Text mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes

Abstract Autism is a spectrum disorder with wide variation in type and severity of symptoms. Understanding gene–phenotype associations is vital to unravel the disease mechanisms and advance its diagnosis and treatment. To date, several databases have stored a large portion of gene–phenotype associat...

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Autores principales:	Sijie Li, Ziqi Guo, Jacob B. Ioffe, Yunfei Hu, Yi Zhen, Xin Zhou
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cf105b54b2c14a6e844db2f624cc83a9
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spelling	oai:doaj.org-article:cf105b54b2c14a6e844db2f624cc83a92021-12-02T16:06:43ZText mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes10.1038/s41598-021-94742-z2045-2322https://doaj.org/article/cf105b54b2c14a6e844db2f624cc83a92021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94742-zhttps://doaj.org/toc/2045-2322Abstract Autism is a spectrum disorder with wide variation in type and severity of symptoms. Understanding gene–phenotype associations is vital to unravel the disease mechanisms and advance its diagnosis and treatment. To date, several databases have stored a large portion of gene–phenotype associations which are mainly obtained from genetic experiments. However, a large proportion of gene–phenotype associations are still buried in the autism-related literature and there are limited resources to investigate autism-associated gene–phenotype associations. Given the abundance of the autism-related literature, we were thus motivated to develop Autism_genepheno, a text mining pipeline to identify sentence-level mentions of autism-associated genes and phenotypes in literature through natural language processing methods. We have generated a comprehensive database of gene–phenotype associations in the last five years’ autism-related literature that can be easily updated as new literature becomes available. We have evaluated our pipeline through several different approaches, and we are able to rank and select top autism-associated genes through their unique and wide spectrum of phenotypic profiles, which could provide a unique resource for the diagnosis and treatment of autism. The data resources and the Autism_genpheno pipeline are available at: https://github.com/maiziezhoulab/Autism_genepheno .Sijie LiZiqi GuoJacob B. IoffeYunfei HuYi ZhenXin ZhouNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sijie Li Ziqi Guo Jacob B. Ioffe Yunfei Hu Yi Zhen Xin Zhou Text mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes
description	Abstract Autism is a spectrum disorder with wide variation in type and severity of symptoms. Understanding gene–phenotype associations is vital to unravel the disease mechanisms and advance its diagnosis and treatment. To date, several databases have stored a large portion of gene–phenotype associations which are mainly obtained from genetic experiments. However, a large proportion of gene–phenotype associations are still buried in the autism-related literature and there are limited resources to investigate autism-associated gene–phenotype associations. Given the abundance of the autism-related literature, we were thus motivated to develop Autism_genepheno, a text mining pipeline to identify sentence-level mentions of autism-associated genes and phenotypes in literature through natural language processing methods. We have generated a comprehensive database of gene–phenotype associations in the last five years’ autism-related literature that can be easily updated as new literature becomes available. We have evaluated our pipeline through several different approaches, and we are able to rank and select top autism-associated genes through their unique and wide spectrum of phenotypic profiles, which could provide a unique resource for the diagnosis and treatment of autism. The data resources and the Autism_genpheno pipeline are available at: https://github.com/maiziezhoulab/Autism_genepheno .
format	article
author	Sijie Li Ziqi Guo Jacob B. Ioffe Yunfei Hu Yi Zhen Xin Zhou
author_facet	Sijie Li Ziqi Guo Jacob B. Ioffe Yunfei Hu Yi Zhen Xin Zhou
author_sort	Sijie Li
title	Text mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes
title_short	Text mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes
title_full	Text mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes
title_fullStr	Text mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes
title_full_unstemmed	Text mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes
title_sort	text mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/cf105b54b2c14a6e844db2f624cc83a9
work_keys_str_mv	AT sijieli textminingofgenephenotypeassociationsrevealsnewphenotypicprofilesofautismassociatedgenes AT ziqiguo textminingofgenephenotypeassociationsrevealsnewphenotypicprofilesofautismassociatedgenes AT jacobbioffe textminingofgenephenotypeassociationsrevealsnewphenotypicprofilesofautismassociatedgenes AT yunfeihu textminingofgenephenotypeassociationsrevealsnewphenotypicprofilesofautismassociatedgenes AT yizhen textminingofgenephenotypeassociationsrevealsnewphenotypicprofilesofautismassociatedgenes AT xinzhou textminingofgenephenotypeassociationsrevealsnewphenotypicprofilesofautismassociatedgenes
_version_	1718384932765564928

Text mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes

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