algorithm. We evaluated the ability of the method to discriminate between the alternative models of AMH out-of-Africa, using simulated data. Once assessed that the models are distinguishable, we compared simulated data with real genomic variation, from modern and archaic populations. This analysis showed that a model of multiple dispersals is four-fold as likely as the alternative single-dispersal model. According to our estimates, the two dispersal processes may be placed, respectively, around 74,000 and around 46,000 years ago.

scholarly journals ABCDP: Approximate Bayesian Computation with Differential Privacy

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 961
Author(s):  
Mijung Park ◽  
Margarita Vinaroz ◽  
Wittawat Jitkrittum
Keyword(s):  
Approximate Bayesian Computation ◽  
Differential Privacy ◽  
Simulated Data ◽  
Bayesian Computation ◽  
Abc Algorithm ◽  
Quantity Of Interest ◽  
Sparse Vector ◽  
Minimal Modification ◽  
Approximate Bayesian ◽  
Privacy Level

We developed a novel approximate Bayesian computation (ABC) framework, ABCDP, which produces differentially private (DP) and approximate posterior samples. Our framework takes advantage of the sparse vector technique (SVT), widely studied in the differential privacy literature. SVT incurs the privacy cost only when a condition (whether a quantity of interest is above/below a threshold) is met. If the condition is sparsely met during the repeated queries, SVT can drastically reduce the cumulative privacy loss, unlike the usual case where every query incurs the privacy loss. In ABC, the quantity of interest is the distance between observed and simulated data, and only when the distance is below a threshold can we take the corresponding prior sample as a posterior sample. Hence, applying SVT to ABC is an organic way to transform an ABC algorithm to a privacy-preserving variant with minimal modification, but yields the posterior samples with a high privacy level. We theoretically analyzed the interplay between the noise added for privacy and the accuracy of the posterior samples. We apply ABCDP to several data simulators and show the efficacy of the proposed framework.

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