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Prieto G, Vázquez J. Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics. J Proteome Res 2020; 19:1285-1297. [PMID: 32037837 DOI: 10.1021/acs.jproteome.9b00819] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Shotgun proteomics is the method of choice for high-throughput protein identification; however, robust statistical methods are essential to automatize this task while minimizing the number of false identifications. The standard method for estimating the false discovery rate (FDR) of individual identifications and keeping it below a threshold (typically 1%) is the target-decoy approach. However, numerous works have shown that FDR at the protein level may become much larger than FDR at the peptide level. The development of an appropriate scoring model to identify proteins from their peptides using high-throughput shotgun proteomics is highly needed. In this study, we present a novel protein-level scoring algorithm that uses the scores of the identified peptides and maintains all of the properties expected for a true protein probability. We also present a refinement of the picked method to calculate FDR at the protein level. These algorithms can be used together as a robust identification workflow suitable for large-scale proteomics, and we show that the identification performance of this workflow is superior to that of other widely used methods in several samples and using different search engines. Our protein probability model offers the scientific community an algorithm that is easy to integrate into protein identification workflows for the automated analysis of shotgun proteomics data.
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Affiliation(s)
- Gorka Prieto
- Department of Communications Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
| | - Jesús Vázquez
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28049 Madrid, Spain
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2
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Abstract
Shotgun proteomics is the method of choice for large-scale protein identification. However, the use of a robust statistical workflow to validate such identification is mandatory to minimize false matches, ambiguities, and amplification of error rates from spectra to proteins. In this chapter we emphasize the key concepts to take into account when processing the output of a search engine to obtain reliable peptide or protein identifications. We assume that the reader is already familiar with tandem mass spectrometry so we can focus on the use of statistical confidence methods. After introducing the key concepts we present different software tools and how to use them with an example dataset.
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Affiliation(s)
- Gorka Prieto
- Department of Communications Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), Bilbao, Spain.
| | - Jesús Vázquez
- Laboratory of Cardiovascular Proteomics, Centro Nacional de Investigaciones Cardiovasculares (CNIC) and CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
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3
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Higdon R, Earl RK, Stanberry L, Hudac CM, Montague E, Stewart E, Janko I, Choiniere J, Broomall W, Kolker N, Bernier RA, Kolker E. The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2016; 19:197-208. [PMID: 25831060 DOI: 10.1089/omi.2015.0020] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare.
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Affiliation(s)
- Roger Higdon
- 1 Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
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4
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Higdon R, Kolker E. Can "normal" protein expression ranges be estimated with high-throughput proteomics? J Proteome Res 2015; 14:2398-407. [PMID: 25877823 DOI: 10.1021/acs.jproteome.5b00176] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Although biological science discovery often involves comparing conditions to a normal state, in proteomics little is actually known about normal. Two Human Proteome studies featured in Nature offer new insights into protein expression and an opportunity to assess how high-throughput proteomics measures normal protein ranges. We use data from these studies to estimate technical and biological variability in protein expression and compare them to other expression data sets from normal tissue. Results show that measured protein expression across same-tissue replicates vary by ±4- to 10-fold for most proteins. Coefficients of variation (CV) for protein expression measurements range from 62% to 117% across different tissue experiments; however, adjusting for technical variation reduced this variability by as much as 50%. In addition, the CV could also be reduced by limiting comparisons to proteins with at least 3 or more unique peptide identifications as the CV was on average 33% lower than for proteins with 2 or fewer peptide identifications. We also selected 13 housekeeping proteins and genes that were expressed across all tissues with low variability to determine their utility as a reference set for normalization and comparative purposes. These results present the first step toward estimating normal protein ranges by determining the variability in expression measurements through combining publicly available data. They support an approach that combines standard protocols with replicates of normal tissues to estimate normal protein ranges for large numbers of proteins and tissues. This would be a tremendous resource for normal cellular physiology and comparisons of proteomics studies.
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Affiliation(s)
- Roger Higdon
- †Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington 98101, United States.,‡CDO Analytics, Seattle Children's Hospital, Seattle, Washington 98101, United States
| | - Eugene Kolker
- †Bioinformatics and High-Throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, Washington 98101, United States.,‡CDO Analytics, Seattle Children's Hospital, Seattle, Washington 98101, United States.,§Departments of Biomedical Informatics and Medical Education and Pediatrics, University of Washington, Seattle, Washington 98195, United States.,∥Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States
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Montague E, Janko I, Stanberry L, Lee E, Choiniere J, Anderson N, Stewart E, Broomall W, Higdon R, Kolker N, Kolker E. Beyond protein expression, MOPED goes multi-omics. Nucleic Acids Res 2014; 43:D1145-51. [PMID: 25404128 PMCID: PMC4383969 DOI: 10.1093/nar/gku1175] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
MOPED (Multi-Omics Profiling Expression Database; http://moped.proteinspire.org) has transitioned from solely a protein expression database to a multi-omics resource for human and model organisms. Through a web-based interface, MOPED presents consistently processed data for gene, protein and pathway expression. To improve data quality, consistency and use, MOPED includes metadata detailing experimental design and analysis methods. The multi-omics data are integrated through direct links between genes and proteins and further connected to pathways and experiments. MOPED now contains over 5 million records, information for approximately 75 000 genes and 50 000 proteins from four organisms (human, mouse, worm, yeast). These records correspond to 670 unique combinations of experiment, condition, localization and tissue. MOPED includes the following new features: pathway expression, Pathway Details pages, experimental metadata checklists, experiment summary statistics and more advanced searching tools. Advanced searching enables querying for genes, proteins, experiments, pathways and keywords of interest. The system is enhanced with visualizations for comparing across different data types. In the future MOPED will expand the number of organisms, increase integration with pathways and provide connections to disease.
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Affiliation(s)
- Elizabeth Montague
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, WA, USA 98101 High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, WA, USA 98101 CDO Analytics, Seattle Children's, Seattle, WA, USA 98101 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, WA, USA 98101
| | - Imre Janko
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, WA, USA 98101 CDO Analytics, Seattle Children's, Seattle, WA, USA 98101 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, WA, USA 98101
| | - Larissa Stanberry
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, WA, USA 98101 High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, WA, USA 98101 CDO Analytics, Seattle Children's, Seattle, WA, USA 98101 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, WA, USA 98101
| | - Elaine Lee
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, WA, USA 98101 CDO Analytics, Seattle Children's, Seattle, WA, USA 98101 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, WA, USA 98101
| | - John Choiniere
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, WA, USA 98101 High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, WA, USA 98101 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, WA, USA 98101
| | - Nathaniel Anderson
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, WA, USA 98101 High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, WA, USA 98101 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, WA, USA 98101
| | - Elizabeth Stewart
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, WA, USA 98101 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, WA, USA 98101
| | - William Broomall
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, WA, USA 98101 CDO Analytics, Seattle Children's, Seattle, WA, USA 98101 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, WA, USA 98101
| | - Roger Higdon
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, WA, USA 98101 High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, WA, USA 98101 CDO Analytics, Seattle Children's, Seattle, WA, USA 98101 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, WA, USA 98101
| | - Natali Kolker
- High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, WA, USA 98101 CDO Analytics, Seattle Children's, Seattle, WA, USA 98101 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, WA, USA 98101
| | - Eugene Kolker
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, WA, USA 98101 High-Throughput Analysis Core, Seattle Children's Research Institute, Seattle, WA, USA 98101 CDO Analytics, Seattle Children's, Seattle, WA, USA 98101 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, WA, USA 98101 Departments of Biomedical Informatics and Medical Education and Pediatrics, University of Washington, Seattle, WA, USA 98109 Department of Chemistry and Chemical Biology, College of Science, Northeastern University, Boston, MA 02115
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Wilhelm M, Schlegl J, Hahne H, Gholami AM, Lieberenz M, Savitski MM, Ziegler E, Butzmann L, Gessulat S, Marx H, Mathieson T, Lemeer S, Schnatbaum K, Reimer U, Wenschuh H, Mollenhauer M, Slotta-Huspenina J, Boese JH, Bantscheff M, Gerstmair A, Faerber F, Kuster B. Mass-spectrometry-based draft of the human proteome. Nature 2014; 509:582-7. [PMID: 24870543 DOI: 10.1038/nature13319] [Citation(s) in RCA: 1335] [Impact Index Per Article: 133.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2013] [Accepted: 04/11/2014] [Indexed: 12/15/2022]
Abstract
Proteomes are characterized by large protein-abundance differences, cell-type- and time-dependent expression patterns and post-translational modifications, all of which carry biological information that is not accessible by genomics or transcriptomics. Here we present a mass-spectrometry-based draft of the human proteome and a public, high-performance, in-memory database for real-time analysis of terabytes of big data, called ProteomicsDB. The information assembled from human tissues, cell lines and body fluids enabled estimation of the size of the protein-coding genome, and identified organ-specific proteins and a large number of translated lincRNAs (long intergenic non-coding RNAs). Analysis of messenger RNA and protein-expression profiles of human tissues revealed conserved control of protein abundance, and integration of drug-sensitivity data enabled the identification of proteins predicting resistance or sensitivity. The proteome profiles also hold considerable promise for analysing the composition and stoichiometry of protein complexes. ProteomicsDB thus enables navigation of proteomes, provides biological insight and fosters the development of proteomic technology.
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Affiliation(s)
- Mathias Wilhelm
- 1] Chair of Proteomics and Bioanalytics, Technische Universität München, Emil-Erlenmeyer Forum 5, 85354 Freising, Germany [2] SAP AG, Dietmar-Hopp-Allee 16, 69190 Walldorf, Germany [3]
| | - Judith Schlegl
- 1] SAP AG, Dietmar-Hopp-Allee 16, 69190 Walldorf, Germany [2]
| | - Hannes Hahne
- 1] Chair of Proteomics and Bioanalytics, Technische Universität München, Emil-Erlenmeyer Forum 5, 85354 Freising, Germany [2]
| | - Amin Moghaddas Gholami
- 1] Chair of Proteomics and Bioanalytics, Technische Universität München, Emil-Erlenmeyer Forum 5, 85354 Freising, Germany [2]
| | | | | | | | - Lars Butzmann
- SAP AG, Dietmar-Hopp-Allee 16, 69190 Walldorf, Germany
| | | | - Harald Marx
- Chair of Proteomics and Bioanalytics, Technische Universität München, Emil-Erlenmeyer Forum 5, 85354 Freising, Germany
| | - Toby Mathieson
- Cellzome GmbH, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Simone Lemeer
- Chair of Proteomics and Bioanalytics, Technische Universität München, Emil-Erlenmeyer Forum 5, 85354 Freising, Germany
| | | | - Ulf Reimer
- JPT Peptide Technologies GmbH, Volmerstraße 5, 12489 Berlin, Germany
| | - Holger Wenschuh
- JPT Peptide Technologies GmbH, Volmerstraße 5, 12489 Berlin, Germany
| | - Martin Mollenhauer
- Institute of Pathology, Technische Universität München, Trogerstraße 18, 81675 München, Germany
| | - Julia Slotta-Huspenina
- Institute of Pathology, Technische Universität München, Trogerstraße 18, 81675 München, Germany
| | | | | | | | - Franz Faerber
- SAP AG, Dietmar-Hopp-Allee 16, 69190 Walldorf, Germany
| | - Bernhard Kuster
- 1] Chair of Proteomics and Bioanalytics, Technische Universität München, Emil-Erlenmeyer Forum 5, 85354 Freising, Germany [2] Center for Integrated Protein Science Munich, Germany
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7
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Montague E, Stanberry L, Higdon R, Janko I, Lee E, Anderson N, Choiniere J, Stewart E, Yandl G, Broomall W, Kolker N, Kolker E. MOPED 2.5--an integrated multi-omics resource: multi-omics profiling expression database now includes transcriptomics data. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2014; 18:335-43. [PMID: 24910945 PMCID: PMC4048574 DOI: 10.1089/omi.2014.0061] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Multi-omics data-driven scientific discovery crucially rests on high-throughput technologies and data sharing. Currently, data are scattered across single omics repositories, stored in varying raw and processed formats, and are often accompanied by limited or no metadata. The Multi-Omics Profiling Expression Database (MOPED, http://moped.proteinspire.org ) version 2.5 is a freely accessible multi-omics expression database. Continual improvement and expansion of MOPED is driven by feedback from the Life Sciences Community. In order to meet the emergent need for an integrated multi-omics data resource, MOPED 2.5 now includes gene relative expression data in addition to protein absolute and relative expression data from over 250 large-scale experiments. To facilitate accurate integration of experiments and increase reproducibility, MOPED provides extensive metadata through the Data-Enabled Life Sciences Alliance (DELSA Global, http://delsaglobal.org ) metadata checklist. MOPED 2.5 has greatly increased the number of proteomics absolute and relative expression records to over 500,000, in addition to adding more than four million transcriptomics relative expression records. MOPED has an intuitive user interface with tabs for querying different types of omics expression data and new tools for data visualization. Summary information including expression data, pathway mappings, and direct connection between proteins and genes can be viewed on Protein and Gene Details pages. These connections in MOPED provide a context for multi-omics expression data exploration. Researchers are encouraged to submit omics data which will be consistently processed into expression summaries. MOPED as a multi-omics data resource is a pivotal public database, interdisciplinary knowledge resource, and platform for multi-omics understanding.
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Affiliation(s)
- Elizabeth Montague
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, Washington
- High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Larissa Stanberry
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, Washington
- High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Roger Higdon
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, Washington
- High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Imre Janko
- High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Elaine Lee
- High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Nathaniel Anderson
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, Washington
- High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - John Choiniere
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, Washington
- High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Elizabeth Stewart
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Gregory Yandl
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - William Broomall
- High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Natali Kolker
- High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
| | - Eugene Kolker
- Bioinformatics and High-Throughput Analysis Laboratory, Center for Developmental Therapeutics, Seattle Children's Research Institute, Seattle, Washington
- High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, Washington
- Predictive Analytics, Seattle Children's, Seattle, Washington
- Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, Washington
- Departments of Biomedical Informatics and Medical Education and Pediatrics, University of Washington, Seattle, Washington
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Higdon R, Stewart E, Stanberry L, Haynes W, Choiniere J, Montague E, Anderson N, Yandl G, Janko I, Broomall W, Fishilevich S, Lancet D, Kolker N, Kolker E. MOPED enables discoveries through consistently processed proteomics data. J Proteome Res 2013; 13:107-13. [PMID: 24350770 DOI: 10.1021/pr400884c] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
The Model Organism Protein Expression Database (MOPED, http://moped.proteinspire.org) is an expanding proteomics resource to enable biological and biomedical discoveries. MOPED aggregates simple, standardized and consistently processed summaries of protein expression and metadata from proteomics (mass spectrometry) experiments from human and model organisms (mouse, worm, and yeast). The latest version of MOPED adds new estimates of protein abundance and concentration as well as relative (differential) expression data. MOPED provides a new updated query interface that allows users to explore information by organism, tissue, localization, condition, experiment, or keyword. MOPED supports the Human Proteome Project's efforts to generate chromosome- and diseases-specific proteomes by providing links from proteins to chromosome and disease information as well as many complementary resources. MOPED supports a new omics metadata checklist to harmonize data integration, analysis, and use. MOPED's development is driven by the user community, which spans 90 countries and guides future development that will transform MOPED into a multiomics resource. MOPED encourages users to submit data in a simple format. They can use the metadata checklist to generate a data publication for this submission. As a result, MOPED will provide even greater insights into complex biological processes and systems and enable deeper and more comprehensive biological and biomedical discoveries.
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Higdon R, Haynes W, Stanberry L, Stewart E, Yandl G, Howard C, Broomall W, Kolker N, Kolker E. Unraveling the Complexities of Life Sciences Data. BIG DATA 2013; 1:42-50. [PMID: 27447037 DOI: 10.1089/big.2012.1505] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The life sciences have entered into the realm of big data and data-enabled science, where data can either empower or overwhelm. These data bring the challenges of the 5 Vs of big data: volume, veracity, velocity, variety, and value. Both independently and through our involvement with DELSA Global (Data-Enabled Life Sciences Alliance, DELSAglobal.org), the Kolker Lab ( kolkerlab.org ) is creating partnerships that identify data challenges and solve community needs. We specialize in solutions to complex biological data challenges, as exemplified by the community resource of MOPED (Model Organism Protein Expression Database, MOPED.proteinspire.org ) and the analysis pipeline of SPIRE (Systematic Protein Investigative Research Environment, PROTEINSPIRE.org ). Our collaborative work extends into the computationally intensive tasks of analysis and visualization of millions of protein sequences through innovative implementations of sequence alignment algorithms and creation of the Protein Sequence Universe tool (PSU). Pushing into the future together with our collaborators, our lab is pursuing integration of multi-omics data and exploration of biological pathways, as well as assigning function to proteins and porting solutions to the cloud. Big data have come to the life sciences; discovering the knowledge in the data will bring breakthroughs and benefits.
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Affiliation(s)
- Roger Higdon
- 1 Bioinformatics and High-throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 High-throughput Analysis Core, Center for Developmental Therapeutics, Seattle Children's Research Institute , Seattle, Washington
- 3 Predictive Analytics, Seattle Children's , Seattle, Washington
- 4 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Winston Haynes
- 1 Bioinformatics and High-throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 High-throughput Analysis Core, Center for Developmental Therapeutics, Seattle Children's Research Institute , Seattle, Washington
- 3 Predictive Analytics, Seattle Children's , Seattle, Washington
- 4 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Larissa Stanberry
- 1 Bioinformatics and High-throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 High-throughput Analysis Core, Center for Developmental Therapeutics, Seattle Children's Research Institute , Seattle, Washington
- 3 Predictive Analytics, Seattle Children's , Seattle, Washington
- 4 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Elizabeth Stewart
- 1 Bioinformatics and High-throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 4 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Gregory Yandl
- 1 Bioinformatics and High-throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 High-throughput Analysis Core, Center for Developmental Therapeutics, Seattle Children's Research Institute , Seattle, Washington
- 4 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Chris Howard
- 4 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 5 Center for Developmental Therapeutics, Seattle Children's Research Institute , Seattle, Washington
| | - William Broomall
- 2 High-throughput Analysis Core, Center for Developmental Therapeutics, Seattle Children's Research Institute , Seattle, Washington
- 3 Predictive Analytics, Seattle Children's , Seattle, Washington
- 4 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Natali Kolker
- 2 High-throughput Analysis Core, Center for Developmental Therapeutics, Seattle Children's Research Institute , Seattle, Washington
- 3 Predictive Analytics, Seattle Children's , Seattle, Washington
- 4 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
| | - Eugene Kolker
- 1 Bioinformatics and High-throughput Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington
- 2 High-throughput Analysis Core, Center for Developmental Therapeutics, Seattle Children's Research Institute , Seattle, Washington
- 3 Predictive Analytics, Seattle Children's , Seattle, Washington
- 4 Data-Enabled Life Sciences Alliance (DELSA Global) , Seattle, Washington
- 6 Departments of Biomedical Informatics & Medical Education and Pediatrics, University of Washington , Seattle, Washington
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10
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Kolker E, Higdon R, Haynes W, Welch D, Broomall W, Lancet D, Stanberry L, Kolker N. MOPED: Model Organism Protein Expression Database. Nucleic Acids Res 2012; 40:D1093-9. [PMID: 22139914 PMCID: PMC3245040 DOI: 10.1093/nar/gkr1177] [Citation(s) in RCA: 90] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2011] [Revised: 11/10/2011] [Accepted: 11/11/2011] [Indexed: 01/14/2023] Open
Abstract
Large numbers of mass spectrometry proteomics studies are being conducted to understand all types of biological processes. The size and complexity of proteomics data hinders efforts to easily share, integrate, query and compare the studies. The Model Organism Protein Expression Database (MOPED, htttp://moped.proteinspire.org) is a new and expanding proteomics resource that enables rapid browsing of protein expression information from publicly available studies on humans and model organisms. MOPED is designed to simplify the comparison and sharing of proteomics data for the greater research community. MOPED uniquely provides protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis. Data can be queried for specific proteins, browsed based on organism, tissue, localization and condition and sorted by false discovery rate and expression. MOPED empowers users to visualize their own expression data and compare it with existing studies. Further, MOPED links to various protein and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED contains over 43,000 proteins with at least one spectral match and more than 11 million high certainty spectra.
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Affiliation(s)
- Eugene Kolker
- Bioinformatics and High-throughput Analysis Laboratory, High-throughput Analysis Core, Center for Developmental Therapeutics, Seattle Children's Research Institute, Predicitive Analytics, Seattle Children's Hospital, Seattle, WA 98105, USA.
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11
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Kolker E, Higdon R, Morgan P, Sedensky M, Welch D, Bauman A, Stewart E, Haynes W, Broomall W, Kolker N. SPIRE: Systematic protein investigative research environment. J Proteomics 2011; 75:122-6. [PMID: 21609792 DOI: 10.1016/j.jprot.2011.05.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2011] [Revised: 05/03/2011] [Accepted: 05/05/2011] [Indexed: 12/21/2022]
Abstract
The SPIRE (Systematic Protein Investigative Research Environment) provides web-based experiment-specific mass spectrometry (MS) proteomics analysis (https://www.proteinspire.org). Its emphasis is on usability and integration of the best analytic tools. SPIRE provides an easy to use web-interface and generates results in both interactive and simple data formats. In contrast to run-based approaches, SPIRE conducts the analysis based on the experimental design. It employs novel methods to generate false discovery rates and local false discovery rates (FDR, LFDR) and integrates the best and complementary open-source search and data analysis methods. The SPIRE approach of integrating X!Tandem, OMSSA and SpectraST can produce an increase in protein IDs (52-88%) over current combinations of scoring and single search engines while also providing accurate multi-faceted error estimation. One of SPIRE's primary assets is combining the results with data on protein function, pathways and protein expression from model organisms. We demonstrate some of SPIRE's capabilities by analyzing mitochondrial proteins from the wild type and 3 mutants of C. elegans. SPIRE also connects results to publically available proteomics data through its Model Organism Protein Expression Database (MOPED). SPIRE can also provide analysis and annotation for user supplied protein ID and expression data.
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Affiliation(s)
- Eugene Kolker
- Bioinformatics & High-throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, WA, USA.
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