Andrew R. Jones

ProteomeXchange provides globally coordinated proteomics data submission and dissemination

5. Tools available and ways to submit data to PX ............................................................. 11 5.1. MS/MS data submissions to PRIDE .................................................................................... 11 5.1.1. Creation of supported files for “Complete” submissions .................................................. 11 5.1.1.1. PRIDE XML .................................................................................................................................. 11 5.1.1.2. mzIdentML ................................................................................................................................. 13 5.1.2. Checking the files before submission (initial quality assessment) ..................................... 14 5.1.3. File submission to PRIDE: the PX submission tool ............................................................. 15 5.1.3.1. General Information ................................................................................................................... 15 5.1.3.2. Functionality, Design and Implementation Details .................................................................... 15 5.1.3.3. New open source libraries made available with PX submission tool ......................................... 18 5.1.3.4. PX Submission Tool Java Web Start ............................................................................................ 18 5.1.4. File submission to PRIDE: Command line support using Aspera ........................................ 19 5.1.5. Examples of Partial submissions to PRIDE ......................................................................... 19 5.2. SRM data submissions via PASSEL ..................................................................................... 20

Allele frequency net: a database and online repository for immune gene frequencies in worldwide populations

The allele frequency net database (http://www.allelefrequencies.net) is an online repository that contains information on the frequencies of immune genes and their corresponding alleles in different populations. The extensive variability observed in genes and alleles related to the immune system response and its significance in transplantation, disease association studies and diversity in populations led to the development of this electronic resource. At present, the system contains data from 1133 populations in 608 813 individuals on the frequency of genes from different polymorphic regions such as human leukocyte antigens, killer-cell immunoglobulin-like receptors, major histocompatibility complex Class I chain-related genes and a number of cytokine gene polymorphisms. The project was designed to create a central source for the storage of frequency data and provide individuals with a set of bioinformatics tools to analyze the occurrence of these variants in worldwide populations. The resource has been used in a wide variety of contexts, including clinical applications (histocompatibility, immunology, epidemiology and pharmacogenetics) and population genetics. Demographic information, frequency data and searching tools can be freely accessed through the website.

Allele frequency net 2015 update: new features for HLA epitopes, KIR and disease and HLA adverse drug reaction associations

It has been 12 years since the Allele Frequency Net Database (AFND; http://www.allelefrequencies.net) was first launched, providing the scientific community with an online repository for the storage of immune gene frequencies in different populations across the world. There have been a significant number of improvements from the first version, making AFND a primary resource for many clinical and scientific areas including histocompatibility, immunogenetics, pharmacogenetics and anthropology studies, among many others. The most widely used part of AFND stores population frequency data (alleles, genes or haplotypes) related to human leukocyte antigens (HLA), killer-cell immunoglobulin-like receptors (KIR), major histocompatibility complex class I chain-related genes (MIC) and a number of cytokine gene polymorphisms. AFND now contains >1400 populations from more than 10 million healthy individuals. Here, we report how the main features of AFND have been updated to include a new section on ‘HLA epitope’ frequencies in populations, a new section capturing the results of studies identifying HLA associations with adverse drug reactions (ADRs) and one for the examination of infectious and autoimmune diseases associated with KIR polymorphisms—thus extending AFND to serve a new user base in these growing areas of research. New criteria on data quality have also been included.

The mzIdentML Data Standard for Mass Spectrometry-Based Proteomics Results

We report the release of mzIdentML, an exchange standard for peptide and protein identification data, designed by the Proteomics Standards Initiative. The format was developed by the Proteomics Standards Initiative in collaboration with instrument and software vendors, and the developers of the major open-source projects in proteomics. Software implementations have been developed to enable conversion from most popular proprietary and open-source formats, and mzIdentML will soon be supported by the major public repositories. These developments enable proteomics scientists to start working with the standard for exchanging and publishing data sets in support of publications and they provide a stable platform for bioinformatics groups and commercial software vendors to work with a single file format for identification data.

The proteome of Toxoplasma gondii: integration with the genome provides novel insights into gene expression and annotation

BackgroundAlthough the genomes of many of the most important human and animal pathogens have now been sequenced, our understanding of the actual proteins expressed by these genomes and how well they predict protein sequence and expression is still deficient. We have used three complementary approaches (two-dimensional electrophoresis, gel-liquid chromatography linked tandem mass spectrometry and MudPIT) to analyze the proteome of Toxoplasma gondii, a parasite of medical and veterinary significance, and have developed a public repository for these data within ToxoDB, making for the first time proteomics data an integral part of this key genome resource.ResultsThe draft genome for Toxoplasma predicts around 8,000 genes with varying degrees of confidence. Our data demonstrate how proteomics can inform these predictions and help discover new genes. We have identified nearly one-third (2,252) of all the predicted proteins, with 2,477 intron-spanning peptides providing supporting evidence for correct splice site annotation. Functional predictions for each protein and key pathways were determined from the proteome. Importantly, we show evidence for many proteins that match alternative gene models, or previously unpredicted genes. For example, approximately 15% of peptides matched more convincingly to alternative gene models. We also compared our data with existing transcriptional data in which we highlight apparent discrepancies between gene transcription and protein expression.ConclusionOur data demonstrate the importance of protein data in expression profiling experiments and highlight the necessity of integrating proteomic with genomic data so that iterative refinements of both annotation and expression models are possible.

The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics

The Functional Genomics Experiment data model (FuGE) has been developed to facilitate convergence of data standards for high-throughput, comprehensive analyses in biology. FuGE models the components of an experimental activity that are common across different technologies, including protocols, samples and data. FuGE provides a foundation for describing entire laboratory workflows and for the development of new data formats. The Microarray Gene Expression Data society and the Proteomics Standards Initiative have committed to using FuGE as the basis for defining their respective standards, and other standards groups, including the Metabolomics Standards Initiative, are evaluating FuGE in their development efforts. Adoption of FuGE by multiple standards bodies will enable uniform reporting of common parts of functional genomics workflows, simplify data-integration efforts and ease the burden on researchers seeking to fulfill multiple minimum reporting requirements. Such advances are important for transparent data management and mining in functional genomics and systems biology.

Modulation of the Host Cell Proteome by the Intracellular Apicomplexan Parasite Toxoplasma gondii

ABSTRACT To investigate how intracellular parasites manipulate their host cell environment at the molecular level, we undertook a quantitative proteomic study of cells following infection with the apicomplexan parasite Toxoplasma gondii. Using conventional two-dimensional electrophoresis, difference gel electrophoresis (DIGE), and mass spectrometry, we identified host proteins that were consistently modulated in expression following infection. We detected modification of protein expression in key metabolic pathways, including glycolysis, lipid and sterol metabolism, mitosis, apoptosis, and structural-protein expression, suggestive of global reprogramming of cell metabolism by the parasite. Many of the differentially expressed proteins had not been previously implicated in the response to the parasite, while others provide important corroborative protein evidence for previously proposed hypotheses of pathogen-cell interactions. Significantly, over one-third of all modulated proteins were mitochondrial, and this was further investigated by DIGE analysis of a mitochondrion-enriched preparation from infected cells. Comparison of our proteomic data with previous transcriptional studies suggested that a complex relationship exits between transcription and protein expression that may be partly explained by posttranslational modifications of proteins and revealed the importance of investigating protein changes when interpreting transcriptional data. To investigate this further, we used phosphatase treatment and DIGE to demonstrate changes in the phosphorylation states of several key proteins following infection. Overall, our findings indicate that the host cell proteome responds in a dramatic way to T. gondii invasion, in terms of both protein expression changes and protein modifications, and reveal a complex and intimate molecular relationship between host and parasite.

Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines

LC‐MS experiments can generate large quantities of data, for which a variety of database search engines are available to make peptide and protein identifications. Decoy databases are becoming widely used to place statistical confidence in result sets, allowing the false discovery rate (FDR) to be estimated. Different search engines produce different identification sets so employing more than one search engine could result in an increased number of peptides (and proteins) being identified, if an appropriate mechanism for combining data can be defined. We have developed a search engine independent score, based on FDR, which allows peptide identifications from different search engines to be combined, called the FDR Score. The results demonstrate that the observed FDR is significantly different when analysing the set of identifications made by all three search engines, by each pair of search engines or by a single search engine. Our algorithm assigns identifications to groups according to the set of search engines that have made the identification, and re‐assigns the score (combined FDR Score). The combined FDR Score can differentiate between correct and incorrect peptide identifications with high accuracy, allowing on average 35% more peptide identifications to be made at a fixed FDR than using a single search engine.

The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience

The HUPO Proteomics Standards Initiative has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS)-based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools such as Microsoft Excel or R. We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. mzTab is intended as a lightweight supplement to the existing standard XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. mzTab files can contain protein, peptide, and small molecule identifications together with experimental metadata and basic quantitative information. The format is not intended to store the complete experimental evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the experimental design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biological community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive additional documentation can be found online.

The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics

The range of heterogeneous approaches available for quantifying protein abundance via mass spectrometry (MS)1 leads to considerable challenges in modeling, archiving, exchanging, or submitting experimental data sets as supplemental material to journals. To date, there has been no widely accepted format for capturing the evidence trail of how quantitative analysis has been performed by software, for transferring data between software packages, or for submitting to public databases. In the context of the Proteomics Standards Initiative, we have developed the mzQuantML data standard. The standard can represent quantitative data about regions in two-dimensional retention time versus mass/charge space (called features), peptides, and proteins and protein groups (where there is ambiguity regarding peptide-to-protein inference), and it offers limited support for small molecule (metabolomic) data. The format has structures for representing replicate MS runs, grouping of replicates (for example, as study variables), and capturing the parameters used by software packages to arrive at these values. The format has the capability to reference other standards such as mzML and mzIdentML, and thus the evidence trail for the MS workflow as a whole can now be described. Several software implementations are available, and we encourage other bioinformatics groups to use mzQuantML as an input, internal, or output format for quantitative software and for structuring local repositories. All project resources are available in the public domain from the HUPO Proteomics Standards Initiative http://www.psidev.info/mzquantml.