ImaGene: a convolutional neural network to quantify natural selection from genomic data

Background The genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determining such genetic bases is an evolutionary framework....

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Vydáno v:	BMC bioinformatics Ročník 20; číslo Suppl 9; s. 337 - 12
Hlavní autoři:	Torada, Luis, Lorenzon, Lucrezia, Beddis, Alice, Isildak, Ulas, Pattini, Linda, Mathieson, Sara, Fumagalli, Matteo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 22.11.2019 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Adaptation Algorithms Alleles Artificial neural networks Binding sites Bioinformatics Biological evolution Biomedical and Life Sciences Cable television broadcasting industry Color Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer simulation Continuity (mathematics) Convolutional neural networks Databases, Genetic Deep learning Demographics Evolution (Biology) Evolutionary biology Evolutionary genetics Gene mapping Genetic research Genetics, Population Genomes Genomics Genomics - methods Humans Image manipulation Learning strategies Life Sciences Machine learning Mapping Microarrays Molecular modelling Mutation Natural selection Neural networks Neural Networks, Computer Novels Pattern recognition Phenotype Phenotypes Pipelines Polygenic inheritance Population Population genetics Positive selection Selection, Genetic Signatures Simulation Software Supervised machine learning United States Supervised machine learning Convolutional neural networks Population genetics Natural selection
ISSN:	1471-2105, 1471-2105
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Popis
Shrnutí:	Background The genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determining such genetic bases is an evolutionary framework. As sites targeted by natural selection are likely to harbor important functionalities for the carrier, the identification of selection signatures in the genome has the potential to unveil the genetic mechanisms underpinning human phenotypes. Popular methods of detecting such signals rely on compressing genomic information into summary statistics, resulting in the loss of information. Furthermore, few methods are able to quantify the strength of selection. Here we explored the use of deep learning in evolutionary biology and implemented a program, called ImaGene , to apply convolutional neural networks on population genomic data for the detection and quantification of natural selection. Results ImaGene enables genomic information from multiple individuals to be represented as abstract images. Each image is created by stacking aligned genomic data and encoding distinct alleles into separate colors. To detect and quantify signatures of positive selection, ImaGene implements a convolutional neural network which is trained using simulations. We show how the method implemented in ImaGene can be affected by data manipulation and learning strategies. In particular, we show how sorting images by row and column leads to accurate predictions. We also demonstrate how the misspecification of the correct demographic model for producing training data can influence the quantification of positive selection. We finally illustrate an approach to estimate the selection coefficient, a continuous variable, using multiclass classification techniques. Conclusions While the use of deep learning in evolutionary genomics is in its infancy, here we demonstrated its potential to detect informative patterns from large-scale genomic data. We implemented methods to process genomic data for deep learning in a user-friendly program called ImaGene . The joint inference of the evolutionary history of mutations and their functional impact will facilitate mapping studies and provide novel insights into the molecular mechanisms associated with human phenotypes.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-019-2927-x