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Methods Mol Biol. 2017;1611:1-14. doi: 10.1007/978-1-4939-7015-5_1.

Using PFP and ESG Protein Function Prediction Web Servers.

Author information

1
Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA.
2
Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
3
Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA. dkihara@purdue.edu.
4
Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA. dkihara@purdue.edu.

Abstract

Elucidating biological function of proteins is a fundamental problem in molecular biology and bioinformatics. Conventionally, protein function is annotated based on homology using sequence similarity search tools such as BLAST and FASTA. These methods perform well when obvious homologs exist for a query sequence; however, they will not provide any functional information otherwise. As a result, the functions of many genes in newly sequenced genomes are left unknown, which await functional interpretation. Here, we introduce two webservers for function prediction methods, which effectively use distantly related sequences to improve function annotation coverage and accuracy: Protein Function Prediction (PFP) and Extended Similarity Group (ESG). These two methods have been tested extensively in various benchmark studies and ranked among the top in community-based assessments for computational function annotation, including Critical Assessment of Function Annotation (CAFA) in 2010-2011 (CAFA1) and 2013-2014 (CAFA2). Both servers are equipped with user-friendly visualizations of predicted GO terms, which provide intuitive illustrations of relationships of predicted GO terms. In addition to PFP and ESG, we also introduce NaviGO, a server for the interactive analysis of GO annotations of proteins. All the servers are available at http://kiharalab.org/software.php .

KEYWORDS:

Automated function prediction; BLAST; Gene Ontology; Genome annotation; Protein function prediction; Sequence analysis

PMID:
28451967
DOI:
10.1007/978-1-4939-7015-5_1
[Indexed for MEDLINE]

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