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PrestOMIC, an open source application for dissemination of proteomic datasets by individual laboratories.

ABSTRACT: BACKGROUND:Technological advances in mass spectrometry and other detection methods are leading to larger and larger proteomics datasets. However, when papers describing such information are published the enormous volume of data can typically only be provided as supplementary data in a tabular form through the journal website. Several journals in the proteomics field, together with the Human Proteome Organization's (HUPO) Proteomics Standards Initiative and institutions such as the Institute for Systems Biology are working towards standardizing the reporting of proteomics data, but just defining standards is only a means towards an end for sharing data. Data repositories such as ProteomeCommons.org and the Open Proteomics Database allow for public access to proteomics data but provide little, if any, interpretation. RESULTS & CONCLUSION:Here we describe PrestOMIC, an open source application for storing mass spectrometry-based proteomic data in a relational database and for providing a user-friendly, searchable and customizable browser interface to share one's data with the scientific community. The underlying database and all associated applications are built on other existing open source tools, allowing PrestOMIC to be modified as the data standards evolve. We then use PrestOMIC to present a recently published dataset from our group through our website.

SUBMITTER: Howes CG

PROVIDER: S-EPMC1892544 | biostudies-literature | 2007 Jun

REPOSITORIES: biostudies-literature

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PrestOMIC, an open source application for dissemination of proteomic datasets by individual laboratories.

Howes Charles G CG Foster Leonard J LJ

Proteome science 20070606

<h4>Background</h4>Technological advances in mass spectrometry and other detection methods are leading to larger and larger proteomics datasets. However, when papers describing such information are published the enormous volume of data can typically only be provided as supplementary data in a tabular form through the journal website. Several journals in the proteomics field, together with the Human Proteome Organization's (HUPO) Proteomics Standards Initiative and institutions such as the Instit ...[more]

PMID: 17553161

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Project description:BackgroundMicrobiome 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.ResultsWe 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.ConclusionsBy 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.

Project description:The increased prevalence and affordability of 3D scanning technology is beginning to have significant effects on the research questions and approaches available for studies of morphology. As the current trend of larger and more precise 3D datasets is unlikely to slow in the future, there is a need for efficient and capable tools for high-throughput quantitative analysis of biological shape. The promise and the challenge of implementing relatively automated methods for characterizing surface shape can be seen in the example of dental topographic analysis. Dental topographic analysis comprises a suite of techniques for quantifying tooth surfaces and component features. Topographic techniques have provided insight on mammalian molar form-function relationships and these methods could be applied to address other topics and questions. At the same time implementing multiple complementary topographic methods can have high time and labor costs, and comparability of data formats and approaches is difficult to predict. To address these challenges I present MorphoTester, an open source application for visualizing and quantifying topography from 3D triangulated polygon meshes. This application is Python-based and is free to use. MorphoTester implements three commonly used dental topographic metrics-Dirichlet normal energy, relief index, and orientation patch count rotated (OPCR). Previous OPCR algorithms have used raster-based grid data, which is not directly interchangeable with vector-based triangulated polygon meshes. A 3D-OPCR algorithm is provided here for quantifying complexity from polygon meshes. The efficacy of this metric is tested in a sample of mandibular second molars belonging to four species of cercopithecoid primates. Results suggest that 3D-OPCR is at least as effective for quantifying complexity as previous approaches, and may be more effective due to finer resolution of surface data considered here. MorphoTester represents an advancement in the automated quantification of morphology, and can be modified to adapt to future needs and priorities.

Dataset Information

PrestOMIC, an open source application for dissemination of proteomic datasets by individual laboratories.

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PrestOMIC, an open source application for dissemination of proteomic datasets by individual laboratories.

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