CATH-FunL: Improving Gene Target Selection by Predicting Functional Modules in Biological Systems

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In the past decades, a marked increase in data availability has revolutionized the study of biology. Advances in experimental techniques mean that we now have an abundance of information about the genes and proteins in our cells and their interactions. This unprecedented volume of data presents a challenge for biologists: how to best combine and exploit different data sources to gain meaningful biological insights.CATH-FunL is a tool designed to address this problem. FunL will allow users to predict novel proteins ('targets') likely to be associated with a set of proteins they are interested in - for example, known components in a protein signalling pathway. CATH-FunL will also allow users to gain further insight into these predicted targets by organizing and annotating this list of predicted genes. Finally, CATH-FunL will provide intuitive visualizations of the predicted targets and the functional relations between them.CATH-FunL's prediction methods are based on the well-documented concept of guilt-by-association. Much of the data produced by modern experimental techniques can be used to infer whether proteins participate in the same biological process - that is, whether they are functionally associated. Evidence for functional association comprises physical binding between proteins, correlation in expression patterns and numerous other, more indirect indicators. Guilty-by-association methods represent this information as a network of functional associations between proteins and attempts to use the structure of the network to predict new associations.The simplest methods simply make predictions based on the direct network neighbours of a protein. This, however, ignores the rich information present in the overall topology of the network: for example, groups of proteins relating to the same function are known to form densely connected clusters within the network, with fewer connections to other proteins. FunL aims to exploit this type of structure using a powerful and well-studied approach known as graph kernels.CATH-FunL will integrate a large volume of protein interaction/association information, from several public repositories and our own in-house tools for protein association prediction. These data will be represented as networks, combined and then transformed into a ranked list of potential targets using kernel-based methods, based on a set of query and known proteins provided by the user. Query proteins will be ranked by the strength of their association to known proteins.FunL will provide further insight into the target proteins by providing information about their function. Functional annotation is often performed using terms from the Gene Ontology (GO). However, on average, <10% of genes in an organism have been experimentally characterised - GO annotations can therefore be sparse or unreliable for many proteins. Therefore, we will supplement experimental GO annotations with predicted annotations using state-of-the-art, in-house, sequence based prediction methods. Once the target list has been computed, CATH-FunL will organise the list into functionally coherent sub-groups. This will allow users to detect potential patterns in the predicted targets and to focus on particular biological processes of interest to them. Because much of the computational work involved in this clustering will already be done by FunL at the query stage, this provides a very efficient way of classifying the target list proteins.Finally, FunL will visualise the results in an intuitive way. We will use both network based visualisations and explore more innovative approaches related to the kernel-based methods.In summary, CATH-FunL will allow users to combine their own datasets of experimentally analysed genes with information from heterogeneous publicly available repositories and our in-house functional annotation datasets to gain valuable functional insights into biological processes they are interested in.