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Proteomics data can supplement genome annotation efforts, for example being used to confirm gene models or correct gene annotation errors. Here, we present a large-scale proteogenomics study of two important apicomplexan pathogens: Toxoplasma gondii and Neospora caninum. We queried proteomics data against a panel of official and alternate gene models generated directly from RNASeq data, using several newly generated and some previously published MS datasets for this meta-analysis. We identified a total of 201 996 and 39 953 peptide-spectrum matches for T. gondii and N. caninum, respectively, at a 1% peptide FDR threshold. This equated to the identification of 30 494 distinct peptide sequences and 2921 proteins (matches to official gene models) for T. gondii, and 8911 peptides/1273 proteins for N. caninum following stringent protein-level thresholding. We have also identified 289 and 140 loci for T. gondii and N. caninum, respectively, which mapped to RNA-Seq-derived gene models used in our analysis and apparently absent from the official annotation (release 10 from EuPathDB) of these species. We present several examples in our study where the RNA-Seq evidence can help in correction of the current gene model and can help in discovery of potential new genes. The findings of this study have been integrated into the EuPathDB. The data have been deposited to the ProteomeXchange with identifiers PXD000297and PXD000298.

The recent massive increase in capability for sequencing genomes is producing enormous advances in our understanding of biological systems. However, there is a bottleneck in genome annotation--determining the structure of all transcribed genes. Experimental data from MS studies can play a major role in confirming and correcting gene structure--proteogenomics. However, there are some technical and practical challenges to overcome, since proteogenomics requires pipelines comprising a complex set of interconnected modules as well as bespoke routines, for example in protein inference and statistics. We are introducing a complete, open source pipeline for proteogenomics, called ProteoAnnotator, which incorporates a graphical user interface and implements the Proteomics Standards Initiative mzIdentML standard for each analysis stage. All steps are included as standalone modules with the mzIdentML library, allowing other groups to re-use the whole pipeline or constituent parts within other tools. We have developed new modules for pre-processing and combining multiple search databases, for performing peptide-level statistics on mzIdentML files, for scoring grouped protein identifications matched to a given genomic locus to validate that updates to the official gene models are statistically sound and for mapping end results back onto the genome. ProteoAnnotator is available from http://www.proteoannotator.org/. All MS data have been deposited in the ProteomeXchange with identifiers PXD001042 and PXD001390 (http://proteomecentral.proteomexchange.org/dataset/PXD001042; http://proteomecentral.proteomexchange.org/dataset/PXD001390).