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- PublicationComputational selection of novel antigenic targets in the Mycobacterium bovis proteomeThe discovery of novel antigens is an essential requirement in devising new diagnostics for use in both M. tuberculosis (Mtb) and M. bovis control programmes. Reverse vaccinology is now a feasible method of extracting potential immunogenic epitopes from bacterial genomes to reduce the cost of experimental screening of antigens for anamnestic responses in infected hosts. Since a significant focus has been on the role of CD4+ T cells, the ability to predict peptide binding to MHC-II molecules is seen as a key step in discovery.Previous antigen-mining experiments for identification of novel diagnostic or vaccine candidates for human and bovine TB follow a targeted approach, where specific groups of proteins suspected to contain likely candidates are identified and evaluated for mmunogenicity. A disadvantage of those approaches is that they are restricted to a relatively small set of proteins biased by the initial selection criteria. Our objective was to computationally select antigens in a less biased manner.
- PublicationGOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression dataBackground: Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. Results: We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples. Conclusions: GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.
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