Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms.
Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms.
复制标题
DOI：
10.1186/1471-2105-13-s4-s14
复制
发表时间：
2012-03-28
影响因子：
3
通讯作者：
Fontana P
中科院分区：
文献类型：
--
作者：
Falda M;Toppo S;Pescarolo A;Lavezzo E;Di Camillo B;Facchinetti A;Cilia E;Velasco R;Fontana P
在下一代测序技术时代，预测蛋白质功能的要求越来越高。为每个序列分配一个策展人审查功能的任务是不切实际的。生物信息学工具，易于使用，能够提供自动和可靠的注释在基因组规模，是必要的和紧迫的。在这种情况下，基因本体提供了一种方法来标准化的注释分类与结构化的词汇表，可以很容易地利用计算方法。Argot2是一个基于网络的功能预测工具，能够注释从小数据集到整个基因组的核酸或蛋白质序列。它接受FASTA格式的序列列表作为输入，分别使用BLAST和HMMER检索与UniProKB和Pfam数据库进行处理;然后用从UniProtKB-GOA数据库检索的GO术语注释这些序列，并使用来自BLAST和HMMER的e值对术语进行加权。加权的GO术语根据基因本体描述的它们的语义相似性关系及其相关得分进行处理。该算法是基于在以前的工具称为隐语开发的原始想法。整个引擎已经完全重写，以提高准确性和计算效率，从而允许完整基因组的注释。改进后的算法已经在葡萄和苹果的内部基因组项目中得到了成功的应用和测试，并且在我们所有的基准条件下都被证明具有很高的精确度和召回率。它也被成功地与Blast2GO进行了比较，Blast2GO是最常用于序列注释的方法之一。该服务器可在www.example.com上免费访问。
Predicting protein function has become increasingly demanding in the era of next generation sequencing technology. The task to assign a curator-reviewed function to every single sequence is impracticable. Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent. In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods. Argot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes. It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER. The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score. The algorithm is based on the original idea developed in a previous tool called Argot. The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes. The revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions. It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation. The server is freely accessible at http://www.medcomp.medicina.unipd.it/Argot2.