FunTaxIS-lite:一个简单而轻巧的解决方案,用于研究所有生物体中的蛋白质功能。

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad549
Federico Bianca, Emilio Ispano, Ermanno Gazzola, Enrico Lavezzo, Paolo Fontana, Stefano Toppo
{"title":"FunTaxIS-lite:一个简单而轻巧的解决方案,用于研究所有生物体中的蛋白质功能。","authors":"Federico Bianca,&nbsp;Emilio Ispano,&nbsp;Ermanno Gazzola,&nbsp;Enrico Lavezzo,&nbsp;Paolo Fontana,&nbsp;Stefano Toppo","doi":"10.1093/bioinformatics/btad549","DOIUrl":null,"url":null,"abstract":"<p><strong>Motivation: </strong>Defining the full domain of protein functions belonging to an organism is a complex challenge that is due to the huge heterogeneity of the taxonomy, where single or small groups of species can bear unique functional characteristics. FunTaxIS-lite provides a solution to this challenge by determining taxon-based constraints on Gene Ontology (GO) terms, which specify the functions that an organism can or cannot perform. The tool employs a set of rules to generate and spread the constraints across both the taxon hierarchy and the GO graph.</p><p><strong>Results: </strong>The taxon-based constraints produced by FunTaxIS-lite extend those provided by the Gene Ontology Consortium by an average of 300%. The implementation of these rules significantly reduces errors in function predictions made by automatic algorithms and can assist in correcting inconsistent protein annotations in databases.</p><p><strong>Availability and implementation: </strong>FunTaxIS-lite is available on https://www.medcomp.medicina.unipd.it/funtaxis-lite and from https://github.com/MedCompUnipd/FunTaxIS-lite.</p>","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500080/pdf/","citationCount":"0","resultStr":"{\"title\":\"FunTaxIS-lite: a simple and light solution to investigate protein functions in all living organisms.\",\"authors\":\"Federico Bianca,&nbsp;Emilio Ispano,&nbsp;Ermanno Gazzola,&nbsp;Enrico Lavezzo,&nbsp;Paolo Fontana,&nbsp;Stefano Toppo\",\"doi\":\"10.1093/bioinformatics/btad549\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Motivation: </strong>Defining the full domain of protein functions belonging to an organism is a complex challenge that is due to the huge heterogeneity of the taxonomy, where single or small groups of species can bear unique functional characteristics. FunTaxIS-lite provides a solution to this challenge by determining taxon-based constraints on Gene Ontology (GO) terms, which specify the functions that an organism can or cannot perform. The tool employs a set of rules to generate and spread the constraints across both the taxon hierarchy and the GO graph.</p><p><strong>Results: </strong>The taxon-based constraints produced by FunTaxIS-lite extend those provided by the Gene Ontology Consortium by an average of 300%. The implementation of these rules significantly reduces errors in function predictions made by automatic algorithms and can assist in correcting inconsistent protein annotations in databases.</p><p><strong>Availability and implementation: </strong>FunTaxIS-lite is available on https://www.medcomp.medicina.unipd.it/funtaxis-lite and from https://github.com/MedCompUnipd/FunTaxIS-lite.</p>\",\"PeriodicalId\":8903,\"journal\":{\"name\":\"Bioinformatics\",\"volume\":\"39 9\",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2023-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500080/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/bioinformatics/btad549\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btad549","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

动机:由于分类学的巨大异质性,定义属于生物体的蛋白质功能的完整域是一项复杂的挑战,其中单个或小群体的物种可以具有独特的功能特征。FunTaxIS-lite通过确定基因本体(GO)术语的基于分类的约束来解决这一挑战,这些术语指定了生物体能执行或不能执行的功能。该工具使用一组规则在分类单元层次结构和GO图中生成和传播约束。结果:FunTaxIS-lite提供的基于分类的约束平均比Gene Ontology Consortium提供的约束扩展了300%。这些规则的实现大大减少了自动算法在功能预测中的错误,并有助于纠正数据库中不一致的蛋白质注释。可用性和实现:FunTaxIS-lite可从https://www.medcomp.medicina.unipd.it/funtaxis-lite和https://github.com/MedCompUnipd/FunTaxIS-lite获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
FunTaxIS-lite: a simple and light solution to investigate protein functions in all living organisms.

Motivation: Defining the full domain of protein functions belonging to an organism is a complex challenge that is due to the huge heterogeneity of the taxonomy, where single or small groups of species can bear unique functional characteristics. FunTaxIS-lite provides a solution to this challenge by determining taxon-based constraints on Gene Ontology (GO) terms, which specify the functions that an organism can or cannot perform. The tool employs a set of rules to generate and spread the constraints across both the taxon hierarchy and the GO graph.

Results: The taxon-based constraints produced by FunTaxIS-lite extend those provided by the Gene Ontology Consortium by an average of 300%. The implementation of these rules significantly reduces errors in function predictions made by automatic algorithms and can assist in correcting inconsistent protein annotations in databases.

Availability and implementation: FunTaxIS-lite is available on https://www.medcomp.medicina.unipd.it/funtaxis-lite and from https://github.com/MedCompUnipd/FunTaxIS-lite.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
期刊最新文献
MEHunter: Transformer-based mobile element variant detection from long reads Metabolic syndrome may be more frequent in treatment-naive sarcoidosis patients. Coracle—A Machine Learning Framework to Identify Bacteria Associated with Continuous Variables CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1