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Yelamanchi SD, Tyagi A, Mohanty V, Dutta P, Korbonits M, Chavan S, Advani J, Madugundu AK, Dey G, Datta KK, Rajyalakshmi M, Sahasrabuddhe NA, Chaturvedi A, Kumar A, Das AA, Ghosh D, Jogdand GM, Nair HH, Saini K, Panchal M, Sarvaiya MA, Mohanraj SS, Sengupta N, Saxena P, Subramani PA, Kumar P, Akkali R, Reshma SV, Santhosh RS, Rastogi S, Kumar S, Ghosh SK, Irlapati VK, Srinivasan A, Radotra BD, Mathur PP, Wong GW, Satishchandra P, Chatterjee A, Gowda H, Bhansali A, Pandey A, Shankar SK, Mahadevan A, Prasad TSK. Proteomic Analysis of the Human Anterior Pituitary Gland. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2019; 22:759-769. [PMID: 30571610 DOI: 10.1089/omi.2018.0160] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The pituitary function is regulated by a complex system involving the hypothalamus and biological networks within the pituitary. Although the hormones secreted from the pituitary have been well studied, comprehensive analyses of the pituitary proteome are limited. Pituitary proteomics is a field of postgenomic research that is crucial to understand human health and pituitary diseases. In this context, we report here a systematic proteomic profiling of human anterior pituitary gland (adenohypophysis) using high-resolution Fourier transform mass spectrometry. A total of 2164 proteins were identified in this study, of which 105 proteins were identified for the first time compared with high-throughput proteomic-based studies from human pituitary glands. In addition, we identified 480 proteins with secretory potential and 187 N-terminally acetylated proteins. These are the first region-specific data that could serve as a vital resource for further investigations on the physiological role of the human anterior pituitary glands and the proteins secreted by them. We anticipate that the identification of previously unknown proteins in the present study will accelerate biomedical research to decipher their role in functioning of the human anterior pituitary gland and associated human diseases.
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Affiliation(s)
| | - Ankur Tyagi
- 2 Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Varshasnata Mohanty
- 2 Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
| | - Pinaki Dutta
- 3 Department of Endocrinology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Márta Korbonits
- 4 Department of Endocrinology, Barts and the London School of Medicine, Queen Mary University of London, London, United Kingdom
| | - Sandip Chavan
- 1 Institute of Bioinformatics, International Technology Park, Bangalore, India
| | - Jayshree Advani
- 1 Institute of Bioinformatics, International Technology Park, Bangalore, India.,5 Manipal Academy of Higher Education, Manipal, India
| | - Anil K Madugundu
- 1 Institute of Bioinformatics, International Technology Park, Bangalore, India.,5 Manipal Academy of Higher Education, Manipal, India.,6 Center for Molecular Medicine, National Institute of Mental Health & Neurosciences, Bangalore, India.,7 Department of Laboratory Medicine and Pathology and Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota
| | - Gourav Dey
- 1 Institute of Bioinformatics, International Technology Park, Bangalore, India.,5 Manipal Academy of Higher Education, Manipal, India
| | - Keshava K Datta
- 1 Institute of Bioinformatics, International Technology Park, Bangalore, India
| | - M Rajyalakshmi
- 8 Department of Biotechnology, BMS College of Engineering, Bangalore, India
| | | | - Abhishek Chaturvedi
- 9 Department of Biochemistry, Melaka Manipal Medical College, Manipal Academy of Higher Education, Manipal, India
| | - Amit Kumar
- 10 Institute of Life Sciences, Nalco Square, Bhubaneswar, India
| | - Apabrita Ayan Das
- 11 Cell Biology and Physiology Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Dhiman Ghosh
- 12 Protein Engineering and Neurobiology Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology, Bombay, India
| | | | - Haritha H Nair
- 13 Division of Cancer Research, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, India
| | - Keshav Saini
- 14 Faculty of Life Sciences and Biotechnology, South Asian University, New Delhi, India
| | - Manoj Panchal
- 15 Department of Life Science, Central University of South Bihar, Gaya, India
| | | | - Soundappan S Mohanraj
- 17 Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Nabonita Sengupta
- 18 Neuroinflammation Laboratory, National Brain Research Centre, Manesar, India
| | - Priti Saxena
- 14 Faculty of Life Sciences and Biotechnology, South Asian University, New Delhi, India
| | | | - Pradeep Kumar
- 20 Department of Biotechnology, VBS Purvanchal University, Jaunpur, India
| | - Rakhil Akkali
- 21 Department of Biotechnology, Indian Institute of Technology, Madras, India
| | | | | | - Sangita Rastogi
- 24 Microbiology Laboratory, National Institute of Pathology, New Delhi, India
| | - Sudarshan Kumar
- 25 Proteomics and Structural Biology Laboratory, Animal Biotechnology Center, National Dairy Research Institute, Karnal, India
| | - Susanta Kumar Ghosh
- 19 Department of Molecular Parasitology, National Institute of Malaria Research, Bangalore, India
| | | | - Anand Srinivasan
- 27 Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Bishan Das Radotra
- 28 Department of Histopathology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Premendu P Mathur
- 29 Department of Biochemistry and Molecular Biology, School of Life Sciences, Pondicherry University, Pondicherry, India
| | - G William Wong
- 30 Department of Physiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - Aditi Chatterjee
- 1 Institute of Bioinformatics, International Technology Park, Bangalore, India
| | - Harsha Gowda
- 1 Institute of Bioinformatics, International Technology Park, Bangalore, India
| | - Anil Bhansali
- 3 Department of Endocrinology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Akhilesh Pandey
- 1 Institute of Bioinformatics, International Technology Park, Bangalore, India.,5 Manipal Academy of Higher Education, Manipal, India.,6 Center for Molecular Medicine, National Institute of Mental Health & Neurosciences, Bangalore, India.,7 Department of Laboratory Medicine and Pathology and Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota.,32 McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.,33 Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland.,34 Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.,35 Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Susarla K Shankar
- 36 Department of Neuropathology, National Institute of Mental Health and Neuro Sciences, Bangalore, India.,37 Human Brain Tissue Repository, National Institute of Mental Health and Neuro Sciences, Neurobiology Research Centre, Bangalore, India
| | - Anita Mahadevan
- 36 Department of Neuropathology, National Institute of Mental Health and Neuro Sciences, Bangalore, India.,37 Human Brain Tissue Repository, National Institute of Mental Health and Neuro Sciences, Neurobiology Research Centre, Bangalore, India
| | - T S Keshava Prasad
- 1 Institute of Bioinformatics, International Technology Park, Bangalore, India.,2 Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India
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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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Personalized medicine beyond genomics: alternative futures in big data—proteomics, environtome and the social proteome. J Neural Transm (Vienna) 2015; 124:25-32. [DOI: 10.1007/s00702-015-1489-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2015] [Accepted: 11/19/2015] [Indexed: 12/15/2022]
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4
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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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Yadav AK, Kumar D, Dash D. Learning from decoys to improve the sensitivity and specificity of proteomics database search results. PLoS One 2012. [PMID: 23189209 PMCID: PMC3506577 DOI: 10.1371/journal.pone.0050651] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The statistical validation of database search results is a complex issue in bottom-up proteomics. The correct and incorrect peptide spectrum match (PSM) scores overlap significantly, making an accurate assessment of true peptide matches challenging. Since the complete separation between the true and false hits is practically never achieved, there is need for better methods and rescoring algorithms to improve upon the primary database search results. Here we describe the calibration and False Discovery Rate (FDR) estimation of database search scores through a dynamic FDR calculation method, FlexiFDR, which increases both the sensitivity and specificity of search results. Modelling a simple linear regression on the decoy hits for different charge states, the method maximized the number of true positives and reduced the number of false negatives in several standard datasets of varying complexity (18-mix, 49-mix, 200-mix) and few complex datasets (E. coli and Yeast) obtained from a wide variety of MS platforms. The net positive gain for correct spectral and peptide identifications was up to 14.81% and 6.2% respectively. The approach is applicable to different search methodologies- separate as well as concatenated database search, high mass accuracy, and semi-tryptic and modification searches. FlexiFDR was also applied to Mascot results and showed better performance than before. We have shown that appropriate threshold learnt from decoys, can be very effective in improving the database search results. FlexiFDR adapts itself to different instruments, data types and MS platforms. It learns from the decoy hits and sets a flexible threshold that automatically aligns itself to the underlying variables of data quality and size.
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Affiliation(s)
- Amit Kumar Yadav
- GNR Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Dhirendra Kumar
- GNR Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, Delhi, India
| | - Debasis Dash
- GNR Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, Delhi, India
- * E-mail:
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6
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Yao C, Li H, Shen X, He Z, He L, Guo Z. Reproducibility and concordance of differential DNA methylation and gene expression in cancer. PLoS One 2012; 7:e29686. [PMID: 22235325 PMCID: PMC3250460 DOI: 10.1371/journal.pone.0029686] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2011] [Accepted: 12/01/2011] [Indexed: 12/11/2022] Open
Abstract
Background Hundreds of genes with differential DNA methylation of promoters have been identified for various cancers. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. Methodology/Principal Findings Using array data for seven types of cancers, we first evaluated the effects of experimental batches on differential DNA methylation detection. Second, we compared the directions of DNA methylation changes detected from different datasets for the same cancer. Third, we evaluated the concordance between methylation and gene expression changes. Finally, we compared DNA methylation changes in different cancers. For a given cancer, the directions of methylation and expression changes detected from different datasets, excluding potential batch effects, were highly consistent. In different cancers, DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression, whereas hypomethylation was only weakly correlated with the up-regulation of genes. Finally, we found that genes commonly hypomethylated in different cancers primarily performed functions associated with chronic inflammation, such as ‘keratinization’, ‘chemotaxis’ and ‘immune response’. Conclusions Batch effects could greatly affect the discovery of DNA methylation biomarkers. For a particular cancer, both differential DNA methylation and gene expression can be reproducibly detected from different studies with no batch effects. While DNA hypermethylation is significantly linked to gene down-regulation, hypomethylation is only weakly correlated with gene up-regulation and is likely to be linked to chronic inflammation.
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Affiliation(s)
- Chen Yao
- Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongdong Li
- Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaopei Shen
- Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu, China
| | - Zheng He
- Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu, China
| | - Lang He
- Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu, China
| | - Zheng Guo
- Bioinformatics Centre and Key Laboratory for NeuroInfomation of the Education Ministry of China, School of Life Science, University of Electronic Science and Technology of China, Chengdu, China
- Colleges of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- * E-mail:
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7
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Reproducible cancer biomarker discovery in SELDI-TOF MS using different pre-processing algorithms. PLoS One 2011; 6:e26294. [PMID: 22022591 PMCID: PMC3194809 DOI: 10.1371/journal.pone.0026294] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2011] [Accepted: 09/24/2011] [Indexed: 12/14/2022] Open
Abstract
Background There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS) studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached. Results In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE) peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consistency of DE peak identification using different algorithms. One factor is that some DE peaks selected from one peak profile were not detected as peaks in other profiles, and the second factor is that the statistical power of identifying DE peaks in large peak profiles with many peaks may be low due to the large scale of the tests and small number of samples. Furthermore, we demonstrated that the DE peak detection power in large profiles could be improved by the stratified false discovery rate (FDR) control approach and that the reproducibility of DE peak detection could thereby be increased. Conclusions Comparing and evaluating pre-processing algorithms in terms of reproducibility can elucidate the relationship among different algorithms and also help in selecting a pre-processing algorithm. The DE peaks selected from small peak profiles with few peaks for a dataset tend to be reproducibly detected in large peak profiles, which suggests that a suitable pre-processing algorithm should be able to produce peaks sufficient for identifying useful and reproducible biomarkers.
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Bauman A, Higdon R, Rapson S, Loiue B, Hogan J, Stacy R, Napuli A, Guo W, van Voorhis W, Roach J, Lu V, Landorf E, Stewart E, Kolker N, Collart F, Myler P, van Belle G, Kolker E. Design and initial characterization of the SC-200 proteomics standard mixture. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2011; 15:73-82. [PMID: 21250827 DOI: 10.1089/omi.2010.0118] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate = 1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.
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Affiliation(s)
- Andrew Bauman
- Seattle Children's Research Institute, Bioinformatics and High-throughput Analysis Laboratory, Seattle Children's Research Institute, High-throughput Analysis Core, Seattle, Washington 98109, USA
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9
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Holmes C, McDonald F, Jones M, Ozdemir V, Graham JE. Standardization and omics science: technical and social dimensions are inseparable and demand symmetrical study. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2010; 14:327-32. [PMID: 20455752 DOI: 10.1089/omi.2010.0022] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Standardization is critical to scientists and regulators to ensure the quality and interoperability of research processes, as well as the safety and efficacy of the attendant research products. This is perhaps most evident in the case of "omics science," which is enabled by a host of diverse high-throughput technologies such as genomics, proteomics, and metabolomics. But standards are of interest to (and shaped by) others far beyond the immediate realm of individual scientists, laboratories, scientific consortia, or governments that develop, apply, and regulate them. Indeed, scientific standards have consequences for the social, ethical, and legal environment in which innovative technologies are regulated, and thereby command the attention of policy makers and citizens. This article argues that standardization of omics science is both technical and social. A critical synthesis of the social science literature indicates that: (1) standardization requires a degree of flexibility to be practical at the level of scientific practice in disparate sites; (2) the manner in which standards are created, and by whom, will impact their perceived legitimacy and therefore their potential to be used; and (3) the process of standardization itself is important to establishing the legitimacy of an area of scientific research.
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Affiliation(s)
- Christina Holmes
- Technoscience and Regulation Research Unit, Faculty of Medicine, Dalhousie University, Halifax, Canada
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10
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Hather G, Higdon R, Bauman A, von Haller PD, Kolker E. Estimating false discovery rates for peptide and protein identification using randomized databases. Proteomics 2010; 10:2369-76. [PMID: 20391536 DOI: 10.1002/pmic.200900619] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
MS-based proteomics characterizes protein contents of biological samples. The most common approach is to first match observed MS/MS peptide spectra against theoretical spectra from a protein sequence database and then to score these matches. The false discovery rate (FDR) can be estimated as a function of the score by searching together the protein sequence database and its randomized version and comparing the score distributions of the randomized versus nonrandomized matches. This work introduces a straightforward isotonic regression-based method to estimate the cumulative FDRs and local FDRs (LFDRs) of peptide identification. Our isotonic method not only performed as well as other methods used for comparison, but also has the advantages of being: (i) monotonic in the score, (ii) computationally simple, and (iii) not dependent on assumptions about score distributions. We demonstrate the flexibility of our approach by using it to estimate FDRs and LFDRs for protein identification using summaries of the peptide spectra scores. We reconfirmed that several of these methods were superior to a two-peptide rule. Finally, by estimating both the FDRs and LFDRs, we showed for both peptide and protein identification, moderate FDR values (5%) corresponded to large LFDR values (53 and 60%).
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Affiliation(s)
- Gregory Hather
- Bioinformatics & High-throughput Analysis Laboratory, Seattle Children's Research Institute, Seattle, WA 98101, USA
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11
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Vogt L, Bartolomaeus T, Giribet G. The linguistic problem of morphology: structure versus homology and the standardization of morphological data. Cladistics 2009; 26:301-325. [DOI: 10.1111/j.1096-0031.2009.00286.x] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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12
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Moxon JV, Padula MP, Herbert BR, Golledge J. Challenges, current status and future perspectives of proteomics in improving understanding, diagnosis and treatment of vascular disease. Eur J Vasc Endovasc Surg 2009; 38:346-55. [PMID: 19541510 PMCID: PMC2727576 DOI: 10.1016/j.ejvs.2009.05.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2009] [Accepted: 05/11/2009] [Indexed: 01/21/2023]
Abstract
Technical advances have seen the rapid adoption of genomics and multiplex genetic polymorphism identification to research on vascular diseases. The utilization of proteomics for the study of vascular diseases has been limited by comparison. In this review we outline currently available proteomics techniques, the challenges to using these approaches and modifications which may improve the utilization of proteomics in the study of vascular diseases.
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Affiliation(s)
- Joseph V. Moxon
- Vascular Biology Unit, School of Medicine and Dentistry, James Cook University, Townsville, Queensland 4811, Australia
| | - Matthew P. Padula
- Proteomics Technology Centre of Expertise, Faculty of Science, University of Technology, Sydney, New South Wales 2007, Australia
| | - Ben R. Herbert
- Proteomics Technology Centre of Expertise, Faculty of Science, University of Technology, Sydney, New South Wales 2007, Australia
| | - Jonathan Golledge
- Vascular Biology Unit, School of Medicine and Dentistry, James Cook University, Townsville, Queensland 4811, Australia
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Nie L, Wu G, Zhang W. Statistical Application and Challenges in Global Gel-Free Proteomic Analysis by Mass Spectrometry. Crit Rev Biotechnol 2008; 28:297-307. [DOI: 10.1080/07388550802543158] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Taylor CF, Paton NW, Lilley KS, Binz PA, Julian RK, Jones AR, Zhu W, Apweiler R, Aebersold R, Deutsch EW, Dunn MJ, Heck AJR, Leitner A, Macht M, Mann M, Martens L, Neubert TA, Patterson SD, Ping P, Seymour SL, Souda P, Tsugita A, Vandekerckhove J, Vondriska TM, Whitelegge JP, Wilkins MR, Xenarios I, Yates JR, Hermjakob H. The minimum information about a proteomics experiment (MIAPE). Nat Biotechnol 2007; 25:887-93. [PMID: 17687369 DOI: 10.1038/nbt1329] [Citation(s) in RCA: 528] [Impact Index Per Article: 31.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Both the generation and the analysis of proteomics data are now widespread, and high-throughput approaches are commonplace. Protocols continue to increase in complexity as methods and technologies evolve and diversify. To encourage the standardized collection, integration, storage and dissemination of proteomics data, the Human Proteome Organization's Proteomics Standards Initiative develops guidance modules for reporting the use of techniques such as gel electrophoresis and mass spectrometry. This paper describes the processes and principles underpinning the development of these modules; discusses the ramifications for various interest groups such as experimentalists, funders, publishers and the private sector; addresses the issue of overlap with other reporting guidelines; and highlights the criticality of appropriate tools and resources in enabling 'MIAPE-compliant' reporting.
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Affiliation(s)
- Chris F Taylor
- The HUPO Proteomics Standards Initiative, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
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Abstract
This 2006 'Plant Proteomics Update' is a continuation of the two previously published in 'Proteomics' by 2004 (Canovas et al., Proteomics 2004, 4, 285-298) and 2006 (Rossignol et al., Proteomics 2006, 6, 5529-5548) and it aims to bring up-to-date the contribution of proteomics to plant biology on the basis of the original research papers published throughout 2006, with references to those appearing last year. According to the published papers and topics addressed, we can conclude that, as observed for the three previous years, there has been a quantitative, but not qualitative leap in plant proteomics. The full potential of proteomics is far from being exploited in plant biology research, especially if compared to other organisms, mainly yeast and humans, and a number of challenges, mainly technological, remain to be tackled. The original papers published last year numbered nearly 100 and deal with the proteome of at least 26 plant species, with a high percentage for Arabidopsis thaliana (28) and rice (11). Scientific objectives ranged from proteomic analysis of organs/tissues/cell suspensions (57) or subcellular fractions (29), to the study of plant development (12), the effect of hormones and signalling molecules (8) and response to symbionts (4) and stresses (27). A small number of contributions have covered PTMs (8) and protein interactions (4). 2-DE (specifically IEF-SDS-PAGE) coupled to MS still constitutes the almost unique platform utilized in plant proteome analysis. The application of gel-free protein separation methods and 'second generation' proteomic techniques such as multidimensional protein identification technology (MudPIT), and those for quantitative proteomics including DIGE, isotope-coded affinity tags (ICAT), iTRAQ and stable isotope labelling by amino acids in cell culture (SILAC) still remains anecdotal. This review is divided into seven sections: Introduction, Methodology, Subcellular proteomes, Development, Responses to biotic and abiotic stresses, PTMs and Protein interactions. Section 8 summarizes the major pitfalls and challenges of plant proteomics.
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Affiliation(s)
- Jesús V Jorrín
- Agricultural and Plant Biochemistry Research Group-Plant Proteomics, Department of Biochemistry and Molecular Biology, University of Córdoba, Córdoba, Spain.
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Kolker E, Hogan JM, Higdon R, Kolker N, Landorf E, Yakunin AF, Collart FR, van Belle G. Development of BIATECH-54 standard mixtures for assessment of protein identification and relative expression. Proteomics 2007; 7:3693-8. [PMID: 17890649 DOI: 10.1002/pmic.200700088] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Mixtures of known proteins have been very useful in the assessment and validation of methods for high-throughput (HTP) MS (MS/MS) proteomics experiments. However, these test mixtures have generally consisted of few proteins at near equal concentration or of a single protein at varied concentrations. Such mixtures are too simple to effectively assess the validity of error rates for protein identification and differential expression in HTP MS/MS studies. This work aimed at overcoming these limitations and simulating studies of complex biological samples. We introduced a pair of 54-protein standard mixtures of variable concentrations with up to a 1000-fold dynamic range in concentration and up to ten-fold expression ratios with additional negative controls (infinite expression ratios). These test mixtures comprised 16 off-the-shelf Sigma-Aldrich proteins and 38 Shewanella oneidensis proteins produced in-house. The standard proteins were systematically distributed into three main concentration groups (high, medium, and low) and then the concentrations were varied differently for each mixture within the groups to generate different expression ratios. The mixtures were analyzed with both low mass accuracy LCQ and high mass accuracy FT-LTQ instruments. In addition, these 54 standard proteins closely follow the molecular weight distributions of both bacterial and human proteomes. As a result, these new standard mixtures allow for a much more realistic assessment of approaches for protein identification and label-free differential expression than previous mixtures. Finally, methodology and experimental design developed in this work can be readily applied in future to development of more complex standard mixtures for HTP proteomics studies.
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