Abstract
The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations.
Highlights
One of the central research goals of systems biology is modelling various biological processes
A biological pathway is formed by a series of chemical reactions catalyzed by enzymes within a cell
Some of the most common biological pathways are those associated with metabolism, regulation of gene expression and transmission of molecular signals
Summary
One of the central research goals of systems biology is modelling various biological processes. A quite promising approach is to apply knowledge discovery and data mining techniques to predict some protein features based on a set of known data Such rule-based methods provide rich automatic functional annotations and aid in performing integrity checks. One of the very first pathway prediction systems was PathFinder [16] which aims to identify signaling pathways in protein-protein interaction networks It extracts the characteristics of known signal transduction pathways and their functional annotations in the form of association rules. The pathway prediction system utilizes data from UniProtKB/Swiss-Prot [8], which is a high quality manually annotated and non-redundant protein sequence database containing experimental results, computed features and scientific conclusions. A Java Archive (JAR) package for applying the prediction models on UniProtKB/TrEMBL prokaryotic entries is provided at http:// www.ebi.ac.uk/~rsaidi/arba/software
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