FunTaxIS-lite: a simple and light solution to investigate protein functions in all living organisms.

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

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Abstract

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.

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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。

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。

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来源期刊

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.