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Decoy database searches are used to filter out false positive protein identifications derived from search engines, but there is no consensus about which decoy is "the best". We evaluate nine different decoy designs using public data sets from samples of known composition. Statistically significant performance differences were found, but no single decoy stood out among the best performers. Ultimately, we recommend peptide level reverse decoys searched independently from the target.

This article provides an overview of publicly available proteomic data repositories in a single document with a particular focus on the latest developments, many of which are not announced through traditional publications. The review is intended to inform the proteomics practitioner of the options for storage and dissemination of their MS/MS data in the public domain, and to help those who want to mine proteomic data generated by others. The latter area has arguably seen the most development in recent times, as repositories have sprouted new tools for data analysis, visualisation and experimental design. We also highlight key biological datasets available at each repository, including standard datasets. Finally, we touch upon areas of significant challenge and future directions.

Recognition of virus infection by innate pattern recognition receptors (PRRs), including membrane-associated toll-like receptors (TLR) and cytoplasmic RIG-I-like receptors (RLR), activates cascades of signal transduction pathways leading to production of type I interferons (IFN) and proinflammatory cytokines that orchestrate the elimination of the viruses. Although it has been demonstrated that PRR-mediated innate immunity plays an essential role in defending virus from infection, it also occasionally results in overwhelming production of proinflammatory cytokines that cause severe inflammation, blood vessel leakage and tissue damage. In our efforts to identify small molecules that selectively enhance PRR-mediated antiviral, but not the detrimental inflammatory response, we discovered a compound, RO 90–7501 (‘2’-(4-Aminophenyl)-[2,5′-bi-1H-benzimidazol]-5-amine), that significantly promoted both TLR3 and RLR ligand-induced IFN-β gene expression and antiviral response, most likely via selective activation of p38 mitogen-activated protein kinase (MAPK) pathway. Our results thus imply that pharmacological modulation of PRR signal transduction pathways in favor of the induction of a beneficial antiviral response can be a novel therapeutic strategy.

Microarray technology for mammalian cells has been utilized mainly for humans, mouse, and rat gene expression analysis. In this approach the feasibility of cross-species hybridization experiments using Chinese hamster ovary (CHO) cells was evaluated. Sequence alignments of available data for CHO were performed against mouse and rat transcripts to determine the homology between the investigated species. We implemented a probability model based on this homology in order to estimate the chance for successful hybridization using Agilent's 60-mer oligonucleotide platform. Heat-shock expression data from CHO, mouse 3T3, and rat A10 cells were generated to determine intraspecies variability, reproducibility, and specificity in order to assess the accuracy of this method. Detected signature genes, in particular from studies with the mouse arrays, showed a reliable similarity between these two rodents and were confirmed by quantitative RT-PCR. Our findings provide evidence that cross-species analysis can be a useful tool to study gene expression profiles of related organisms for which species-specific microarrays are not available.

As proteomic MS has increased in throughput, so has the demand to catalogue the increasing number of peptides and proteins observed by the community using this technique. As in other 'omics' fields, this brings obvious scientific benefits such as sharing of results and prevention of unnecessary repetition, but also provides technical insights, such as the ability to compare proteome coverage between different laboratories, or between different proteomic platforms. Journals are also moving towards mandating that proteomics data be submitted to public repositories upon publication. In response to these demands, several web-based repositories have been established to store protein and peptide identifications derived from MS data, and a similar number of peptide identification software pipelines have emerged to deliver identifications to these repositories. This paper reviews the latest developments in public domain peptide and protein identification databases and describes the analysis pipelines that feed them. Recent applications of the tools to pertinent biological problems are examined, and through comparing and contrasting the capabilities of each system, the issues facing research users of web-based repositories are explored. Future developments and mechanisms to enhance system functionality and user-interfacing opportunities are also suggested.

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