BACKGROUND: Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines. RESULTS: We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation. CONCLUSIONS: By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself.

Motivation: Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of true identifications from false identifications by database searching and filtering algorithms in metaproteomics. Results: Sipros Ensemble was developed here for metaproteomics using an ensemble approach. Three diverse scoring functions from MyriMatch, Comet and the original Sipros were incorporated within a single database searching engine. Supervised classification with logistic regression was used to filter database searching results. Benchmarking with soil and marine microbial communities demonstrated a higher number of peptide and protein identifications by Sipros Ensemble than MyriMatch/Percolator, Comet/Percolator, MS-GF+/Percolator, Comet & MyriMatch/iProphet and Comet & MyriMatch & MS-GF+/iProphet. Sipros Ensemble was computationally efficient and scalable on supercomputers. Availability and implementation: Freely available under the GNU GPL license at http://sipros.omicsbio.org. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Systematic bias in mass measurement adversely affects data quality and negates the advantages of high precision instruments. RESULTS: We introduce the mzRefinery tool for calibration of mass spectrometry data files. Using confident peptide spectrum matches, three different calibration methods are explored and the optimal transform function is chosen. After calibration, systematic bias is removed and the mass measurement errors are centered at 0 ppm. Because it is part of the ProteoWizard package, mzRefinery can read and write a wide variety of file formats. AVAILABILITY AND IMPLEMENTATION: The mzRefinery tool is part of msConvert, available with the ProteoWizard open source package at http://proteowizard.sourceforge.net/ CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The National Institutes of Health (NIH), National Cancer Institute's Early Detection Research Network (EDRN) is a cross-institutional collaborative initiative seeking to accelerate the clinical application of cancer biomarker research. Over the past decade, it has been our role, as EDRN's Informatics Center (IC), to develop a comprehensive information services infrastructure as well as a set of software tools and services to support this overall initiative. We have recently developed a novel application called the Laboratory Catalog and Archive Service (LabCAS) whose focus is to extend EDRN IC data management and processing capabilities to EDRN laboratories. By leveraging the same technologies used to manage and process NASA Earth and Planetary data sets, we offer EDRN researchers an effective way of managing their laboratory data. More specifically, LabCAS enables EDRN researchers to reliably archive their experimental data, to optionally share these data in a controlled manner with other researchers, and to gain insight into these data through highly configurable data analysis pipelines tailored to the broad range of biomarker related experiments. This particular collaboration leverages expertise from NASA's Jet Propulsion Laboratory, Vanderbilt University Medical Center, and Dartmouth Medical School, as well as builds upon existing cross-governmental collaboration between NASA and the NIH.

Abstract Summary: The large amount of data produced by proteomics experiments requires effective bioinformatics tools for the integration of data management and data analysis. Here we introduce a suite of tools developed at Vanderbilt University to support production proteomics. We present the Backup Utility Service tool for automated instrument file backup and the ScanSifter tool for data conversion. We also describe a queuing system to coordinate identification pipelines and the File Collector tool for batch copying analytical results. These tools are individually useful but collectively reinforce each other. They are particularly valuable for proteomics core facilities or research institutions that need to manage multiple mass spectrometers. With minor changes, they could support other types of biomolecular resource facilities. Availability and Implementation: Source code and executable versions are available under Apache 2.0 License at http://www.vicc.org/jimayersinstitute/data/ Contact: [email protected]

Background Proteomic biomarkers are sets of proteins that may be detected in biofluids for disease detection, prognosis, and treatment selection. Vanderbilt University is taking part in two National Cancer Institute initiatives to establish a basis for clinical biomarkers. The first, Clinical Proteomic Technology Assessment for Cancer (CPTAC), characterizes proteomic technologies for discovery and validation of biomarkers. The second, the Early Detection Research Network (EDRN), attempts to develop new sets of biomarkers and evaluate their effectiveness in clinically relevant samples. The quest for biomarkers is fraught with challenges for both “bench” experiments and bioinformatics. Here, we examine sources of discrepancy at several levels. The search engines that identify peptides from LC-MS/MS experiments differ significantly in the set of spectra that they identify from experiments. Parsimonious protein assembly may prune out proteins in one experiment and retain them in another. Protein differentiation tools may yield divergent results, even when starting from the same data sets. Verifying biomarkers through targeted proteomics has only recently been supported by tools that can work across instruments from different vendors. Translating cancer biology knowledge into clinically relevant tests for this disease will require care at all these levels.

SUMMARY: Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools. AVAILABILITY: Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project.

Shotgun proteomics is a powerful technology for protein identification in complex samples with remarkable applications in elucidating cellular and subcellular proteomes [ 1 , 2 ], and discovering disease biomarkers [ 3 , 4 ]. Shotgun proteomics data analysis usually relies on database search. Commonly used protein sequence databases in shotgun proteomics data analysis do not contain mutation information. This becomes a problem in cancer studies in which the detection of disease-related mutated peptides/proteins is crucial for understanding cancer biology [ 5 ]. Including protein mutation information into sequence databases can help alleviate this problem. Based on the human Cancer Proteome Variation Database developed by us recently [ 6 ], which comprises 41,541 nonsynonymous SNPs in 30,322 proteins from the dbSNP database and around 9000 cancer-related variations in 2,921 proteins, we created a variation-containing protein sequence database and a data analysis workflow for mutant protein identification in shotgun proteomics (Figure 1 ). Applying this workflow on colorectal cancer cell lines identified many peptides that contain either non-cancer-specific or very important cancer-related variations, such as a known somatic mutation in K-Ras in HCT116 cell line. Our workflow for mutant peptide identification has been tested for compatibility with various popular database search engines including Sequest, Mascot, X!Tandom as well as MyriMatch. Architecture for identifying mutant peptides from cancer shotgun proteome data Owing to its protein-centric nature, the approach we proposed can serve as a bridge between genomic variation data and proteomics studies in human cancer.