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Computational Methods for Annotation Transfers from Sequence.

Domenico Cozzetto¹, David T Jones²

¹Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK.

Methods in Molecular Biology (Clifton, N.J.)

|November 5, 2016

Summary

This summary is machine-generated.

Many proteins lack functional information, prompting the use of bioinformatics to predict protein function. This work reviews sequence-based methods for assigning Gene Ontology (GO) terms, highlighting their strengths and weaknesses.

Keywords:

De novo function prediction Homology-based annotation transfers Multi-domain architecture Phylogenomics Protein function prediction

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Area of Science:

Bioinformatics
Proteomics
Computational Biology

Background:

Experimental functional information is missing for many known proteins.
Bioinformatics methods utilize diverse data to predict protein function.
Different data types offer complementary insights into biological roles.

Purpose of the Study:

To review sequence-based bioinformatics methods for protein function prediction.
To focus on methods that directly output Gene Ontology (GO) term assignments.
To present biological and computational concepts, advantages, and limitations of these approaches.

Main Methods:

Analysis of bioinformatics methods accepting amino acid sequences as input.
Evaluation of methods producing Gene Ontology (GO) term assignments directly.
Review of computational and biological concepts relevant to protein function prediction.

Main Results:

Identification of sequence-based methods for GO term assignment.
Discussion of the complementary nature of different data types in function prediction.
Assessment of the advantages and limitations of various bioinformatics approaches.

Conclusions:

Sequence-based methods are crucial for annotating protein function.
Understanding the strengths and limitations of these methods is essential for accurate GO term assignment.
Further development of bioinformatics tools is needed to address the gap in protein functional information.