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Data-Independent Acquisition (DIA) is a method to improve consistent identification and precise quantitation of peptides and proteins by mass spectrometry (MS). The targeted data analysis strategy in DIA relies on spectral assay libraries that are generally derived from a priori measurements of peptides for each species. Although Escherichia coli (E. coli) is among the best studied model organisms, so far there is no spectral assay library for the bacterium publicly available. Here, we generated a spectral assay library for 4,014 of the 4,389 annotated E. coli proteins using one- and two-dimensional fractionated samples, and ion mobility separation enabling deep proteome coverage. We demonstrate the utility of this high-quality library with robustness in quantitation of the E. coli proteome and with rapid-chromatography to enhance throughput by targeted DIA-MS. The spectral assay library supports the detection and quantification of 91.5% of all E. coli proteins at high-confidence with 56,182 proteotypic peptides, making it a valuable resource for the scientific community. Data and spectral libraries are available via ProteomeXchange (PXD020761, PXD020785) and SWATHAtlas (SAL00222-28)., Measurement(s) Proteome • database type spectral library Technology Type(s) SWATH MS protein profiling assay • mass spectrometry • Data-Independent Acquisition Sample Characteristic - Organism Escherichia coli Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13078733

Data-independent acquisition (DIA) mass spectrometry, also known as Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH), is a popular label-free proteomics strategy to comprehensively quantify peptides/proteins utilizing mass spectral libraries to decipher inherently multiplexed spectra collected linearly across a mass range. Although there are many spectral libraries produced worldwide, the quality control of these libraries is lacking. We present the DIALib-QC (DIA library quality control) software tool for the systematic evaluation of a library’s characteristics, completeness and correctness across 62 parameters of compliance, and further provide the option to improve its quality. We demonstrate its utility in assessing and repairing spectral libraries for correctness, accuracy and sensitivity., Most data-independent acquisition (DIA) methods depend on mass spectral libraries for peptide identification but tools to assess library quality are lacking. Here, the authors develop DIALib- QC for the systematic evaluation and correction of spectral libraries.

Human blood plasma provides a highly accessible window to the proteome of any individual in health and disease. Since its inception in 2002, the Human Proteome Organization's Human Plasma Proteome Project (HPPP) has been promoting advances in the study and understanding of the full protein complement of human plasma and on determining the abundance and modifications of its components. In 2017, we review the history of the HPPP and the advances of human plasma proteomics in general, including several recent achievements. We then present the latest 2017-04 build of Human Plasma PeptideAtlas, which yields ∼43 million peptide-spectrum matches and 122,730 distinct peptide sequences from 178 individual experiments at a 1% protein-level FDR globally across all experiments. Applying the latest Human Proteome Project Data Interpretation Guidelines, we catalog 3509 proteins that have at least two non-nested uniquely mapping peptides of nine amino acids or more and1300 additional proteins with ambiguous evidence. We apply the same two-peptide guideline to historical PeptideAtlas builds going back to 2006 and examine the progress made in the past ten years in plasma proteome coverage. We also compare the distribution of proteins in historical PeptideAtlas builds in various RNA abundance and cellular localization categories. We then discuss advances in plasma proteomics based on targeted mass spectrometry as well as affinity assays, which during early 2017 target ∼2000 proteins. Finally, we describe considerations about sample handling and study design, concluding with an outlook for future advances in deciphering the human plasma proteome.

The ProteomeXchange (PX) Consortium of proteomics resources (http://www.proteomexchange.org) was formally started in 2011 to standardize data submission and dissemination of mass spectrometry proteomics data worldwide. We give an overview of the current consortium activities and describe the advances of the past few years. Augmenting the PX founding members (PRIDE and PeptideAtlas, including the PASSEL resource), two new members have joined the consortium: MassIVE and jPOST. ProteomeCentral remains as the common data access portal, providing the ability to search for data sets in all participating PX resources, now with enhanced data visualization components. We describe the updated submission guidelines, now expanded to include four members instead of two. As demonstrated by data submission statistics, PX is supporting a change in culture of the proteomics field: public data sharing is now an accepted standard, supported by requirements for journal submissions resulting in public data release becoming the norm. More than 4500 data sets have been submitted to the various PX resources since 2012. Human is the most represented species with approximately half of the data sets, followed by some of the main model organisms and a growing list of more than 900 diverse species. Data reprocessing activities are becoming more prominent, with both MassIVE and PeptideAtlas releasing the results of reprocessed data sets. Finally, we outline the upcoming advances for ProteomeXchange.

The ability to reliably and reproducibly measure any protein of the human proteome in any tissue or cell type would be transformative for understanding systems-level properties as well as specific pathways in physiology and disease. Here, we describe the generation and verification of a compendium of highly specific assays that enable quantification of 99.7% of the 20,277 annotated human proteins by the widely accessible, sensitive, and robust targeted mass spectrometric method selected reaction monitoring, SRM. This human SRMAtlas provides definitive coordinates that conclusively identify the respective peptide in biological samples. We report data on 166,174 proteotypic peptides providing multiple, independent assays to quantify any human protein and numerous spliced variants, non-synonymous mutations, and post-translational modifications. The data are freely accessible as a resource at http://www.srmatlas.org/, and we demonstrate its utility by examining the network response to inhibition of cholesterol synthesis in liver cells and to docetaxel in prostate cancer lines.

Bottom-up proteomics relies on the proteolytic or chemical cleavage of proteins into peptides, the identification of those peptides via mass spectrometry, and the mapping of the identified peptides back to the reference proteome to infer which possible proteins are identified. Reliable mapping of peptides to proteins still poses substantial challenges when considering similar proteins, protein families, splice isoforms, sequence variation, and possible residue mass modifications, combined with an imperfect and incomplete understanding of the proteome. The ProteoMapper tool enables a comprehensive and rapid mapping of peptides to a reference proteome. The indexer component creates a segmented index for an input proteome from a FASTA or PEFF file. The ProMaST component provides ultrafast mapping of one or more input peptides against the index. ProteoMapper allows searches that take into account known sequence variation encoded in PEFF files. It also enables fuzzy searches to find highly similar peptides with residue order changes or other isobaric or near-isobaric substitutions within a specified mass tolerance. We demonstrate an example of a one-hit-wonder identification in PeptideAtlas that may be better explained by a combination of catalogued and uncatalogued sequence variation in another highly observed protein. ProteoMapper is a free and open source, available for local use after downloading, embedding in other applications, as an online web tool at http://www.peptideatlas.org/map , and as a web service.

The large array of peptides presented to CD8+ T cells by major histocompatibility complex (MHC) class I molecules is referred to as the MHC class I immunopeptidome. Although the MHC class I immunopeptidome is ubiquitous in mammals and represents a critical component of the immune system, very little is known, in any species, about its composition across most tissues and organs in vivo. We applied mass spectrometry (MS) technologies to draft the first tissue-based atlas of the murine MHC class I immunopeptidome in health. Peptides were extracted from 19 normal tissues from C57BL/6 mice and prepared for MS injections, resulting in a total number of 28,448 high-confidence H2Db/Kb-associated peptides identified and annotated in the atlas. This atlas provides initial qualitative data to explore the tissue-specificity of the immunopeptidome and serves as a guide to identify potential tumor-associated antigens from various cancer models. Our data were shared via PRIDE (PXD008733), SysteMHC Atlas (SYSMHC00018) and SWATH Atlas. We anticipate that this unique dataset will be expanded in the future and will find wide applications in basic and translational immunology.

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected “ signal” in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

The Human PeptideAtlas is a compendium of the highest quality peptide identifications from over 1000 shotgun mass spectrometry proteomics experiments collected from many different labs, all reanalyzed through a uniform processing pipeline. The latest 2015-03 build contains substantially more input data than past releases, is mapped to a recent version of our merged reference proteome, and uses improved informatics processing and the development of the AtlasProphet to provide the highest quality results. Within the set of ~20,000 neXtProt primary entries, 14,070 (70%) are confidently detected in the latest build, 5% are ambiguous, 9% are redundant, leaving the total percentage of proteins for which there are no mapping detections at just 16% (3166), all derived from over 133 million peptide-spectrum matches identifying more than 1 million distinct peptides using AtlasProphet to characterize and classify the protein matches. Improved handling for detection and presentation of single amino-acid variants (SAAVs) reveals the detection of 5,326 uniquely mapping SAAVs across 2,794 proteins. With such a large amount of data, the control of false positives is a challenge. We present the methodology and results for maintaining rigorous quality, along with a discussion of the implications of the remaining sources of errors in the build. We check our uncertainty estimates against a set of olfactory receptor proteins not expected to be present in the set. We show how the use of synthetic reference spectra can provide confirmatory evidence for claims of detection of proteins with weak evidence.

This work has been supported by the US NIH BD2K grant U54 GM114833 and a National Natural Science Foundation of China grant (61501071). A.I.N. is supported by US National Institute of Health grant (R01-GM-094231). Y.P.-R. is supported by BBSRC ‘PROCESS’ grant (BB/K01997X/1). M.B. is supported by Projects of International Cooperation and Exchanges grant (2014DFB30010). M.W. is supported by an NIH grant (5P41GM103484-07). J.A.V. and N.d.-T. are supported by the Wellcome Trust (grant WT101477MA). T.T. is supported by the BBSRC ‘ProteoGenomics’ grant (BB/L024225/1). E.W.D. and D.S.C. are supported in part by grant (U24 AI117966- 02S1). S.-A.S. is supported in part by US NIH BD2K grant (1U24AI117966-01). M.W. and N.Bandeira were supported by NIH grant (5P41GM103484-07). N.Bandeira was also partially supported as an Alfred P. Sloan Fellow. S.Subramaniam is supported by NIH grants U01 DK097430 and U01 CA198941

SummaryResearch advancing our understanding of Mycobacterium tuberculosis (Mtb) biology and complex host-Mtb interactions requires consistent and precise quantitative measurements of Mtb proteins. We describe the generation and validation of a compendium of assays to quantify 97% of the 4,012 annotated Mtb proteins by the targeted mass spectrometric method selected reaction monitoring (SRM). Furthermore, we estimate the absolute abundance for 55% of all Mtb proteins, revealing a dynamic range within the Mtb proteome of over four orders of magnitude, and identify previously unannotated proteins. As an example of the assay library utility, we monitored the entire Mtb dormancy survival regulon (DosR), which is linked to anaerobic survival and Mtb persistence, and show its dynamic protein-level regulation during hypoxia. In conclusion, we present a publicly available research resource that supports the sensitive, precise, and reproducible quantification of virtually any Mtb protein by a robust and widely accessible mass spectrometric method.

Organ-enriched blood proteins, those produced primarily in one organ and secreted or exported to the blood, potentially afford a powerful and specific approach to assessing diseases in their cognate organs. In this paper, we demonstrate that quantification of organ-enriched proteins in the blood offers a new strategy to find biomarkers for diagnosis and assessment of drug-induced liver injury (and presumably the assessment of other liver diseases). We used selected reaction monitoring (SRM) mass spectrometry to quantify 81 liver-enriched proteins plus three aminotransferases (ALT1, AST1, and AST2) in plasma of C57BL/6J and NOD/ShiLtJ mice exposed to acetaminophen or carbon tetrachloride. Plasma concentrations of 49 liver-enriched proteins were perturbed significantly in response to liver injury induced by one or both toxins. We validated four of these toxin-responsive proteins (ALDOB, ASS1, BHMT and GLUD1) by Western blotting. By both assays, these four proteins constitute liver injury markers superior to currently employed markers such as ALT and AST. A similar approach was also successful in human serum where we had analyzed 66 liver-enriched proteins in acetaminophen overdose patients. Of these, 23 proteins were elevated in patients; 15 of 23 overlapped with the concentration-increased proteins in the mouse study. A combination of 5 human proteins, AGXT, ALDOB, CRP, FBP1, and MMP9, provides the best diagnostic performance to distinguish acetaminophen overdose patients from controls (sensitivity: 0.85, specificity: 0.84, accuracy: 85%). These five blood proteins are candidates for detecting acetaminophen-induced liver injury using next-generation diagnostic devices (e.g, microfluidic ELIZA assays).

The results of analysis of shotgun proteomics mass spectrometry data can be greatly affected by the selection of the reference protein sequence database against which the spectra are matched. For many species there are multiple sources from which somewhat different sequence sets can be obtained. This can lead to confusion about which database is best in which circumstances – a problem especially acute in human sample analysis. All sequence databases are genome-based, with sequences for the predicted gene and their protein translation products compiled. Our goal is to create a set of primary sequence databases that comprise the union of sequences from many of the different available sources and make the result easily available to the community. We have compiled a set of four sequence databases of varying sizes, from a small database consisting of only the ~20,000 primary isoforms plus contaminants to a very large database that includes almost all non-redundant protein sequences from several sources. This set of tiered, increasingly complete human protein sequence databases suitable for mass spectrometry proteomics sequence database searching is called the Tiered Human Integrated Search Proteome set. In order to evaluate the utility of these databases, we have analyzed two different data sets, one from the HeLa cell line and the other from normal human liver tissue, with each of the four tiers of database complexity. The result is that approximately 0.8%, 1.1%, and 1.5% additional peptides can be identified for Tiers 2, 3, and 4, respectively, as compared with the Tier 1 database, at substantially increasing computational cost. This increase in computational cost may be worth bearing if the identification of sequence variants or the discovery of sequences that are not present in the reviewed knowledge base entries is an important goal of the study. We find that it is useful to search a data set against a simpler database, and then check the uniqueness of the discovered peptides against a more complex database. We have set up an automated system that downloads all the source databases on the first of each month and automatically generates a new set of search databases and makes them available for download at http://www.peptideatlas.org/thisp/.

Biomedical data, in particular omics datasets are being generated at an unprecedented rate. This is due to the falling costs of generating experimental data, improved accuracy and better accessibility to different omics platforms such as genomics, proteomics and metabolomics1,2. As a result, the number of deposited datasets in public repositories originating from various omics approaches has increased dramatically in recent years. With strong support from scientific journals and funders, public data sharing is increasingly considered to be a good scientific practice, facilitating the confirmation of original results, increasing the reproducibility of the analyses, enabling the exploration of new or related hypotheses, and fostering the identification of potential errors, discouraging fraud3. This increase in public data deposition of omics results is a good starting point, but opens up a series of new challenges. For example the research community must now find more efficient ways for storing, organizing and providing access to biomedical data across platforms. These challenges range from achieving a common representation framework for the datasets and the associated metadata from different omics fields, to the availability of efficient methods, protocols and file formats for data exchange between multiple repositories. Therefore, there is a great need for development of new platforms and applications to make possible to search datasets across different omics fields, making such information accessible to the end-user. The FAIR paradigm describes a set of guiding principles to address many of these issues, and aims to make data Findable, Accessible, Interoperable and Re-usable(https://www.force11.org/group/fairgroup/fairprinciples).

Public repositories for proteomics data have accelerated proteomics research by enabling more efficient cross-analyses of datasets, supporting the creation of protein and peptide compendia of experimental results, supporting the development and testing of new software tools, and facilitating the manuscript review process. The repositories available to date have been designed to accommodate either shotgun experiments or generic proteomic data files. Here, we describe a new kind of proteomic data repository for the collection and representation of data from selected reaction monitoring (SRM) measurements. The PeptideAtlas SRM Experiment Library (PASSEL) allows researchers to easily submit proteomic data sets generated by SRM. The raw data are automatically processed in a uniform manner and the results are stored in a database, where they may be downloaded or browsed via a web interface that includes a chromatogram viewer. PASSEL enables cross-analysis of SRM data, supports optimization of SRM data collection, and facilitates the review process of SRM data. Further, PASSEL will help in the assessment of proteotypic peptide performance in a wide array of samples containing the same peptide, as well as across multiple experimental protocols.

Upper gastrointestinal (GI) cancers of the stomach and esophagus have high incidence and mortality worldwide, but they are uncommon in Western countries. Little information exists on the association between vitamin D and risk of upper GI cancers. This study examined the association between circulating 25-hydroxyvitamin D (25(OH)D) and upper GI cancer risk in the Cohort Consortium Vitamin D Pooling Project of Rarer Cancers. Concentrations of 25(OH)D were measured from 1,065 upper GI cancer cases and 1,066 age-, sex-, race-, and season-of blood draw-matched controls from 8 prospective cohort studies. In multivariate-adjusted models, circulating 25(OH)D concentration was not significantly associated with upper GI cancer risk. Subgroup analysis by race showed that among Asians, but not Caucasians, lower concentrations of 25(OH)D (

IntroductionA proof-of-concept demonstration of the use of label-free quantitative glycoproteomics for biomarker discovery workflow is presented in this paper, using a mouse model for skin cancer as an example.Materials and MethodsBlood plasma was collected from ten control mice and ten mice having a mutation in the p19ARFgene, conferring them high propensity to develop skin cancer after carcinogen exposure. We enriched for N-glycosylated plasma proteins, ultimately generating deglycosylated forms of the tryptic peptides for liquid chromatography mass spectrometry (LC-MS) analyses. LC-MS runs for each sample were then performed with a view to identifying proteins that were differentially abundant between the two mouse populations. We then used a recently developed computational framework, Corra, to perform peak picking and alignment, and to compute the statistical significance of any observed changes in individual peptide abundances. Once determined, the most discriminating peptide features were then fragmented and identified by tandem mass spectrometry with the use of inclusion lists.Results and DiscussionsWe assessed the identified proteins to see if there were sets of proteins indicative of specific biological processes that correlate with the presence of disease, and specifically cancer, according to their functional annotations. As expected for such sick animals, many of the proteins identified were related to host immune response. However, a significant number of proteins are also directly associated with processes linked to cancer development, including proteins related to the cell cycle, localization, transport, and cell death. Additional analysis of the same samples in profiling mode, and in triplicate, confirmed that replicate MS analysis of the same plasma sample generated less variation than that observed between plasma samples from different individuals, demonstrating that the reproducibility of the LC-MS platform was sufficient for this application.ConclusionThese results thus show that an LC-MS-based workflow can be a useful tool for the generation of candidate proteins of interest as part of a disease biomarker discovery effort.

We describe the creation process of the Minimum Information Specification for In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE). Modeled after the existing minimum information specification for microarray data, we created a new specification for gene expression localization experiments, initially to facilitate data sharing within a consortium. After successful use within the consortium, the specification was circulated to members of the wider biomedical research community for comment and refinement. After a period of acquiring many new suggested requirements, it was necessary to enter a final phase of excluding those requirements that were deemed inappropriate as a minimum requirement for all experiments. The full specification will soon be published as a version 1.0 proposal to the community, upon which a more full discussion must take place so that the final specification may be achieved with the involvement of the whole community. This paper is part of the special issue of OMICS on data standards.

Genome sequences are available for many bacterial strains, but there has been little progress in using these data to understand the molecular basis of pathogen emergence and differences in strain virulence. Serotype M3 strains of group AStreptococcus(GAS) are a common cause of severe invasive infections with unusually high rates of morbidity and mortality. To gain insight into the molecular basis of this high-virulence phenotype, we sequenced the genome of strain MGAS315, an organism isolated from a patient with streptococcal toxic shock syndrome. The genome is composed of 1,900,521 bp, and it shares ≈1.7 Mb of related genetic material with genomes of serotype M1 and M18 strains. Phage-like elements account for the great majority of variation in gene content relative to the sequenced M1 and M18 strains. Recombination produces chimeric phages and strains with previously uncharacterized arrays of virulence factor genes. Strain MGAS315 has phage genes that encode proteins likely to contribute to pathogenesis, such as streptococcal pyrogenic exotoxin A (SpeA) and SpeK, streptococcal superantigen (SSA), and a previously uncharacterized phospholipase A2(designated Sla). Infected humans had anti-SpeK, -SSA, and -Sla antibodies, indicating that these GAS proteins are madein vivo. SpeK and SSA were pyrogenic and toxic for rabbits. Serotype M3 strains with the phage-encodedspeKandslagenes increased dramatically in frequency late in the 20th century, commensurate with the rise in invasive disease caused by M3 organisms. Taken together, the results show that phage-mediated recombination has played a critical role in the emergence of a new, unusually virulent clone of serotype M3 GAS.

Mass spectrometry is the method of choice for deep and reliable exploration of the (human) proteome. Targeted mass spectrometry reliably detects and quantifies pre-determined sets of proteins in a complex biological matrix and is used in studies that rely on the quantitatively accurate and reproducible measurement of proteins across multiple samples. It requires the one-time, a priori generation of a specific measurement assay for each targeted protein. SWATH-MS is a mass spectrometric method that combines data-independent acquisition (DIA) and targeted data analysis and vastly extends the throughput of proteins that can be targeted in a sample compared to selected reaction monitoring (SRM). Here we present a compendium of highly specific assays covering more than 10,000 human proteins and enabling their targeted analysis in SWATH-MS datasets acquired from research or clinical specimens. This resource supports the confident detection and quantification of 50.9% of all human proteins annotated by UniProtKB/Swiss-Prot and is therefore expected to find wide application in basic and clinical research. Data are available via ProteomeXchange (PXD000953-954) and SWATHAtlas (SAL00016-35).

Glioblastoma multiforme is a highly invasive and aggressive brain tumor with an invariably poor prognosis. The overexpression of epidermal growth factor receptor (EGFR) is a primary influencer of invasion and proliferation in tumor cells and the constitutively active EGFRvIII mutant, found in 30–65% of Glioblastoma multiforme, confers more aggressive invasion. To better understand how EGFR contributes to tumor aggressiveness, we investigated the effect of EGFR on the secreted levels of 65 rationally selected proteins involved in invasion. We employed selected reaction monitoring targeted mass spectrometry using stable isotope labeled internal peptide standards to quantity proteins in the secretome from five GBM (U87) isogenic cell lines in which EGFR, EGFRvIII, and/or PTEN were expressed. Our results show that cell lines with EGFR overexpression and constitutive EGFRvIII expression differ remarkably in the expression profiles for both secreted and intracellular signaling proteins, and alterations in EGFR signaling result in reproducible changes in concentrations of secreted proteins. Furthermore, the EGFRvIII-expressing mutant cell line secretes the majority of the selected invasion-promoting proteins at higher levels than other cell lines tested. Additionally, the intracellular and extracellular protein measurements indicate elevated oxidative stress in the EGFRvIII-expressing cell line. In conclusion, the results of our study demonstrate that EGFR signaling has a significant effect on the levels of secreted invasion-promoting proteins, likely contributing to the aggressiveness of Glioblastoma multiforme. Further characterization of these proteins may provide candidates for new therapeutic strategies and targets as well as biomarkers for this aggressive disease.

PeptideAtlas, SRMAtlas, and PASSEL are Web-accessible resources to support discovery and targeted proteomics research. PeptideAtlas is a multi-species compendium of shotgun proteomic data provided by the scientific community; SRMAtlas is a resource of high-quality, complete proteome SRM assays generated in a consistent manner for the targeted identification and quantification of proteins; and PASSEL is a repository that compiles and represents selected reaction monitoring data, all in an easy-to-use interface. The databases are generated from native mass spectrometry data files that are analyzed in a standardized manner including statistical validation of the results. Each resource offers search functionalities and can be queried by user-defined constraints; the query results are provided in tables or are graphically displayed. PeptideAtlas, SRMAtlas, and PASSEL are publicly available freely via the Web site http://www.peptideatlas.org. In this protocol, we describe the use of these resources, we highlight how to submit, search, collate and download data. Curr. Protoc. Bioinform. 46:13.25.1-13.25.28. © 2014 by John Wiley & Sons, Inc. Keywords: discovery proteomics; targeted proteomics; selected reaction monitoring (SRM); data repository; data resource; complete proteome library

The electron spin resonance (e.s.r.) spin-probe method has been applied to investigate the matrix heterogeneity of natural rubbers differing in molecular weight, molecular weight distribution and gel content. The composite temperature-dependent e.s.r. spectra above the glass transition temperature (Tg) were evaluated in order to obtain the population of spin probes contributing to restricted and fast motion. From the ratio of spin probes distributed in the two motionally different environments, microstructural heterogeneities of the rubber matrix are observed. It is shown that the restricted spin-probe motion above Tg is predominantly determined by the amount of gel phase and structure of the gel. Chemical treatment and mastication of natural rubber, which have a complex influence on gel fraction, molecular weight and intermolecular interactions, have been considered in analysis of the temperature dependence of the e.s.r. spectra. A comparison between natural rubber and a synthetic polyisoprene confirms the contribution of the gel phase and its structure to the microstructural characteristics of rubber matrices.

Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage. Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun) and the other supporting hypothesis-driven (targeted) proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis, which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.

Differential next-generation-omics approaches aid in the visualization of biological processes and pave the way for divulging important events and/or interactions leading to a functional output at cellular or systems level. To this end, we undertook an integrated Nextgen transcriptomics and proteomics approach to divulge differential gene expression of infant and pubertal rat Sertoli cells (Sc).Unlike, pubertal Sc, infant Sc are immature and fail to support spermatogenesis. We found exclusive association of 14 and 19 transcription factor binding sites to infantile and pubertal states of Sc, respectively, using differential transcriptomics-guided genome-wide computational analysis of relevant promoters employing 220 Positional Weight Matrices from the TRANSFAC database. Proteomic SWATH-MS analysis provided extensive quantification of nuclear and cytoplasmic protein fractions revealing 1,670 proteins differentially located between the nucleus and cytoplasm of infant Sc and 890 proteins differentially located within those of pubertal Sc. Based on our multi-omics approach, the transcription factor YY1 was identified as one of the lead candidates regulating differentiation of Sc.YY1 was found to have abundant binding sites on promoters of genes upregulated during puberty. To determine its significance, we generated transgenic rats with Sc specific knockdown of YY1 that led to compromised spermatogenesis.

Internally powered, implanted pulse generators (IPGs) have been an important advance in spinal cord stimulation for the management of pain, but they require surgical replacement, with attendant cost and risk, when the implanted battery is depleted. Battery life is determined by the programmed settings of the implant, but until now the technical means to optimize settings for maximal battery life, delaying surgical replacement as long as possible, Materials and Methods. We have developed a patient-interactive, computerized programmer for use with IPGs. It has been designed for easy operation and comprehensive data management, which have not been features of the standard programmers available until now. It automatically and rapidly presents to the patient a sequence of settings (contact combinations and pulse parameters) specified by the practitioner. Test results are analyzed and sorted to determine the optimal settings by multiple criteria, including battery life. In the present study we used new, improved algorithms to estimate battery life. We have compared the computerized, patient-interactive system with standard practitioner-operated, manual programming methods in a randomized, controlled trial in 44 patients at two study centers. In 95% of patients (41/43), the computerized, patient-interactive system identified new settings with improved estimated battery life (and corresponding anticipated cost savings) which had not been recognized as such using manual methods. The estimated battery life for the setting chosen by each patient using manual methods averaged 25.4 ± 49.5 (mean ± standard deviation) months; the longest battery life identified by computerized methods averaged 55.0 ± 71.7, a 2.2-fold or 29.6 month improvement. Seventy-two percent of patients (31/43) achieved better battery life at settings with technical results (visual analog scale rating of overlap or coverage of pain by stimulation paresthesias) equal or superior to those achieved by manual methods. The overall improvement over the setting chosen by manual methods was 1.41-fold or 10.5 months; averaged by patient, the improvement was 1.63-fold. Estimated cost savings averaged just over one-third. As reported previously, the new system also yields significantly (p

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.

Background Since its inception, proteomics has essentially operated in a discovery mode with the goal of identifying and quantifying the maximal number of proteins in a sample. Increasingly, proteomic measurements are also supporting hypothesis-driven studies, in which a predetermined set of proteins is consistently detected and quantified in multiple samples. Selected reaction monitoring (SRM) is a targeted mass spectrometric technique that supports the detection and quantification of specific proteins in complex samples at high sensitivity and reproducibility. Here, we describe ATAQS, an integrated software platform that supports all stages of targeted, SRM-based proteomics experiments including target selection, transition optimization and post acquisition data analysis. This software will significantly facilitate the use of targeted proteomic techniques and contribute to the generation of highly sensitive, reproducible and complete datasets that are particularly critical for the discovery and validation of targets in hypothesis-driven studies in systems biology. Result We introduce a new open source software pipeline, ATAQS (Automated and Targeted Analysis with Quantitative SRM), which consists of a number of modules that collectively support the SRM assay development workflow for targeted proteomic experiments (project management and generation of protein, peptide and transitions and the validation of peptide detection by SRM). ATAQS provides a flexible pipeline for end-users by allowing the workflow to start or end at any point of the pipeline, and for computational biologists, by enabling the easy extension of java algorithm classes for their own algorithm plug-in or connection via an external web site. This integrated system supports all steps in a SRM-based experiment and provides a user-friendly GUI that can be run by any operating system that allows the installation of the Mozilla Firefox web browser. Conclusions Targeted proteomics via SRM is a powerful new technique that enables the reproducible and accurate identification and quantification of sets of proteins of interest. ATAQS is the first open-source software that supports all steps of the targeted proteomics workflow. ATAQS also provides software API (Application Program Interface) documentation that enables the addition of new algorithms to each of the workflow steps. The software, installation guide and sample dataset can be found in http://tools.proteomecenter.org/ATAQS/ATAQS.html, BMC Bioinformatics, 12, ISSN:1471-2105

The developed, directed mass spectrometry workflow allows to generate consistent and system-wide quantitative maps of microbial proteomes in a single analysis. Application to the human pathogen L. interrogans revealed mechanistic proteome changes over time involved in pathogenic progression and antibiotic defense, and new insights about the regulation of absolute protein abundances within operons., The developed, directed proteomic approach allowed consistent detection and absolute quantification of 1680 proteins of the human pathogen L. interrogans in a single LC–MS/MS experiment. The comparison of 25 extensive, consistent and quantitative proteome maps revealed new insights about the proteome changes involved in pathogenic progression and antibiotic defense of L. interrogans, and about the regulation of protein abundances within operons. The generated time-resolved data sets are compatible with pattern analysis algorithms developed for transcriptomics, including hierarchical clustering and functional enrichment analysis of the detected profile clusters. This is the first study that describes the absolute quantitative behavior of any proteome over multiple states and represents the most comprehensive proteome abundance pattern comparison for any organism to date., Over the last decade, mass spectrometry (MS)-based proteomics has evolved as the method of choice for system-wide proteome studies and now allows for the characterization of several thousands of proteins in a single sample. Despite these great advances, redundant monitoring of protein levels over large sample numbers in a high-throughput manner remains a challenging task. New directed MS strategies have shown to overcome some of the current limitations, thereby enabling the acquisition of consistent and system-wide data sets of proteomes with low-to-moderate complexity at high throughput. In this study, we applied this integrated, two-stage MS strategy to investigate global proteome changes in the human pathogen L. interrogans. In the initial discovery phase, 1680 proteins (out of around 3600 gene products) could be identified (Schmidt et al, 2008) and, by focusing precious MS-sequencing time on the most dominant, specific peptides per protein, all proteins could be accurately and consistently monitored over 25 different samples within a few days of instrument time in the following scoring phase (Figure 1). Additionally, the co-analysis of heavy reference peptides enabled us to obtain absolute protein concentration estimates for all identified proteins in each perturbation (Malmström et al, 2009). The detected proteins did not show any biases against functional groups or protein classes, including membrane proteins, and span an abundance range of more than three orders of magnitude, a range that is expected to cover most of the L. interrogans proteome (Malmström et al, 2009). To elucidate mechanistic proteome changes over time involved in pathogenic progression and antibiotic defense of L. interrogans, we generated time-resolved proteome maps of cells perturbed with serum and three different antibiotics at sublethal concentrations that are currently used to treat Leptospirosis. This yielded an information-rich proteomic data set that describes, for the first time, the absolute quantitative behavior of any proteome over multiple states, and represents the most comprehensive proteome abundance pattern comparison for any organism to date. Using this unique property of the data set, we could quantify protein components of entire pathways across several time points and subject the data sets to cluster analysis, a tool that was previously limited to the transcript level due to incomplete sampling on protein level (Figure 4). Based on these analyses, we could demonstrate that Leptospira cells adjust the cellular abundance of a certain subset of proteins and pathways as a general response to stress while other parts of the proteome respond highly specific. The cells furthermore react to individual treatments by ‘fine tuning' the abundance of certain proteins and pathways in order to cope with the specific cause of stress. Intriguingly, the most specific and significant expression changes were observed for proteins involved in motility, tissue penetration and virulence after serum treatment where we tried to simulate the host environment. While many of the detected protein changes demonstrate good agreement with available transcriptomics data, most proteins showed a poor correlation. This includes potential virulence factors, like Loa22 or OmpL1, with confirmed expression in vivo that were significantly up-regulated on the protein level, but not on the mRNA level, strengthening the importance of proteomic studies. The high resolution and coverage of the proteome data set enabled us to further investigate protein abundance changes of co-regulated genes within operons. This suggests that although most proteins within an operon respond to regulation synchronously, bacterial cells seem to have subtle means to adjust the levels of individual proteins or protein groups outside of the general trend, a phenomena that was recently also observed on the transcript level of other bacteria (Güell et al, 2009). The method can be implemented with standard high-resolution mass spectrometers and software tools that are readily available in the majority of proteomics laboratories. It is scalable to any proteome of low-to-medium complexity and can be extended to post-translational modifications or peptide-labeling strategies for quantification. We therefore expect the approach outlined here to become a cornerstone for microbial systems biology., Over the past decade, liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) has evolved into the main proteome discovery technology. Up to several thousand proteins can now be reliably identified from a sample and the relative abundance of the identified proteins can be determined across samples. However, the remeasurement of substantially similar proteomes, for example those generated by perturbation experiments in systems biology, at high reproducibility and throughput remains challenging. Here, we apply a directed MS strategy to detect and quantify sets of pre-determined peptides in tryptic digests of cells of the human pathogen Leptospira interrogans at 25 different states. We show that in a single LC–MS/MS experiment around 5000 peptides, covering 1680 L. interrogans proteins, can be consistently detected and their absolute expression levels estimated, revealing new insights about the proteome changes involved in pathogenic progression and antibiotic defense of L. interrogans. This is the first study that describes the absolute quantitative behavior of any proteome over multiple states, and represents the most comprehensive proteome abundance pattern comparison for any organism to date.

Background: Expression levels of mRNA and protein by cell types exhibit a range of correlations for different genes. In this study, we compared levels of mRNA abundance for several cluster designation (CD) genes determined by gene arrays using magnetic sorted and laser-capture microdissected human prostate cells with levels of expression of the respective CD proteins determined by immunohistochemical staining in the major cell types of the prostate – basal epithelial, luminal epithelial, stromal fibromuscular, and endothelial – and for prostate precursor/stem cells and prostate carcinoma cells. Immunohistochemical stains of prostate tissues from more than 50 patients were scored for informative CD antigen expression and compared with cell-type specific transcriptomes. Results: Concordance between gene and protein expression findings based on 'present' vs. 'absent' calls ranged from 46 to 68%. Correlation of expression levels was poor to moderate (Pearson correlations ranged from 0 to 0.63). Divergence between the two data types was most frequently seen for genes whose array signals exceeded background (> 50) but lacked immunoreactivity by immunostaining. This could be due to multiple factors, e.g. low levels of protein expression, technological sensitivities, sample processing, probe set definition or anatomical origin of tissue and actual biological differences between transcript and protein abundance. Conclusion: Agreement between these two very different methodologies has great implications for their respective use in both molecular studies and clinical trials employing molecular biomarkers.

Background: In a few animal experiments, aspartame has been linked to hematopoietic and brain cancers. Most animal studies have found no increase in the risk of these or other cancers. Data on humans are sparse for either cancer. Concern lingers regarding this widely used artificial sweetener. Objective: We investigated prospectively whether aspartame consumption is associated with the risk of hematopoietic cancers or gliomas (malignant brain cancer). Methods: We examined 285,079 men and 188,905 women ages 50 to 71 years in the NIH-AARP Diet and Health Study cohort. Daily aspartame intake was derived from responses to a baseline self-administered food frequency questionnaire that queried consumption of four aspartame-containing beverages (soda, fruit drinks, sweetened iced tea, and aspartame added to hot coffee and tea) during the past year. Histologically confirmed incident cancers were identified from eight state cancer registries. Multivariable-adjusted relative risks (RR) and 95% confidence intervals (CI) were estimated using Cox proportional hazards regression that adjusted for age, sex, ethnicity, body mass index, and history of diabetes. Results: During over 5 years of follow-up (1995-2000), 1,888 hematopoietic cancers and 315 malignant gliomas were ascertained. Higher levels of aspartame intake were not associated with the risk of overall hematopoietic cancer (RR for ≥600 mg/d, 0.98; 95% CI, 0.76-1.27), glioma (RR for ≥400 mg/d, 0.73; 95% CI, 0.46-1.15; P for inverse linear trend = 0.05), or their subtypes in men and women. Conclusions: Our findings do not support the hypothesis that aspartame increases hematopoietic or brain cancer risk. (Cancer Epidemiol Biomarkers Prev 2006;15(9):1654–9)

A history of diabetes has been hypothesized to decrease prostate cancer risk, but studies have not always considered confounding or effect modification by dietary or lifestyle factors.We examined the association between diabetes history and subsequent prostate cancer risk in 328,316 men enrolled in the NIH-AARP Diet and Health Study. Participants were ages 50-71 years and without a prostate cancer diagnosis at baseline in 1995. A prior history of physician-diagnosed diabetes was assessed using a self-administered mailed questionnaire. Cases of prostate cancer were ascertained by matching the cohort to state cancer registries. Multivariable relative risks (RR) and 95% confidence intervals (CI) of prostate cancer were estimated using Cox regression.During 5 years and 1,432,676 person-years of follow-up, 11,193 prostate cancer cases were ascertained. The age-adjusted and multivariable RRs of prostate cancer comparing men with diabetes to those without diabetes were 0.69 (95% CI=0.64, 0.74) and 0.71 (95% CI=0.66, 0.76), respectively, indicating no important confounding. The inverse association between diabetes and prostate cancer was particularly strong among men in the highest category of routine physical activity at work or home (RR=0.41; 95% CI=0.23, 0.74; p value for test of interaction = 0.03). Findings were similar for organ-confined and advanced prostate cancer.Results from this large prospective study suggest that a history of diabetes is associated with a decreased risk of prostate cancer. The relationship strengthened with high levels of routine physical activity. Because increased physical activity is associated with lower circulating levels of insulin and testosterone, our findings support a role of hypoinsulinemia and low androgenicity linking diabetes to decreased prostate cancer risk.

Background The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types. Results SBEAMS-Microarray, a module of the open-source Systems Biology Experiment Analysis Management System (SBEAMS), enables MIAME-compliant storage, management, analysis, and integration of high-throughput genomic expression data. It is interoperable with the Cytoscape network integration, visualization, analysis, and modeling software platform. Conclusion SBEAMS-Microarray provides end-to-end support for genomic expression analyses for network-based systems biology research.

OBJECTIVE Programmable, multicontact, implanted stimulation devices represent an important advance in spinal cord stimulation for the management of pain. They facilitate the technical goal of covering areas of pain by stimulation-evoked paresthesiae. Adjustment after implantation requires major investments of time and effort, however, if the capabilities of these devices are to be used to full advantage. The objective of maximizing coverage should be met while using practitioners' time efficiently. METHODS We have developed a patient-interactive, computerized system designed for greater ease and safety of operation, compared with the standard external devices used to control and adjust implanted pulse generators. The system automatically and rapidly presents to the patient the contact combinations and pulse parameters specified by the practitioner. The patient adjusts the amplitude of stimulation and then records drawings of stimulation paresthesiae (for comparison with pain drawings), followed by visual analog scale ratings for each setting. Test results are analyzed and sorted to determine the optimal settings. We compared the automated, patient-interactive system with traditional, practitioner-operated, manual programming methods in a randomized controlled trial at two study centers, with 44 patients. RESULTS The automated, patient-interactive system yielded significantly (P < 0.0001) better technical results than did traditional manual methods, in achieving coverage of pain by stimulation paresthesiae (mean 100-point visual analog scale ratings of 70 and 46, respectively). The visual analog scale ratings were higher for automated testing for 38 patients, higher for manual testing for 0 patients, and equal (tied) for 6 patients. Multivariate analysis demonstrated that the advantage of automated testing occurred independently of practitioner experience; the advantage was significantly greater, however, for experienced patients. The rate of testing (number of settings tested per unit time) was significantly (P < 0.0001) greater for the automated system, in comparison with the rate with a human operator using traditional, manual, programming methods (mean of 0.73 settings/min versus 0.49 settings/min). The automated system also identified settings with improved estimated battery life (and corresponding anticipated cost savings). No complications were observed with automated testing; one complication (transient discomfort attributable to excessive stimulation) occurred with manual testing. CONCLUSION Automated, patient-interactive adjustment of implanted spinal cord stimulators is significantly more effective and more efficient than traditional manual methods of adjustment. It offers not only improved clinical efficacy but also potential cost savings in extending implanted battery life. It has the additional potential advantages of standardization, quality control, and record keeping, to facilitate clinical research and patient care. It should enhance the clinical application of spinal cord stimulation for the treatment of chronic intractable pain.

Targeted proteomics via selected reaction monitoring is a powerful mass spectrometric technique affording higher dynamic range, increased specificity and lower limits of detection than other shotgun mass spectrometry methods when applied to proteome analyses. However, it involves selective measurement of predetermined analytes, which requires more preparation in the form of selecting appropriate signatures for the proteins and peptides that are to be targeted. There is a growing number of software programs and resources for selecting optimal transitions and the instrument settings used for the detection and quantification of the targeted peptides, but the exchange of this information is hindered by a lack of a standard format. We have developed a new standardized format, called TraML, for encoding transition lists and associated metadata. In addition to introducing the TraML format, we demonstrate several implementations across the community, and provide semantic validators, extensive documentation, and multiple example instances to demonstrate correctly written documents. Widespread use of TraML will facilitate the exchange of transitions, reduce time spent handling incompatible list formats, increase the reusability of previously optimized transitions, and thus accelerate the widespread adoption of targeted proteomics via selected reaction monitoring.

This document compares contractor maintenance and military maintenance for a battlefield system comprised mainly of commercial data processing equipment. As the military approaches the increasingly digital battlefield with streamlined acquisition methodologies, the importance of technically qualified and highly mobile military repairpersons becomes paramount. The document concludes that battlefield systems must have battlefield support. otherwise the system may fail to support its users.

The phosphorylation and dephosphorylation of proteins by kinases and phosphatases constitute an essential regulatory network in eukaryotic cells. This network supports the flow of information from sensors through signaling systems to effector molecules, and ultimately drives the phenotype and function of cells, tissues, and organisms. Dysregulation of this process has severe consequences and is one of the main factors in the emergence and progression of diseases, including cancer. Thus, major efforts have been invested in developing specific inhibitors that modulate the activity of individual kinases or phosphatases; however, it has been difficult to assess how such pharmacological interventions would affect the cellular signaling network as a whole. Here, we used label-free, quantitative phosphoproteomics in a systematically perturbed model organism (Saccharomyces cerevisiae) to determine the relationships between 97 kinases, 27 phosphatases, and more than 1000 phosphoproteins. We identified 8814 regulated phosphorylation events, describing the first system-wide protein phosphorylation network in vivo. Our results show that, at steady state, inactivation of most kinases and phosphatases affected large parts of the phosphorylation-modulated signal transduction machinery, and not only the immediate downstream targets. The observed cellular growth phenotype was often well maintained despite the perturbations, arguing for considerable robustness in the system. Our results serve to constrain future models of cellular signaling and reinforce the idea that simple linear representations of signaling pathways might be insufficient for drug development and for describing organismal homeostasis.

Perhaps the most remarkable achievement of the semiconductor industry, in recent years, has been the spectacular reduction in component size. The (basically financial) forces which continue to fuel this process have also brought about several technological advances. The reduction in transit time which results from reduced component size has led to improved gains and switching times [e.g. 1]. Smaller components require less external circuitry and, because internal circuitry is more reliable, more components may be interconnected with an associated increase in the complexity of the tasks performable by an integrated circuit. Larger memories are available for the same size of chip, thus manufacturers of, for example, Workstations and Minicomputers are able to increase their built-in RAM capacity with no loss of space. Of course, miniaturisation has also compounded some long standing problems. Some of the failure mechanisms which beset large scale integrated (LSI) circuits are magnified in the very large scale integrated (VLSI) circuits available today.

This study demonstrates utilization of VSP data for resolving interpretation ambiguity from surface seismic data. The downgoing waves from a suitably designed VSP survey are combined to simulate a tomography problem. The solution lends itself to a velocity model that helps to resolve ambiguity in interpretation, Additionally the ray coverage derived from the final model helps to obtain an understanding of spatial resolving power of the particular tomographic data set.

Background Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics. Results We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling. Conclusion The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.

There has been considerable recent interest in proteomic analyses of plasma for the purpose of discovering biomarkers. Profiling N-linked glycopeptides is a particularly promising method because the population of N-linked glycosites represents the proteomes of plasma, the cell surface, and secreted proteins at very low redundancy and provides a compelling link between the tissue and plasma proteomes. Here, we describe UniPep http://www.unipep.org - a database of human N-linked glycosites - as a resource for biomarker discovery.

Vulcanizates containing only di- and poly-suiphidic crosslinks can be transformed into soluble rubber by treatment with hexane-1-thiol/piperidine, enabling examination of the crosslink sites by 1H NMR spectroscopy in solution.

The production of patulin by Penicillium patulum and P. expansum growing on apple juice and apple tissue derived from Bramley's seedling apples has been investigated. The carbon sources responsible for the patulin production have been determined using 14 C techniques and growth on defined media.

A search for nuclear substrates of the cAMP-dependent protein kinase (cAMP-d PK) of Dictyostelium discoideum led to the identification of several such proteins. Identification was based initially on increased phosphorylation of the proteins in nuclear extracts catalyzed by added cAMP-d PK. One protein of Mr 38,000 was phosphorylated also in intact nuclei and in vivo; the amount of phosphoprotein or the level of phosphorylation increased during development. Both the Mr 38,000 protein and another substrate of Mr 195,000 were found in the nuclei of prespore and prestalk cells. Phosphorylation of other potential substrates of the cAMP-d PK was either prespore or prestalk cell-specific.