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

Abstract 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...

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Autores principales:	Arnaud Nguembang Fadja, Fabrizio Riguzzi, Giorgio Bertorelle, Emiliano Trucchi
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Genomic data Inference of natural selection Deep Learning Convolutional Neural Networks Computer applications to medicine. Medical informatics R858-859.7 Analysis QA299.6-433
Acceso en línea:	https://doaj.org/article/5e540e5ba9c846429f8779d37d55e3f3
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spelling	oai:doaj.org-article:5e540e5ba9c846429f8779d37d55e3f32021-12-05T12:03:54ZIdentification of natural selection in genomic data with deep convolutional neural network10.1186/s13040-021-00280-91756-0381https://doaj.org/article/5e540e5ba9c846429f8779d37d55e3f32021-12-01T00:00:00Zhttps://doi.org/10.1186/s13040-021-00280-9https://doaj.org/toc/1756-0381Abstract 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.Arnaud Nguembang FadjaFabrizio RiguzziGiorgio BertorelleEmiliano TrucchiBMCarticleGenomic dataInference of natural selectionDeep LearningConvolutional Neural NetworksComputer applications to medicine. Medical informaticsR858-859.7AnalysisQA299.6-433ENBioData Mining, Vol 14, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genomic data Inference of natural selection Deep Learning Convolutional Neural Networks Computer applications to medicine. Medical informatics R858-859.7 Analysis QA299.6-433
spellingShingle	Genomic data Inference of natural selection Deep Learning Convolutional Neural Networks Computer applications to medicine. Medical informatics R858-859.7 Analysis QA299.6-433 Arnaud Nguembang Fadja Fabrizio Riguzzi Giorgio Bertorelle Emiliano Trucchi Identification of natural selection in genomic data with deep convolutional neural network
description	Abstract 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.
format	article
author	Arnaud Nguembang Fadja Fabrizio Riguzzi Giorgio Bertorelle Emiliano Trucchi
author_facet	Arnaud Nguembang Fadja Fabrizio Riguzzi Giorgio Bertorelle Emiliano Trucchi
author_sort	Arnaud Nguembang Fadja
title	Identification of natural selection in genomic data with deep convolutional neural network
title_short	Identification of natural selection in genomic data with deep convolutional neural network
title_full	Identification of natural selection in genomic data with deep convolutional neural network
title_fullStr	Identification of natural selection in genomic data with deep convolutional neural network
title_full_unstemmed	Identification of natural selection in genomic data with deep convolutional neural network
title_sort	identification of natural selection in genomic data with deep convolutional neural network
publisher	BMC
publishDate	2021
url	https://doaj.org/article/5e540e5ba9c846429f8779d37d55e3f3
work_keys_str_mv	AT arnaudnguembangfadja identificationofnaturalselectioningenomicdatawithdeepconvolutionalneuralnetwork AT fabrizioriguzzi identificationofnaturalselectioningenomicdatawithdeepconvolutionalneuralnetwork AT giorgiobertorelle identificationofnaturalselectioningenomicdatawithdeepconvolutionalneuralnetwork AT emilianotrucchi identificationofnaturalselectioningenomicdatawithdeepconvolutionalneuralnetwork
_version_	1718372255865503744

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

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