Abstract
BackgroundHigh-throughput bio-techniques accumulate ever-increasing amount of genomic and proteomic data. These data are far from being functionally characterized, despite the advances in gene (or gene’s product proteins) functional annotations. Due to experimental techniques and to the research bias in biology, the regularly updated functional annotation databases, i.e., the Gene Ontology (GO), are far from being complete. Given the importance of protein functions for biological studies and drug design, proteins should be more comprehensively and precisely annotated.ResultsWe proposed downward Random Walks (dRW) to predict missing (or new) functions of partially annotated proteins. Particularly, we apply downward random walks with restart on the GO directed acyclic graph, along with the available functions of a protein, to estimate the probability of missing functions. To further boost the prediction accuracy, we extend dRW to dRW-kNN. dRW-kNN computes the semantic similarity between proteins based on the functional annotations of proteins; it then predicts functions based on the functions estimated by dRW, together with the functions associated with the k nearest proteins. Our proposed models can predict two kinds of missing functions: (i) the ones that are missing for a protein but associated with other proteins of interest; (ii) the ones that are not available for any protein of interest, but exist in the GO hierarchy. Experimental results on the proteins of Yeast and Human show that dRW and dRW-kNN can replenish functions more accurately than other related approaches, especially for sparse functions associated with no more than 10 proteins.ConclusionThe empirical study shows that the semantic similarity between GO terms and the ontology hierarchy play important roles in predicting protein function. The proposed dRW and dRW-kNN can serve as tools for replenishing functions of partially annotated proteins.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0713-y) contains supplementary material, which is available to authorized users.
Highlights
High-throughput bio-techniques accumulate ever-increasing amount of genomic and proteomic data
The Gene Ontology Annotation (GOA) files of Yeast and Human were obtained from the European Bioinformatics Institute2
An interesting observation is that the average number of terms associated with a protein is close to the standard deviation; this is because some proteins in the GOA are not annotated with any term
Summary
High-throughput bio-techniques accumulate ever-increasing amount of genomic and proteomic data. These data are far from being functionally characterized, despite the advances in gene (or gene’s product proteins) functional annotations. Due to experimental techniques and to the research bias in biology, the regularly updated functional annotation databases, i.e., the Gene Ontology (GO), are far from being complete. The Gene Ontology (GO) is a controlled vocabulary of terms for describing the biological roles of genes and their products (i.e., proteins) [1]. The advance in protein functional annotation far lags behind the pace of accumulated proteomic and genomic data. Schones et al [4] found that the functional annotations of high-throughput genomic and proteomic data are biased and shallow. Automatically annotating the functional roles of these proteins using GO
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