Identification of natural selection in genomic data with deep convolutional neural network

• Main Authors: Bertorelle, G. (Author), Nguembang Fadja, A. (Author), Riguzzi, F. (Author), Trucchi, E. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: adult; article; convolutional neural network; Convolutional Neural Networks; deep learning; Deep Learning; Genomic data; human tissue; Inference of natural selection; natural selection; simulation; supervised machine learning
• Online Access: View Fulltext in Publisher

 Background: With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to make inferences on demographic and adaptive processes using genomic data. Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics. Results: The paper proposes a method based on Supervised Machine Learning for classifying genomic data, represented as windows of genomic sequences from a sample of individuals belonging to the same population. A Convolutional Neural Network is used to test whether a genomic window shows the signature of natural selection. Training performed on simulated data show that the proposed model can accurately predict neutral and selection processes on portions of genomes taken from real populations with almost 90% accuracy. © 2021, The Author(s).