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Amanullah A, Upadhyay A, Joshi V, Mishra R, Jana NR, Mishra A. Progressing neurobiological strategies against proteostasis failure: Challenges in neurodegeneration. Prog Neurobiol 2017; 159:1-38. [DOI: 10.1016/j.pneurobio.2017.08.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2017] [Revised: 06/01/2017] [Accepted: 08/25/2017] [Indexed: 02/07/2023]
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Sunitha B, Gayathri N, Kumar M, Keshava Prasad TS, Nalini A, Padmanabhan B, Srinivas Bharath MM. Muscle biopsies from human muscle diseases with myopathic pathology reveal common alterations in mitochondrial function. J Neurochem 2016; 138:174-91. [PMID: 27015874 DOI: 10.1111/jnc.13626] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Revised: 03/16/2016] [Accepted: 03/20/2016] [Indexed: 01/17/2023]
Abstract
Muscle diseases are clinically and genetically heterogeneous and manifest as dystrophic, inflammatory and myopathic pathologies, among others. Our previous study on the cardiotoxin mouse model of myodegeneration and inflammation linked muscle pathology with mitochondrial damage and oxidative stress. In this study, we investigated whether human muscle diseases display mitochondrial changes. Muscle biopsies from muscle disease patients, represented by dysferlinopathy (dysfy) (dystrophic pathology; n = 43), polymyositis (PM) (inflammatory pathology; n = 24), and distal myopathy with rimmed vacuoles (DMRV) (distal myopathy; n = 31) were analyzed. Mitochondrial damage (ragged blue and COX-deficient fibers) was revealed in dysfy, PM, and DMRV cases by enzyme histochemistry (SDH and COX-SDH), electron microscopy (vacuolation and altered cristae) and biochemical assays (significantly increased ADP/ATP ratio). Proteomic analysis of muscle mitochondria from all three muscle diseases by isobaric tag for relative and absolute quantitation labeling and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis demonstrated down-regulation of electron transport chain (ETC) complex subunits, assembly factors and Krebs cycle enzymes. Interestingly, 80 of the under-expressed proteins were common among the three pathologies. Assay of ETC and Krebs cycle enzyme activities validated the MS data. Mitochondrial proteins from muscle pathologies also displayed higher tryptophan (Trp) oxidation and the same was corroborated in the cardiotoxin model. Molecular modeling predicted Trp oxidation to alter the local structure of mitochondrial proteins. Our data highlight mitochondrial alterations in muscle pathologies, represented by morphological changes, altered mitochondrial proteome and protein oxidation, thereby establishing the role of mitochondrial damage in human muscle diseases. We investigated whether human muscle diseases display mitochondrial changes. Muscle biopsies from dysferlinopathy (Dysfy), polymyositis (PM), and distal myopathy with rimmed vacuoles (DMRV) displayed morphological and biochemical evidences of mitochondrial dysfunction. Proteomic analysis revealed down-regulation of electron transport chain (ETC) subunits, assembly factors, and tricarboxylic acid (TCA) cycle enzymes, with 80 proteins common among the three pathologies. Mitochondrial proteins from muscle pathologies also displayed higher Trp oxidation that could alter the local structure. Cover image for this issue: doi: 10.1111/jnc.13324.
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Affiliation(s)
- Balaraju Sunitha
- Department of Neurochemistry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, Karnataka, India.,Department of Neuropathology, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, Karnataka, India
| | - Narayanappa Gayathri
- Department of Neuropathology, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, Karnataka, India
| | - Manish Kumar
- Institute of Bioinformatics, Whitefield, Bangalore, Karnataka, India
| | - Thottethodi Subrahmanya Keshava Prasad
- Institute of Bioinformatics, Whitefield, Bangalore, Karnataka, India.,NIMHANS-IOB Proteomics and Bioinformatics Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, Karnataka, India.,YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore, India
| | - Atchayaram Nalini
- Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, Karnataka, India
| | - Balasundaram Padmanabhan
- Department of Biophysics, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, Karnataka, India
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Wu Z, Pang W, Coghill GM. An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems. Soft comput 2015; 19:1595-1610. [PMID: 25999782 PMCID: PMC4433352 DOI: 10.1007/s00500-014-1467-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Computational modelling of biochemical systems based on top-down and bottom-up approaches has been well studied over the last decade. In this research, after illustrating how to generate atomic components by a set of given reactants and two user pre-defined component patterns, we propose an integrative top-down and bottom-up modelling approach for stepwise qualitative exploration of interactions among reactants in biochemical systems. Evolution strategy is applied to the top-down modelling approach to compose models, and simulated annealing is employed in the bottom-up modelling approach to explore potential interactions based on models constructed from the top-down modelling process. Both the top-down and bottom-up approaches support stepwise modular addition or subtraction for the model evolution. Experimental results indicate that our modelling approach is feasible to learn the relationships among biochemical reactants qualitatively. In addition, hidden reactants of the target biochemical system can be obtained by generating complex reactants in corresponding composed models. Moreover, qualitatively learned models with inferred reactants and alternative topologies can be used for further web-lab experimental investigations by biologists of interest, which may result in a better understanding of the system.
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Affiliation(s)
- Zujian Wu
- College of Information Science and Technology, Jinan University, Guangzhou, 510632 People’s Republic of China
- School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, AB24 3UE Scotland, UK
| | - Wei Pang
- School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, AB24 3UE Scotland, UK
| | - George M. Coghill
- School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, AB24 3UE Scotland, UK
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4
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Abstract
Due to their versatility, quadrupole ion traps have become popular mass spectrometers in the growing field of proteomics. High sensitivity, user friendliness and low cost are the key features that have contributed to the success of the technology. However, mass measurement accuracy, resolution and mass range are still not comparable to the analytical performances obtained on other mass spectrometers. In the past 5 years, researchers have tried to overcome these drawbacks, focusing their attention on two different aspects of ion-trap mass spectrometry, development of novel types of ion traps and manipulation of the gas-phase ion chemistry, in order to obtain alternative techniques for tandem mass spectrometry analysis. In the field of trapping devices, improvements in instrumental design have led to the linear ion trap, digital ion trap and orbitrap. Activation methods based on electrons, chemically produced by an anion or from irradiation with an electron beam, have demonstrated their utility in providing complementary sequence information for improving confidence in protein identification.
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5
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Kuczenski RS, Aggarwal K, Lee KH. Improved understanding of gene expression regulation using systems biology. Expert Rev Proteomics 2014; 2:915-24. [PMID: 16307520 DOI: 10.1586/14789450.2.6.915] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This article reviews the current state of systems biology approaches, including the experimental tools used to generate 'omic' data and computational frameworks to interpret this data. Through illustrative examples, systems biology approaches to understand gene expression and gene expression regulation are discussed. Some of the challenges facing this field and the future opportunities in the systems biology era are highlighted.
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Affiliation(s)
- Robert S Kuczenski
- Cornell University, School of Chemical & Biomolecular Engineering, 120 Olin Hall, Ithaca, NY 14853, USA.
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Giordano M, Natale M, Cornaz M, Ruffino A, Bonino D, Bucci EM. iMole, a web based image retrieval system from biomedical literature. Electrophoresis 2013; 34:1965-8. [PMID: 23592298 DOI: 10.1002/elps.201300085] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Revised: 03/15/2013] [Accepted: 03/30/2013] [Indexed: 11/08/2022]
Abstract
iMole is a platform that automatically extracts images and captions from biomedical literature. Images are tagged with terms contained in figure captions by means of a sophisticate text-mining tool. Moreover, iMole allows the user to upload directly their own images within the database and manually tag images by curated dictionary. Using iMole the researchers can develop a proper biomedical image database, storing the images extracted from paper of interest, image found on the web repositories, and their own experimental images. In order to show the functioning of the platform, we used iMole to build a 2DE database. Briefly, tagged 2DE gel images were collected and stored in a searchable 2DE gel database, available to users through an interactive web interface. Images were obtained by automatically parsing 16,608 proteomic publications, which yielded more than 16,500 images. The database can be further expanded by users with images of interest trough a manual uploading process. iMole is available with a preloaded set of 2DE gel data at http://imole.biodigitalvalley.com.
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7
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Medina-Aunon JA, Krishna R, Ghali F, Albar JP, Jones AJ. A guide for integration of proteomic data standards into laboratory workflows. Proteomics 2013; 13:480-92. [DOI: 10.1002/pmic.201200268] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2012] [Revised: 08/14/2012] [Accepted: 09/10/2012] [Indexed: 01/28/2023]
Affiliation(s)
| | - Ritesh Krishna
- Institute of Integrative Biology; University of Liverpool; Liverpool; UK
| | - Fawaz Ghali
- Institute of Integrative Biology; University of Liverpool; Liverpool; UK
| | - Juan P. Albar
- Centro Nacional de Biotecnología; CSIC; Madrid; Spain
| | - Andrew J. Jones
- Institute of Integrative Biology; University of Liverpool; Liverpool; UK
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Jones AR, Eisenacher M, Mayer G, Kohlbacher O, Siepen J, Hubbard SJ, Selley JN, Searle BC, Shofstahl J, Seymour SL, Julian R, Binz PA, Deutsch EW, Hermjakob H, Reisinger F, Griss J, Vizcaíno JA, Chambers M, Pizarro A, Creasy D. The mzIdentML data standard for mass spectrometry-based proteomics results. Mol Cell Proteomics 2012. [PMID: 22375074 DOI: 10.1074/mcp.m111.014381 [epub ahead of print]] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2023] Open
Abstract
We report the release of mzIdentML, an exchange standard for peptide and protein identification data, designed by the Proteomics Standards Initiative. The format was developed by the Proteomics Standards Initiative in collaboration with instrument and software vendors, and the developers of the major open-source projects in proteomics. Software implementations have been developed to enable conversion from most popular proprietary and open-source formats, and mzIdentML will soon be supported by the major public repositories. These developments enable proteomics scientists to start working with the standard for exchanging and publishing data sets in support of publications and they provide a stable platform for bioinformatics groups and commercial software vendors to work with a single file format for identification data.
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Affiliation(s)
- Andrew R Jones
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZJ, UK.
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9
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Jones AR, Eisenacher M, Mayer G, Kohlbacher O, Siepen J, Hubbard SJ, Selley JN, Searle BC, Shofstahl J, Seymour SL, Julian R, Binz PA, Deutsch EW, Hermjakob H, Reisinger F, Griss J, Vizcaíno JA, Chambers M, Pizarro A, Creasy D. The mzIdentML data standard for mass spectrometry-based proteomics results. Mol Cell Proteomics 2012; 11:M111.014381. [PMID: 22375074 PMCID: PMC3394945 DOI: 10.1074/mcp.m111.014381] [Citation(s) in RCA: 148] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2011] [Revised: 12/20/2011] [Indexed: 11/06/2022] Open
Abstract
We report the release of mzIdentML, an exchange standard for peptide and protein identification data, designed by the Proteomics Standards Initiative. The format was developed by the Proteomics Standards Initiative in collaboration with instrument and software vendors, and the developers of the major open-source projects in proteomics. Software implementations have been developed to enable conversion from most popular proprietary and open-source formats, and mzIdentML will soon be supported by the major public repositories. These developments enable proteomics scientists to start working with the standard for exchanging and publishing data sets in support of publications and they provide a stable platform for bioinformatics groups and commercial software vendors to work with a single file format for identification data.
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Affiliation(s)
- Andrew R Jones
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZJ, UK.
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Lista S, Faltraco F, Hampel H. Biological and methodical challenges of blood-based proteomics in the field of neurological research. Prog Neurobiol 2012; 101-102:18-34. [PMID: 22743551 DOI: 10.1016/j.pneurobio.2012.06.006] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2011] [Revised: 03/28/2012] [Accepted: 06/18/2012] [Indexed: 12/17/2022]
Abstract
Biomarker discovery is an application of major importance in today's proteomic research. There is an urgent need for suitable biomarkers to improve diagnostic tools and treatment in various neurological diseases, such as neurodegenerative disorders. Recent years have witnessed an enormous interest in proteomics, which is currently seen as an invaluable tool to shed more light on complex interacting signalling pathways and molecular networks involved in several neuropathological conditions. However, while first results of proteomic research studies have sparked much public attention, the momentum of further proteomic biomarker research in neurological disorders may suffer by its very complex methodology which is sensitive to various sources of artefacts. A major source of variability is proteome perturbation caused by sample handling/preservation (preanalytical phase) and processing/measurement (analytical phase). The aim of the present review is to summarize the current literature focusing on the crucial role played by preanalytical and analytical factors that affect the quality of samples and the reliability of the data produced in blood-based proteomic biomarker research in neurology, which may apply to Alzheimer's disease (AD) as well as other neurological disorders. Procedures for sample preparation and protocols for the analysis of serum and plasma samples will be delineated. Finally, the potential usefulness of bioinformatics--allowing for the assembly, store, and processing of data--as well as its contribution to the execution of proteomic studies will be critically discussed.
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Affiliation(s)
- Simone Lista
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe-University of Frankfurt, Frankfurt am Main, Germany.
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Schaab C, Geiger T, Stoehr G, Cox J, Mann M. Analysis of high accuracy, quantitative proteomics data in the MaxQB database. Mol Cell Proteomics 2012; 11:M111.014068. [PMID: 22301388 PMCID: PMC3316731 DOI: 10.1074/mcp.m111.014068] [Citation(s) in RCA: 155] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment spectra, is accessible to the community via a user-friendly web interface at http://www.biochem.mpg.de/maxqb.
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Affiliation(s)
- Christoph Schaab
- Department of Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, D-82152 Martinsried, Germany
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12
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Lindsey ML, Weintraub ST, Lange RA. Using extracellular matrix proteomics to understand left ventricular remodeling. CIRCULATION. CARDIOVASCULAR GENETICS 2012; 5:o1-7. [PMID: 22337931 PMCID: PMC3282021 DOI: 10.1161/circgenetics.110.957803] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Survival following myocardial infarction (MI) has improved substantially over the last 40 years; however, the incidence of subsequent congestive heart failure has dramatically increased as a consequence. Discovering plasma markers that signify adverse cardiac remodeling may allow high-risk patients to be recognized earlier and may provide an improved way to assess treatment efficacy. Alterations in extracellular matrix (ECM) regulate cardiac remodeling following MI and potentially provide a large array of candidate indicators. The field of cardiac proteomics has progressed rapidly over the past 20 years, since publication of the first two-dimensional electrophoretic gels of left ventricle proteins. Proteomic approaches are now routinely utilized to better understand how the left ventricle responds to injury. In this review, we will discuss how methods have developed to allow comprehensive evaluation of the ECM proteome. We will explain how ECM proteomic data can be used to predict adverse remodeling for an individual patient and highlight future directions. Although this review will focus on the use of ECM proteomics to better understand post-MI remodeling responses, these approaches have applicability to a wide-range of cardiac pathologies, including pressure overload hypertrophy, viral myocarditis, and non-ischemic heart failure.
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Affiliation(s)
- Merry L Lindsey
- Department of Medicine and Department of Biochemistry, University of Texas Health Science Center at San Antonio, TX, USA.
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13
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SILEC: a protocol for generating and using isotopically labeled coenzyme A mass spectrometry standards. Nat Protoc 2011; 7:1-12. [PMID: 22157971 DOI: 10.1038/nprot.2011.421] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Stable isotope labeling by essential nutrients in cell culture (SILEC) was recently developed to generate isotopically labeled coenzyme A (CoA) and short-chain acyl-CoA thioesters. This was accomplished by modifying the widely used technique of stable isotope labeling by amino acids in cell culture to include [(13)C(3)(15)N]-pantothenate (vitamin B(5)), a CoA precursor, instead of the isotopically labeled amino acids. The lack of a de novo pantothenate synthesis pathway allowed for efficient and near-complete labeling of the measured CoA species. This protocol provides a step-by-step approach for generating stable isotope-labeled short-chain acyl-CoA internal standards in mammalian and insect cells as well as instructions on how to use them in stable isotope dilution mass spectrometric-based analyses. Troubleshooting guidelines, as well as a list of unlabeled and labeled CoA species, are also included. This protocol represents a prototype for generating stable isotope internal standards from labeled essential nutrients such as pantothenate. The generation and use of SILEC standards takes approximately 2-3 weeks.
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Sahoo SS, Nguyen V, Bodenreider O, Parikh P, Minning T, Sheth AP. A unified framework for managing provenance information in translational research. BMC Bioinformatics 2011; 12:461. [PMID: 22126369 PMCID: PMC3298549 DOI: 10.1186/1471-2105-12-461] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2011] [Accepted: 11/29/2011] [Indexed: 01/21/2023] Open
Abstract
Background A critical aspect of the NIH Translational Research roadmap, which seeks to accelerate the delivery of "bench-side" discoveries to patient's "bedside," is the management of the provenance metadata that keeps track of the origin and history of data resources as they traverse the path from the bench to the bedside and back. A comprehensive provenance framework is essential for researchers to verify the quality of data, reproduce scientific results published in peer-reviewed literature, validate scientific process, and associate trust value with data and results. Traditional approaches to provenance management have focused on only partial sections of the translational research life cycle and they do not incorporate "domain semantics", which is essential to support domain-specific querying and analysis by scientists. Results We identify a common set of challenges in managing provenance information across the pre-publication and post-publication phases of data in the translational research lifecycle. We define the semantic provenance framework (SPF), underpinned by the Provenir upper-level provenance ontology, to address these challenges in the four stages of provenance metadata: (a) Provenance collection - during data generation (b) Provenance representation - to support interoperability, reasoning, and incorporate domain semantics (c) Provenance storage and propagation - to allow efficient storage and seamless propagation of provenance as the data is transferred across applications (d) Provenance query - to support queries with increasing complexity over large data size and also support knowledge discovery applications We apply the SPF to two exemplar translational research projects, namely the Semantic Problem Solving Environment for Trypanosoma cruzi (T.cruzi SPSE) and the Biomedical Knowledge Repository (BKR) project, to demonstrate its effectiveness. Conclusions The SPF provides a unified framework to effectively manage provenance of translational research data during pre and post-publication phases. This framework is underpinned by an upper-level provenance ontology called Provenir that is extended to create domain-specific provenance ontologies to facilitate provenance interoperability, seamless propagation of provenance, automated querying, and analysis.
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Affiliation(s)
- Satya S Sahoo
- Department of Computer Science and Engineering, Wright State University, Dayton, OH, USA.
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Orchard S, Kersey P, Zhu W, Montecchi-Palazzi L, Hermjakob H, Apweiler R. Progress in Establishing Common Standards for Exchanging Proteomics Data: The Second Meeting of the HUPO Proteomics Standards Initiative. Comp Funct Genomics 2011; 4:203-6. [PMID: 18629121 PMCID: PMC2447408 DOI: 10.1002/cfg.279] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2003] [Revised: 02/06/2003] [Accepted: 02/06/2003] [Indexed: 11/11/2022] Open
Abstract
The Proteomics Standards Initiative (PSI) aims to define community standards for data representation in proteomics and to facilitate data comparison, exchange and
verification. Rapid progress has been made in the development of common standards
for data exchange in the fields of both mass spectrometry and protein–protein interactions
since the first PSI meeting [1]. Both hardware and software manufacturers
have agreed to work to ensure that a proteomics-specific extension is created for the
emerging ASTM mass spectrometry standard and the data model for a proteomics
experiment has advanced significantly. The Protein–Protein Interactions (PPI) group
expects to publish the Level 1 PSI data exchange format for protein–protein interactions
by early summer this year, and discussion as to the additional content of Level
2 has been initiated.
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Affiliation(s)
- Sandra Orchard
- EMBL Outstation European Bioinformatics Institute Wellcome Trust Genome Campus Hinxton, Cambridge UK
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Wasbrough ER, Dorus S, Hester S, Howard-Murkin J, Lilley K, Wilkin E, Polpitiya A, Petritis K, Karr TL. The Drosophila melanogaster sperm proteome-II (DmSP-II). J Proteomics 2010; 73:2171-85. [DOI: 10.1016/j.jprot.2010.09.002] [Citation(s) in RCA: 124] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2010] [Revised: 09/07/2010] [Accepted: 09/07/2010] [Indexed: 01/07/2023]
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18
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Gibson F, Hoogland C, Martinez-Bartolomé S, Medina-Aunon JA, Albar JP, Babnigg G, Wipat A, Hermjakob H, Almeida JS, Stanislaus R, Paton NW, Jones AR. The gel electrophoresis markup language (GelML) from the Proteomics Standards Initiative. Proteomics 2010; 10:3073-81. [PMID: 20677327 PMCID: PMC3193076 DOI: 10.1002/pmic.201000120] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2010] [Accepted: 06/09/2010] [Indexed: 11/11/2022]
Abstract
The Human Proteome Organisation's Proteomics Standards Initiative has developed the GelML (gel electrophoresis markup language) data exchange format for representing gel electrophoresis experiments performed in proteomics investigations. The format closely follows the reporting guidelines for gel electrophoresis, which are part of the Minimum Information About a Proteomics Experiment (MIAPE) set of modules. GelML supports the capture of metadata (such as experimental protocols) and data (such as gel images) resulting from gel electrophoresis so that laboratories can be compliant with the MIAPE Gel Electrophoresis guidelines, while allowing such data sets to be exchanged or downloaded from public repositories. The format is sufficiently flexible to capture data from a broad range of experimental processes, and complements other PSI formats for MS data and the results of protein and peptide identifications to capture entire gel-based proteome workflows. GelML has resulted from the open standardisation process of PSI consisting of both public consultation and anonymous review of the specifications.
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Affiliation(s)
- Frank Gibson
- School of Computing Science, Newcastle University, Newcastle, UK
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Yan Q. Bioinformatics for transporter pharmacogenomics and systems biology: data integration and modeling with UML. Methods Mol Biol 2010; 637:23-45. [PMID: 20419428 DOI: 10.1007/978-1-60761-700-6_2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Bioinformatics is the rational study at an abstract level that can influence the way we understand biomedical facts and the way we apply the biomedical knowledge. Bioinformatics is facing challenges in helping with finding the relationships between genetic structures and functions, analyzing genotype-phenotype associations, and understanding gene-environment interactions at the systems level. One of the most important issues in bioinformatics is data integration. The data integration methods introduced here can be used to organize and integrate both public and in-house data. With the volume of data and the high complexity, computational decision support is essential for integrative transporter studies in pharmacogenomics, nutrigenomics, epigenetics, and systems biology. For the development of such a decision support system, object-oriented (OO) models can be constructed using the Unified Modeling Language (UML). A methodology is developed to build biomedical models at different system levels and construct corresponding UML diagrams, including use case diagrams, class diagrams, and sequence diagrams. By OO modeling using UML, the problems of transporter pharmacogenomics and systems biology can be approached from different angles with a more complete view, which may greatly enhance the efforts in effective drug discovery and development. Bioinformatics resources of membrane transporters and general bioinformatics databases and tools that are frequently used in transporter studies are also collected here. An informatics decision support system based on the models presented here is available at http://www.pharmtao.com/transporter . The methodology developed here can also be used for other biomedical fields.
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Beynon RJ. Enabling proteomics: the need for an extendable 'workbench' for user-configurable solutions. Comp Funct Genomics 2010; 5:52-5. [PMID: 18629039 PMCID: PMC2447322 DOI: 10.1002/cfg.372] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2003] [Revised: 11/18/2003] [Accepted: 11/26/2003] [Indexed: 11/08/2022] Open
Abstract
Proteomics has the capability to generate overwhelming quantities of data in relatively short timescales, and it is not uncommon to see experimenters investing substantially more time in data analysis than in data gathering. Although several sophisticated tools for data reduction and analysis are available, they lack the flexibility to cope with increasingly innovative experimental strategies and new database resources that encode both qualitative and quantitative data. I will outline a specification of a flexible proteomics tool that could address many current bottlenecks and deficiencies.
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Affiliation(s)
- Robert J Beynon
- Department of Veterinary Preclinical Sciences, University of Liverpool, Crown Street, Liverpool L69 7ZJ, UK.
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21
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Jones A, Wastling J, Hunt E. Proposal for a standard representation of two-dimensional gel electrophoresis data. Comp Funct Genomics 2010; 4:492-501. [PMID: 18628860 PMCID: PMC2447283 DOI: 10.1002/cfg.323] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The global analysis of proteins is now feasible due to improvements in techniques such as two-dimensional gel electrophoresis (2-DE), mass spectrometry, yeast two-hybrid
systems and the development of bioinformatics applications. The experiments form
the basis of proteomics, and present significant challenges in data analysis, storage and
querying. We argue that a standard format for proteome data is required to enable
the storage, exchange and subsequent re-analysis of large datasets. We describe the
criteria that must be met for the development of a standard for proteomics. We have
developed a model to represent data from 2-DE experiments, including difference
gel electrophoresis along with image analysis and statistical analysis across multiple
gels. This part of proteomics analysis is not represented in current proposals for
proteomics standards. We are working with the Proteomics Standards Initiative to
develop a model encompassing biological sample origin, experimental protocols, a
number of separation techniques and mass spectrometry. The standard format will
facilitate the development of central repositories of data, enabling results to be verified
or re-analysed, and the correlation of results produced by different research groups
using a variety of laboratory techniques.
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Affiliation(s)
- Andrew Jones
- Department of Computing Science, University of Glasgow, 17 Lilybank Gardens, Glasgow G12 8QQ, UK.
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22
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Oh E, Hasan MN, Jamshed M, Park SH, Hong HM, Song EJ, Yoo YS. Growing trend of CE at the omics level: The frontier of systems biology. Electrophoresis 2010; 31:74-92. [DOI: 10.1002/elps.200900410] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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23
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Hsu HY, Joos TO, Koga H. Multiplex microsphere-based flow cytometric platforms for protein analysis and their application in clinical proteomics â from assays to results. Electrophoresis 2009; 30:4008-19. [DOI: 10.1002/elps.200900211] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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24
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Farag MA, Deavours BE, de Fátima Â, Naoumkina M, Dixon RA, Sumner LW. Integrated metabolite and transcript profiling identify a biosynthetic mechanism for hispidol in Medicago truncatula cell cultures. PLANT PHYSIOLOGY 2009; 151:1096-113. [PMID: 19571306 PMCID: PMC2773099 DOI: 10.1104/pp.109.141481] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2009] [Accepted: 06/29/2009] [Indexed: 05/08/2023]
Abstract
Metabolic profiling of elicited barrel medic (Medicago truncatula) cell cultures using high-performance liquid chromatography coupled to photodiode and mass spectrometry detection revealed the accumulation of the aurone hispidol (6-hydroxy-2-[(4-hydroxyphenyl)methylidene]-1-benzofuran-3-one) as a major response to yeast elicitor. Parallel, large-scale transcriptome profiling indicated that three peroxidases, MtPRX1, MtPRX2, and MtPRX3, were coordinately induced with the accumulation of hispidol. MtPRX1 and MtPRX2 exhibited aurone synthase activity based upon in vitro substrate specificity and product profiles of recombinant proteins expressed in Escherichia coli. Hispidol possessed significant antifungal activity relative to other M. truncatula phenylpropanoids tested but has not been reported in this species before and was not found in differentiated roots in which high levels of the peroxidase transcripts accumulated. We propose that hispidol is formed in cell cultures by metabolic spillover when the pool of its precursor, isoliquiritigenin, builds up as a result of an imbalance between the upstream and downstream segments of the phenylpropanoid pathway, reflecting the plasticity of plant secondary metabolism. The results illustrate that integration of metabolomics and transcriptomics in genetically reprogrammed plant cell cultures is a powerful approach for the discovery of novel bioactive secondary metabolites and the mechanisms underlying their generation.
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Affiliation(s)
| | | | | | | | | | - Lloyd W. Sumner
- Plant Biology Division, Samuel Roberts Noble Foundation, Ardmore, Oklahoma 73401 (M.A.F., B.E.D., M.N., R.A.D., L.W.S.); Pharmacognosy Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt 11562 (M.A.F.); Department of Biology, Colorado State University, Fort Collins, Colorado 80523 (B.E.D.); and Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Campus Pampulha, Belo Horizonte, Minas Gerais, 31270–901, Brazil (Â.d.F.)
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25
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Song YS, Lee HW, Park YR, Kim DK, Sim J, Kang HP, Kim JH. TMA-TAB: a spreadsheet-based document for exchange of tissue microarray data based on the tissue microarray-object model. J Biomed Inform 2009; 43:435-41. [PMID: 19835983 DOI: 10.1016/j.jbi.2009.10.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2009] [Revised: 10/05/2009] [Accepted: 10/07/2009] [Indexed: 10/20/2022]
Abstract
The importance of tissue microarrays (TMA) as clinical validation tools for cDNA microarray results is increasing, whereas researchers are still suffering from TMA data management issues. After we developed a comprehensive data model for TMA data storage, exchange and analysis, TMA-OM, we focused our attention on the development of a user-friendly exchange format with high expressivity in order to promote data communication of TMA results and TMA-OM supportive database applications. We developed TMA-TAB, a spreadsheet-based data format for TMA data submission to the TMA-OM supportive TMA database system. TMA-TAB was developed by simplifying, modifying and reorganizing classes, attributes and templates of TMA-OM into five entities: experiment, block, slide, core_in_block, and core_in_slide. Five tab-delimited formats (investigation design format, block description format, slide description format, core clinicohistopathological data format, and core result data format) were made, each representing the entities of experiment, block, slide, core_in_block, and core_in_slide. We implemented TMA-TAB import and export modules on Xperanto-TMA, a TMA-OM supportive database application, to facilitate data submission. Development and implementation of TMA-TAB and TMA-OM provide a strong infrastructure for powerful and user-friendly TMA data management.
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Affiliation(s)
- Young Soo Song
- Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, Republic of Korea
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26
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Jones AR, Lister AL, Hermida L, Wilkinson P, Eisenacher M, Belhajjame K, Gibson F, Lord P, Pocock M, Rosenfelder H, Santoyo-Lopez J, Wipat A, Paton NW. Modeling and managing experimental data using FuGE. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2009; 13:239-51. [PMID: 19441879 DOI: 10.1089/omi.2008.0080] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
The Functional Genomics Experiment data model (FuGE) has been developed to increase the consistency and efficiency of experimental data modeling in the life sciences, and it has been adopted by a number of high-profile standardization organizations. FuGE can be used: (1) directly, whereby generic modeling constructs are used to represent concepts from specific experimental activities; or (2) as a framework within which method-specific models can be developed. FuGE is both rich and flexible, providing a considerable number of modeling constructs, which can be used in a range of different ways. However, such richness and flexibility also mean that modelers and application developers have choices to make when applying FuGE in a given context. This paper captures emerging best practice in the use of FuGE in the light of the experience of several groups by: (1) proposing guidelines for the use and extension of the FuGE data model; (2) presenting design patterns that reflect recurring requirements in experimental data modeling; and (3) describing a community software tool kit (STK) that supports application development using FuGE. We anticipate that these guidelines will encourage consistent usage of FuGE, and as such, will contribute to the development of convergent data standards in omics research.
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Affiliation(s)
- Andrew R Jones
- Department of Preclinical Veterinary Science, Faculty of Veterinary Science, University of Liverpool, Liverpool, United Kingdom.
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Embury SM, Missier P, Sampaio S, Greenwood RM, Preece AD. Incorporating Domain-Specific Information Quality Constraints into Database Queries. ACM JOURNAL OF DATA AND INFORMATION QUALITY 2009. [DOI: 10.1145/1577840.1577846] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The range of information now available in queryable repositories opens up a host of possibilities for new and valuable forms of data analysis. Database query languages such as SQL and XQuery offer a concise and high-level means by which such analyses can be implemented, facilitating the extraction of relevant data subsets into either generic or bespoke data analysis environments. Unfortunately, the quality of data in these repositories is often highly variable. The data is still useful, but only if the consumer is aware of the data quality problems and can work around them. Standard query languages offer little support for this aspect of data management. In principle, however, it should be possible to embed constraints describing the consumer’s data quality requirements into the query directly, so that the query evaluator can take over responsibility for enforcing them during query processing.
Most previous attempts to incorporate information quality constraints into database queries have been based around a small number of highly generic quality measures, which are defined and computed by the information provider. This is a useful approach in some application areas but, in practice, quality criteria are more commonly determined by the user of the information not by the provider. In this article, we explore an approach to incorporating quality constraints into database queries where the definition of quality is set by the user and not the provider of the information. Our approach is based around the concept of a
quality view
, a configurable quality assessment component into which domain-specific notions of quality can be embedded. We examine how quality views can be incorporated into XQuery, and draw from this the language features that are required in general to embed quality views into any query language. We also propose some syntactic sugar on top of XQuery to simplify the process of querying with quality constraints.
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28
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Abstract
Toxoplasma gondii is a ubiquitous, Apicomplexan parasite that, in humans, can cause several clinical syndromes, including encephalitis, chorioretinitis and congenital infection. T. gondii was described a little over 100 years ago in the tissues of the gundi (Ctenodoactylus gundi). There are a large number of applicable experimental techniques available for this pathogen and it has become a model organism for the study of intracellular pathogens. With the completion of the genomes for a type I (GT-1), type II (ME49) and type III (VEG) strains, proteomic studies on this organism have been greatly facilitated. Several subcellular proteomic studies have been completed on this pathogen. These studies have helped elucidate specialized invasion organelles and their composition, as well as proteins associated with the cytoskeleton. Global proteomic studies are leading to improved strategies for genome annotation in this organism and an improved understanding of protein regulation in this pathogen. Web-based resources, such as EPIC-DB and ToxoDB, provide proteomic data and support for studies on T. gondii. This review will summarize the current status of proteomic research on T. gondii.
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Affiliation(s)
- Louis M Weiss
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Forchheimer 504, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
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29
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Mead JA, Bianco L, Bessant C. Recent developments in public proteomic MS repositories and pipelines. Proteomics 2009; 9:861-81. [PMID: 19212957 DOI: 10.1002/pmic.200800553] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This article provides an overview of publicly available proteomic data repositories in a single document with a particular focus on the latest developments, many of which are not announced through traditional publications. The review is intended to inform the proteomics practitioner of the options for storage and dissemination of their MS/MS data in the public domain, and to help those who want to mine proteomic data generated by others. The latter area has arguably seen the most development in recent times, as repositories have sprouted new tools for data analysis, visualisation and experimental design. We also highlight key biological datasets available at each repository, including standard datasets. Finally, we touch upon areas of significant challenge and future directions.
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30
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Zheng G, Li H, Wang C, Sheng Q, Fan H, Yang S, Liu B, Dai J, Zeng R, Xie L. A platform to standardize, store, and visualize proteomics experimental data. Acta Biochim Biophys Sin (Shanghai) 2009; 41:273-9. [PMID: 19352541 DOI: 10.1093/abbs/gmp010] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
With the development of functional genomics research, large-scale proteomics studies are now widespread, presenting significant challenges for data storage, exchange, and analysis. Here we present the Integrated Proteomics Exploring Database (IPED) as a platform for managing proteomics experimental data (both process and result data). IPED is based on the schema of the Proteome Experimental Data Repository (PEDRo), and complies with the General Proteomics Standard (GPS) drafted by the Proteomics Standards Committee of the Human Proteome Organization. In our work, we developed three components for the IPED platform: the IPED client editor, IPED server software, and IPED web interface. The client editor collects experimental data and generates an extensible markup language (XML) data file compliant with PEDRo and GPS; the server software parses the XML data file and loads information into a core database; and the web interface displays experimental results, to provide a convenient graphic representation of data. Given software convenience and data abundance, IPED is a powerful platform for data exchange and presents an important resource for the proteomics community. In its current release, IPED is available at http://www.biosino.org/iped2.
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Affiliation(s)
- Guangyong Zheng
- Bioinformation Center, Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
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31
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Nebrich G, Herrmann M, Hartl D, Diedrich M, Kreitler T, Wierling C, Klose J, Giavalisco P, Zabel C, Mao L. PROTEOMER: A workflow-optimized laboratory information management system for 2-D electrophoresis-centered proteomics. Proteomics 2009; 9:1795-808. [DOI: 10.1002/pmic.200800522] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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32
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Schmidt F, Schmid M, Thiede B, Pleissner KP, Böhme M, Jungblut PR. Assembling proteomics data as a prerequisite for the analysis of large scale experiments. Chem Cent J 2009; 3:2. [PMID: 19166578 PMCID: PMC2653022 DOI: 10.1186/1752-153x-3-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2008] [Accepted: 01/23/2009] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Despite the complete determination of the genome sequence of a huge number of bacteria, their proteomes remain relatively poorly defined. Beside new methods to increase the number of identified proteins new database applications are necessary to store and present results of large- scale proteomics experiments. RESULTS In the present study, a database concept has been developed to address these issues and to offer complete information via a web interface. In our concept, the Oracle based data repository system SQL-LIMS plays the central role in the proteomics workflow and was applied to the proteomes of Mycobacterium tuberculosis, Helicobacter pylori, Salmonella typhimurium and protein complexes such as 20S proteasome. Technical operations of our proteomics labs were used as the standard for SQL-LIMS template creation. By means of a Java based data parser, post-processed data of different approaches, such as LC/ESI-MS, MALDI-MS and 2-D gel electrophoresis (2-DE), were stored in SQL-LIMS. A minimum set of the proteomics data were transferred in our public 2D-PAGE database using a Java based interface (Data Transfer Tool) with the requirements of the PEDRo standardization. Furthermore, the stored proteomics data were extractable out of SQL-LIMS via XML. CONCLUSION The Oracle based data repository system SQL-LIMS played the central role in the proteomics workflow concept. Technical operations of our proteomics labs were used as standards for SQL-LIMS templates. Using a Java based parser, post-processed data of different approaches such as LC/ESI-MS, MALDI-MS and 1-DE and 2-DE were stored in SQL-LIMS. Thus, unique data formats of different instruments were unified and stored in SQL-LIMS tables. Moreover, a unique submission identifier allowed fast access to all experimental data. This was the main advantage compared to multi software solutions, especially if personnel fluctuations are high. Moreover, large scale and high-throughput experiments must be managed in a comprehensive repository system such as SQL-LIMS, to query results in a systematic manner. On the other hand, these database systems are expensive and require at least one full time administrator and specialized lab manager. Moreover, the high technical dynamics in proteomics may cause problems to adjust new data formats. To summarize, SQL-LIMS met the requirements of proteomics data handling especially in skilled processes such as gel-electrophoresis or mass spectrometry and fulfilled the PSI standardization criteria. The data transfer into a public domain via DTT facilitated validation of proteomics data. Additionally, evaluation of mass spectra by post-processing using MS-Screener improved the reliability of mass analysis and prevented storage of data junk.
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Affiliation(s)
- Frank Schmidt
- Max Planck Institute for Infection Biology, Core Facility Protein Analysis, Berlin, Germany.
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Benton HP, Wong DM, Trauger SA, Siuzdak G. XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization. Anal Chem 2008; 80:6382-9. [PMID: 18627180 DOI: 10.1021/ac800795f] [Citation(s) in RCA: 185] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Mass spectrometry based metabolomics represents a new area for bioinformatics technology development. While the computational tools currently available such as XCMS statistically assess and rank LC-MS features, they do not provide information about their structural identity. XCMS(2) is an open source software package which has been developed to automatically search tandem mass spectrometry (MS/MS) data against high quality experimental MS/MS data from known metabolites contained in a reference library (METLIN). Scoring of hits is based on a "shared peak count" method that identifies masses of fragment ions shared between the analytical and reference MS/MS spectra. Another functional component of XCMS(2) is the capability of providing structural information for unknown metabolites, which are not in the METLIN database. This "similarity search" algorithm has been developed to detect possible structural motifs in the unknown metabolite which may produce characteristic fragment ions and neutral losses to related reference compounds contained in METLIN, even if the precursor masses are not the same.
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Affiliation(s)
- H P Benton
- Department of Molecular Biology and The Center for Mass Spectrometry, The Scripps Research Institute 10550 North Torrey Pines Road, La Jolla, California 92037, USA
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Minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE). Nat Biotechnol 2008; 26:305-12. [PMID: 18327244 DOI: 10.1038/nbt1391] [Citation(s) in RCA: 100] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
One purpose of the biomedical literature is to report results in sufficient detail that the methods of data collection and analysis can be independently replicated and verified. Here we present reporting guidelines for gene expression localization experiments: the minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE). MISFISHIE is modeled after the Minimum Information About a Microarray Experiment (MIAME) specification for microarray experiments. Both guidelines define what information should be reported without dictating a format for encoding that information. MISFISHIE describes six types of information to be provided for each experiment: experimental design, biomaterials and treatments, reporters, staining, imaging data and image characterizations. This specification has benefited the consortium within which it was developed and is expected to benefit the wider research community. We welcome feedback from the scientific community to help improve our proposal.
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Jameson D, Garwood K, Garwood C, Booth T, Alper P, Oliver SG, Paton NW. Data capture in bioinformatics: requirements and experiences with Pedro. BMC Bioinformatics 2008; 9:183. [PMID: 18402673 PMCID: PMC2335277 DOI: 10.1186/1471-2105-9-183] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2007] [Accepted: 04/10/2008] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The systematic capture of appropriately annotated experimental data is a prerequisite for most bioinformatics analyses. Data capture is required not only for submission of data to public repositories, but also to underpin integrated analysis, archiving, and sharing - both within laboratories and in collaborative projects. The widespread requirement to capture data means that data capture and annotation are taking place at many sites, but the small scale of the literature on tools, techniques and experiences suggests that there is work to be done to identify good practice and reduce duplication of effort. RESULTS This paper reports on experience gained in the deployment of the Pedro data capture tool in a range of representative bioinformatics applications. The paper makes explicit the requirements that have recurred when capturing data in different contexts, indicates how these requirements are addressed in Pedro, and describes case studies that illustrate where the requirements have arisen in practice. CONCLUSION Data capture is a fundamental activity for bioinformatics; all biological data resources build on some form of data capture activity, and many require a blend of import, analysis and annotation. Recurring requirements in data capture suggest that model-driven architectures can be used to construct data capture infrastructures that can be rapidly configured to meet the needs of individual use cases. We have described how one such model-driven infrastructure, namely Pedro, has been deployed in representative case studies, and discussed the extent to which the model-driven approach has been effective in practice.
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Affiliation(s)
- Daniel Jameson
- School of Chemistry, Manchester Interdisciplinary Biocentre, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK.
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Tsiknakis M, Brochhausen M, Nabrzyski J, Pucacki J, Sfakianakis SG, Potamias G, Desmedt C, Kafetzopoulos D. A semantic grid infrastructure enabling integrated access and analysis of multilevel biomedical data in support of postgenomic clinical trials on cancer. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE : A PUBLICATION OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY 2008; 12:205-17. [PMID: 18348950 DOI: 10.1109/titb.2007.903519] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper reports on original results of the Advancing Clinico-Genomic Trials on Cancer integrated project focusing on the design and development of a European biomedical grid infrastructure in support of multicentric, postgenomic clinical trials (CTs) on cancer. Postgenomic CTs use multilevel clinical and genomic data and advanced computational analysis and visualization tools to test hypothesis in trying to identify the molecular reasons for a disease and the stratification of patients in terms of treatment. This paper provides a presentation of the needs of users involved in postgenomic CTs, and presents such needs in the form of scenarios, which drive the requirements engineering phase of the project. Subsequently, the initial architecture specified by the project is presented, and its services are classified and discussed. A key set of such services are those used for wrapping heterogeneous clinical trial management systems and other public biological databases. Also, the main technological challenge, i.e. the design and development of semantically rich grid services is discussed. In achieving such an objective, extensive use of ontologies and metadata are required. The Master Ontology on Cancer, developed by the project, is presented, and our approach to develop the required metadata registries, which provide semantically rich information about available data and computational services, is provided. Finally, a short discussion of the work lying ahead is included.
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Affiliation(s)
- Manolis Tsiknakis
- Foundation for Research and Technology-Hellas, Institute of Computer Science, GR-71110 Heraklion, Greece.
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37
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Local pixel value collection algorithm for spot segmentation in two-dimensional gel electrophoresis research. Comp Funct Genomics 2008:89596. [PMID: 18274608 PMCID: PMC2216074 DOI: 10.1155/2007/89596] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2006] [Accepted: 06/14/2007] [Indexed: 12/02/2022] Open
Abstract
Two-dimensional gel-electrophoresis (2-DE) images show the expression levels of
several hundreds of proteins where each protein is represented as a blob-shaped spot of
grey level values. The spot detection, that is, the segmentation process has to be efficient as
it is the first step in the gel processing. Such extraction of information is a very complex
task. In this paper, we propose a novel spot detector that is basically a morphology-based
method with the use of a seeded region growing as a central paradigm and
which relies on the spot correlation information. The method is tested on our synthetic
as well as on real gels with human samples from SWISS-2DPAGE (two-dimensional
polyacrylamide gel electrophoresis) database. A comparison of results is done with a
method called pixel value collection (PVC). Since our algorithm efficiently uses local
spot information, segments the spot by collecting pixel values and its affinity with
PVC, we named it local pixel value collection (LPVC). The results show that LPVC
achieves similar segmentation results as PVC, but is much faster than PVC.
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38
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Stanislaus R, Arthur JM, Rajagopalan B, Moerschell R, McGlothlen B, Almeida JS. An open-source representation for 2-DE-centric proteomics and support infrastructure for data storage and analysis. BMC Bioinformatics 2008; 9:4. [PMID: 18179696 PMCID: PMC2231339 DOI: 10.1186/1471-2105-9-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2007] [Accepted: 01/07/2008] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND In spite of two-dimensional gel electrophoresis (2-DE) being an effective and widely used method to screen the proteome, its data standardization has still not matured to the level of microarray genomics data or mass spectrometry approaches. The trend toward identifying encompassing data standards has been expanding from genomics to transcriptomics, and more recently to proteomics. The relative success of genomic and transcriptomic data standardization has enabled the development of central repositories such as GenBank and Gene Expression Omnibus. An equivalent 2-DE-centric data structure would similarly have to include a balance among raw data, basic feature detection results, sufficiency in the description of the experimental context and methods, and an overall structure that facilitates a diversity of usages, from central reposition to local data representation in LIMs systems. RESULTS & CONCLUSION Achieving such a balance can only be accomplished through several iterations involving bioinformaticians, bench molecular biologists, and the manufacturers of the equipment and commercial software from which the data is primarily generated. Such an encompassing data structure is described here, developed as the mature successor to the well established and broadly used earlier version. A public repository, AGML Central, is configured with a suite of tools for the conversion from a variety of popular formats, web-based visualization, and interoperation with other tools and repositories, and is particularly mass-spectrometry oriented with I/O for annotation and data analysis.
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Affiliation(s)
- Romesh Stanislaus
- The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
| | - John M Arthur
- Medical University of South Carolina, 171 Ashley Ave., Charleston, SC 29425, USA
| | - Balaji Rajagopalan
- Virginia Bioinformatics Institute, Washington Street, MC 0447, Blacksburg, VA 24061, USA
| | - Rick Moerschell
- BioRad Laboratories, 1000 Alfred Nobel Dr., Hercules, CA 94547, USA
| | - Brian McGlothlen
- BioRad Laboratories, 1000 Alfred Nobel Dr., Hercules, CA 94547, USA
| | - Jonas S Almeida
- The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA
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Droit A, Hunter JM, Rouleau M, Ethier C, Picard-Cloutier A, Bourgais D, Poirier GG. PARPs database: a LIMS systems for protein-protein interaction data mining or laboratory information management system. BMC Bioinformatics 2007; 8:483. [PMID: 18093328 PMCID: PMC2266781 DOI: 10.1186/1471-2105-8-483] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2007] [Accepted: 12/19/2007] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In the "post-genome" era, mass spectrometry (MS) has become an important method for the analysis of proteins and the rapid advancement of this technique, in combination with other proteomics methods, results in an increasing amount of proteome data. This data must be archived and analysed using specialized bioinformatics tools. DESCRIPTION We herein describe "PARPs database," a data analysis and management pipeline for liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics. PARPs database is a web-based tool whose features include experiment annotation, protein database searching, protein sequence management, as well as data-mining of the peptides and proteins identified. CONCLUSION Using this pipeline, we have successfully identified several interactions of biological significance between PARP-1 and other proteins, namely RFC-1, 2, 3, 4 and 5.
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Affiliation(s)
- Arnaud Droit
- Health and Environment Unit, Laval University Medical research Center, CHUQ, Québec, Canada.
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Jones AR, Pizarro A, Spellman P, Miller M. FuGE: Functional Genomics Experiment Object Model. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2007; 10:179-84. [PMID: 16901224 DOI: 10.1089/omi.2006.10.179] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This is an interim report on the Functional Genomics Experiment (FuGE) Object Model. FuGE is a framework for creating data standards for high-throughput biological experiments, developed by a consortium of researchers from academia and industry. FuGE supports rich annotation of samples, protocols, instruments, and software, as well as providing extension points for technology specific details. It has been adopted by microarray and proteomics standards bodies as a basis for forthcoming standards. It is hoped that standards developers for other omics techniques will join this collaborative effort; widespread adoption will allow uniform annotation of common parts of functional genomics workflows, reduce standard development and learning times through the sharing of consistent practice, and ease the construction of software for accessing and integrating functional genomics data.
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Affiliation(s)
- Andrew R Jones
- School of Computer Science, University of Manchester, Manchester, United Kingdom.
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Jones AR, Miller M, Aebersold R, Apweiler R, Ball CA, Brazma A, DeGreef J, Hardy N, Hermjakob H, Hubbard SJ, Hussey P, Igra M, Jenkins H, Julian RK, Laursen K, Oliver SG, Paton NW, Sansone SA, Sarkans U, Stoeckert CJ, Taylor CF, Whetzel PL, White JA, Spellman P, Pizarro A. The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics. Nat Biotechnol 2007; 25:1127-33. [DOI: 10.1038/nbt1347] [Citation(s) in RCA: 79] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Abstract
The use of gel electrophoresis to separate and, in some instances, to quantify the abundance of large numbers of proteins from complex mixtures, has been well established for several decades. The quantity of publicly available data is still relatively modest due to a lack of community accepted data standards, tools to facilitate the data sharing process and controlled vocabularies to ensure that consistent terminology is used to describe the experimental methodology. It is becoming widely recognised that there are significant benefits in data sharing for proteomics, allowing results to be verified and new findings to be generated by re-analysis of published studies. We report on standards development by the Gel Analysis Workgroup of the Proteomics Standards Initiative. The workgroup develops reporting requirements, data formats and controlled vocabularies for experimental gel electrophoresis, and informatics performed on gel images. We present a tutorial on how such resources can be used and how the community should get involved with the on-going projects. Finally, we present a roadmap for future developments in this area.
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Affiliation(s)
- Andrew R Jones
- School of Computer Science and the Faculty of Life Sciences, University of Manchester, Manchester, UK.
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Kricka LJ, Master SR. Validation and Quality Control of Protein Microarray-based Analytical Methods. Mol Biotechnol 2007; 38:19-31. [DOI: 10.1007/s12033-007-0066-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2007] [Accepted: 07/06/2007] [Indexed: 01/20/2023]
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Mead JA, Shadforth IP, Bessant C. Public proteomic MS repositories and pipelines: available tools and biological applications. Proteomics 2007; 7:2769-86. [PMID: 17654461 DOI: 10.1002/pmic.200700152] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
As proteomic MS has increased in throughput, so has the demand to catalogue the increasing number of peptides and proteins observed by the community using this technique. As in other 'omics' fields, this brings obvious scientific benefits such as sharing of results and prevention of unnecessary repetition, but also provides technical insights, such as the ability to compare proteome coverage between different laboratories, or between different proteomic platforms. Journals are also moving towards mandating that proteomics data be submitted to public repositories upon publication. In response to these demands, several web-based repositories have been established to store protein and peptide identifications derived from MS data, and a similar number of peptide identification software pipelines have emerged to deliver identifications to these repositories. This paper reviews the latest developments in public domain peptide and protein identification databases and describes the analysis pipelines that feed them. Recent applications of the tools to pertinent biological problems are examined, and through comparing and contrasting the capabilities of each system, the issues facing research users of web-based repositories are explored. Future developments and mechanisms to enhance system functionality and user-interfacing opportunities are also suggested.
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Affiliation(s)
- Jennifer A Mead
- Cranfield Health, Cranfield University, Silsoe, Bedfordshire, UK
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Fournier ML, Gilmore JM, Martin-Brown SA, Washburn MP. Multidimensional Separations-Based Shotgun Proteomics. Chem Rev 2007; 107:3654-86. [PMID: 17649983 DOI: 10.1021/cr068279a] [Citation(s) in RCA: 171] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data. BMC Bioinformatics 2007; 8:197. [PMID: 17567892 PMCID: PMC1906842 DOI: 10.1186/1471-2105-8-197] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2007] [Accepted: 06/13/2007] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The advancements of proteomics technologies have led to a rapid increase in the number, size and rate at which datasets are generated. Managing and extracting valuable information from such datasets requires the use of data management platforms and computational approaches. RESULTS We have developed the MAss SPECTRometry Analysis System (MASPECTRAS), a platform for management and analysis of proteomics LC-MS/MS data. MASPECTRAS is based on the Proteome Experimental Data Repository (PEDRo) relational database schema and follows the guidelines of the Proteomics Standards Initiative (PSI). Analysis modules include: 1) import and parsing of the results from the search engines SEQUEST, Mascot, Spectrum Mill, X! Tandem, and OMSSA; 2) peptide validation, 3) clustering of proteins based on Markov Clustering and multiple alignments; and 4) quantification using the Automated Statistical Analysis of Protein Abundance Ratios algorithm (ASAPRatio). The system provides customizable data retrieval and visualization tools, as well as export to PRoteomics IDEntifications public repository (PRIDE). MASPECTRAS is freely available at http://genome.tugraz.at/maspectras CONCLUSION Given the unique features and the flexibility due to the use of standard software technology, our platform represents significant advance and could be of great interest to the proteomics community.
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Zhang X, Wei D, Yap Y, Li L, Guo S, Chen F. Mass spectrometry-based "omics" technologies in cancer diagnostics. MASS SPECTROMETRY REVIEWS 2007; 26:403-31. [PMID: 17405143 DOI: 10.1002/mas.20132] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Many "omics" techniques have been developed for one goal: biomarker discovery and early diagnosis of human cancers. A comprehensive review of mass spectrometry-based "omics" approaches performed on various biological samples for molecular diagnosis of human cancers is presented in this article. Furthermore, the existing and potential problems/solutions (both de facto experimental and bioinformatic challenges), and future prospects have been extensively discussed. Although the use of present omic methods as diagnostic tools are still in their infant stage and consequently not ready for immediate clinical use, it can be envisaged that the "omics"-based cancer diagnostics will gradually enter into the clinic in next 10 years as an important supplement to current clinical diagnostics.
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Affiliation(s)
- Xuewu Zhang
- College of Light Industry and Food Sciences, South China University of Technology, Guangzhou, China.
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Ivakhno S, Kornelyuk A. Quantitative proteomics and its applications for systems biology. BIOCHEMISTRY (MOSCOW) 2007; 71:1060-72. [PMID: 17125453 DOI: 10.1134/s0006297906100026] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Here we discuss the current state of research in the rapidly growing field of quantitative proteomics and its applications to systems biology. Quantitative proteomics can be successfully used for characterizing alterations in protein abundance, finding novel protein-protein and protein-peptide interactions, investigating formation of large macromolecular complexes, and elucidating temporal changes in organellar protein composition and phosphorylation in signal transduction cascades. Further, quantitative proteomics can directly compare activation of entire signaling networks in response to individual stimuli and discover critical differences in their circuits that account for alterations of cell response. Maturation of proteomic bioinformatics applications and continuous improvements in proteomics and related genomics and transcriptomics technologies now allows us to investigate cellular mechanisms at the integrative system level.
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Affiliation(s)
- S Ivakhno
- University of Edinburgh, School of Informatics, Edinburgh, EH8 9LE, Scotland, UK.
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Assmus HE, Herwig R, Cho KH, Wolkenhauer O. Dynamics of biological systems: role of systems biology in medical research. Expert Rev Mol Diagn 2007; 6:891-902. [PMID: 17140376 DOI: 10.1586/14737159.6.6.891] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Cellular systems are networks of interacting components that change with time in response to external and internal events. Studying the dynamic behavior of these networks is the basis for an understanding of cellular functions and disease mechanisms. Quantitative time-series data leading to meaningful models can improve our knowledge of human physiology in health and disease, and aid the search for earlier diagnoses, better therapies and a healthier life. The advent of systems biology is about to take the leap into clinical research and medical applications. This review emphasizes the importance of a dynamic view and understanding of cell function. We discuss the potential for computer-aided mathematical modeling of biological systems in medical research with examples from some of the major therapeutic areas: cancer, cardiovascular, diabetic and neurodegenerative medicine.
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Affiliation(s)
- Heike E Assmus
- University of Rostock, Systems Biology and Bioinformatics Group, Department of Computer Science, 18051 Rostock, Germany.
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Hermjakob H, Apweiler R. The Proteomics Identifications Database (PRIDE) and the ProteomExchange Consortium: making proteomics data accessible. Expert Rev Proteomics 2007; 3:1-3. [PMID: 16445344 DOI: 10.1586/14789450.3.1.1] [Citation(s) in RCA: 65] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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