A Service-Oriented Platform for Approximate Bayesian Computation in Population Genetics - Marin-Caihuan, J; Rojas-Diaz, O; Solar-Gallardo, R (Mary Ann Liebert Inc., 2019)
Approximate Bayesian computation (ABC) is a useful technique developed for solving Bayesian inference without explicitly requiring a likelihood function. In population genetics, it is widely used to extract part of the information about the evolutionary history of genetic data. The ABC compares the summary statistics computed on simulated and observed data sets. Typically, a forward-in-Time approach is used to simulate the genetic material of a population starting from an initial ancestral population and following the evolution of the individuals by advancing generation by generation under various demographic and genetic forces. This approach is computationally expensive and requires a large number of computations making the use of high-performance computing crucial for decreasing the overall response times. In this work, we propose a fully distributed web service-oriented platform for ABC that is based on forward-in-Time simulations. Our proposal is based on a client-server approach. The client enables users to define simulation scenarios. The server enables efficient and scalable population simulations and can be deployed on a distributed cluster of processors or even in the cloud. It is composed of four services: A workload generator, a simulation controller, a simulation results analyzer, and a result builder. The server performs multithread simulations by executing a simulation kernel encapsulated in a proposed libgdrift library. We present and evaluate three different libgdrift library approaches whose algorithms aim to reduce execution times and memory consumption. © Copyright 2019, Mary Ann Liebert, Inc., publishers 2019.