The Proteomics Standards Initiative has recently released the mzIdentML data standard for representing peptide and protein identification results, for example, created by a search engine. When a new standard format is produced, it is important that software tools are available that make it straightforward for laboratory scientists to use it routinely and for bioinformaticians to embed support in their own tools. Here we report the release of several open-source Java-based software packages based on mzIdentML: ProteoIDViewer, mzidLibrary, and mzidValidator. The ProteoIDViewer is a desktop application allowing users to visualize mzIdentML-formatted results originating from any appropriate identification software; it supports visualization of all the features of the mzIdentML format. The mzidLibrary is a software library containing routines for importing data from external search engines, post-processing identification data (such as false discovery rate calculations), combining results from multiple search engines, performing protein inference, setting identification thresholds, and exporting results from mzIdentML to plain text files. The mzidValidator is able to process files and report warnings or errors if files are not correctly formatted or contain some semantic error. We anticipate that these developments will simplify adoption of the new standard in proteomics laboratories and the integration of mzIdentML into other software tools. All three tools are freely available in the public domain.

Currently, the diagnosis of malignant pheochromocytoma can only be made when there is clinical evidence of metastasis or extensive local invasion. Thus, there is a need for new diagnostic marker(s) to identify tumors with malignant potential. The purpose of this study was to identify microRNAs (miRNAs) that are differentially expressed between benign and malignant pheochromocytomas and assess their diagnostic accuracy. Toward this aim, we analyzed miRNA expression in benign and malignant pheochromocytoma tumor samples using whole genome microarray profiling. Microarray analysis identified eight miRNAs that were significantly differentially expressed between benign and malignant pheochromocytomas. We measured a subset of these miRNAs directly by RT-PCR and found that miR-483-5p, miR-183, and miR-101 had significantly higher expression in malignant tumors as compared to their benign counterparts. Area under the receiver operating curve (AUC) analysis indicated that miR-483-5p, miR-101, and miR-183 could be useful diagnostic markers for distinguishing malignant from benign pheochromocytomas. In addition, these miRNAs could be detected in pheochromocytoma patient serum. Overall our data suggest that misexpression of miR-483-5p, miR-101, and miR-183 is associated with malignant pheochromocytoma.

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).

Summary Climate change is a global threat to species, and their capacity to adapt could be limited by habitat fragmentation. Many initiatives to restore habitats, increase connectivity and/or ensure ‘functioning ecological networks’ are explicitly or implicitly trying to address this threat. However, existing methods of analysing networks mainly treat the landscape as static, and it is difficult to use these to plan restoration. We use a recent method to approximate the speed of a species’ range expansion through a landscape by an analogy to an electrical circuit, which takes into account both the rates of colonisation between patches and the rates at which occupied habitat produces new emigrants. Based on this, we propose and test two methods that can help to optimise the spatial arrangement of habitat for range expansion. First, high current flowing through a habitat patch indicates that it should be a priority for conservation, and this can be the basis of an algorithm for iteratively dropping the least valuable patches. Secondly, high power in a link between two patches indicates that it is a bottleneck in the circuit, and this can be the basis of an algorithm for iteratively adding new patches in the most efficient places. We show that these methods perform well for a variety of realistic landscape patterns, assuming known and fixed dispersal ability and source/target locations. The calculations involved for each parameter set are fast enough to be used as building blocks in a larger optimisation for practical planning of landscapes for multiple species. Thus, we lay the foundation for a new genre of systematic conservation planning, which efficiently proposes restoration as well as minimising loss.

Despite 40 years of control efforts, onchocerciasis (river blindness) remains one of the most important neglected tropical diseases, with 17 million people affected. The etiological agent, Onchocerca volvulus, is a filarial nematode with a complex lifecycle involving several distinct stages in the definitive host and blackfly vector. The challenges of obtaining sufficient material have prevented high-throughput studies and the development of novel strategies for disease control and diagnosis. Here, we utilize the closest relative of O. volvulus, the bovine parasite Onchocerca ochengi, to compare stage-specific proteomes and host-parasite interactions within the secretome. We identified a total of 4260 unique O. ochengi proteins from adult males and females, infective larvae, intrauterine microfilariae, and fluid from intradermal nodules. In addition, 135 proteins were detected from the obligate Wolbachia symbiont. Observed protein families that were enriched in all whole body extracts relative to the complete search database included immunoglobulin-domain proteins, whereas redox and detoxification enzymes and proteins involved in intracellular transport displayed stage-specific overrepresentation. Unexpectedly, the larval stages exhibited enrichment for several mitochondrial-related protein families, including members of peptidase family M16 and proteins which mediate mitochondrial fission and fusion. Quantification of proteins across the lifecycle using the Hi-3 approach supported these qualitative analyses. In nodule fluid, we identified 94 O. ochengi secreted proteins, including homologs of transforming growth factor-β and a second member of a novel 6-ShK toxin domain family, which was originally described from a model filarial nematode (Litomosoides sigmodontis). Strikingly, the 498 bovine proteins identified in nodule fluid were strongly dominated by antimicrobial proteins, especially cathelicidins. This first high-throughput analysis of an Onchocerca spp. proteome across the lifecycle highlights its profound complexity and emphasizes the extremely close relationship between O. ochengi and O. volvulus. The insights presented here provide new candidates for vaccine development, drug targeting and diagnostic biomarkers.