1
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Wang XY, Xu YM, Lau ATY. Proteogenomics in Cancer: Then and Now. J Proteome Res 2023; 22:3103-3122. [PMID: 37725793 DOI: 10.1021/acs.jproteome.3c00196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.
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Affiliation(s)
- Xiu-Yun Wang
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, People's Republic of China
| | - Yan-Ming Xu
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, People's Republic of China
| | - Andy T Y Lau
- Laboratory of Cancer Biology and Epigenetics, Department of Cell Biology and Genetics, Shantou University Medical College, Shantou, Guangdong 515041, People's Republic of China
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2
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Reilly L, Seddighi S, Singleton AB, Cookson MR, Ward ME, Qi YA. Variant biomarker discovery using mass spectrometry-based proteogenomics. FRONTIERS IN AGING 2023; 4:1191993. [PMID: 37168844 PMCID: PMC10165118 DOI: 10.3389/fragi.2023.1191993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 04/13/2023] [Indexed: 05/13/2023]
Abstract
Genomic diversity plays critical roles in risk of disease pathogenesis and diagnosis. While genomic variants-including single nucleotide variants, frameshift variants, and mis-splicing isoforms-are commonly detected at the DNA or RNA level, their translated variant protein or polypeptide products are ultimately the functional units of the associated disease. These products are often released in biofluids and could be leveraged for clinical diagnosis and patient stratification. Recent emergence of integrated analysis of genomics with mass spectrometry-based proteomics for biomarker discovery, also known as proteogenomics, have significantly advanced the understanding disease risk variants, precise medicine, and biomarker discovery. In this review, we discuss variant proteins in the context of cancers and neurodegenerative diseases, outline current and emerging proteogenomic approaches for biomarker discovery, and provide a comprehensive proteogenomic strategy for detection of putative biomarker candidates in human biospecimens. This strategy can be implemented for proteogenomic studies in any field of enquiry. Our review timely addresses the need of biomarkers for aging related diseases.
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Affiliation(s)
- Luke Reilly
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Sahba Seddighi
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Andrew B. Singleton
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
| | - Mark R. Cookson
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
| | - Michael E. Ward
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Yue A. Qi
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
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3
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Gosset-Erard C, Didierjean M, Pansanel J, Lechner A, Wolff P, Kuhn L, Aubriet F, Leize-Wagner E, Chaimbault P, François YN. Nucleos'ID: A New Search Engine Enabling the Untargeted Identification of RNA Post-transcriptional Modifications from Tandem Mass Spectrometry Analyses of Nucleosides. Anal Chem 2023; 95:1608-1617. [PMID: 36598775 DOI: 10.1021/acs.analchem.2c04722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
As RNA post-transcriptional modifications are of growing interest, several methods were developed for their characterization. One of them established for their identification, at the nucleosidic level, is the hyphenation of separation methods, such as liquid chromatography or capillary electrophoresis, to tandem mass spectrometry. However, to our knowledge, no software is yet available for the untargeted identification of RNA post-transcriptional modifications from MS/MS data-dependent acquisitions. Thus, very long and tedious manual data interpretations are required. To meet the need of easier and faster data interpretation, a new user-friendly search engine, called Nucleos'ID, was developed for CE-MS/MS and LC-MS/MS users. Performances of this new software were evaluated on CE-MS/MS data from nucleoside analyses of already well-described Saccharomyces cerevisiae transfer RNA and Bos taurus total tRNA extract. All samples showed great true positive, true negative, and false discovery rates considering the database size containing all modified and unmodified nucleosides referenced in the literature. The true positive and true negative rates obtained were above 0.94, while the false discovery rates were between 0.09 and 0.17. To increase the level of sample complexity, untargeted identification of several RNA modifications from Pseudomonas aeruginosa 70S ribosome was achieved by the Nucleos'ID search following CE-MS/MS analysis.
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Affiliation(s)
- Clarisse Gosset-Erard
- Laboratoire de Spectrométrie de Masse des Interactions et des Systèmes (LSMIS), UMR 7140 (Unistra-CNRS), Université de Strasbourg, Strasbourg67000, France.,Université de Lorraine, LCP-A2MC, F-57000Metz, France
| | - Mévie Didierjean
- Laboratoire de Spectrométrie de Masse des Interactions et des Systèmes (LSMIS), UMR 7140 (Unistra-CNRS), Université de Strasbourg, Strasbourg67000, France
| | - Jérome Pansanel
- Université de Strasbourg, Institut Pluridisciplinaire Hubert Curien (IPHC), CNRS, UMR7178, Strasbourg67037, France
| | - Antony Lechner
- Architecture et Réactivité de l'ARN, Institut de Biologie Moléculaire et Cellulaire, CNRS UPR9002, Université de Strasbourg, Strasbourg67084, France
| | - Philippe Wolff
- Architecture et Réactivité de l'ARN, Institut de Biologie Moléculaire et Cellulaire, CNRS UPR9002, Université de Strasbourg, Strasbourg67084, France
| | - Lauriane Kuhn
- Plateforme Protéomique Strasbourg-Esplanade, Institut de Biologie Moléculaire et Cellulaire, FR1589 CNRS, CEDEX, Strasbourg67084, France
| | | | - Emmanuelle Leize-Wagner
- Laboratoire de Spectrométrie de Masse des Interactions et des Systèmes (LSMIS), UMR 7140 (Unistra-CNRS), Université de Strasbourg, Strasbourg67000, France
| | | | - Yannis-Nicolas François
- Laboratoire de Spectrométrie de Masse des Interactions et des Systèmes (LSMIS), UMR 7140 (Unistra-CNRS), Université de Strasbourg, Strasbourg67000, France
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4
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Aggarwal S, Raj A, Kumar D, Dash D, Yadav AK. False discovery rate: the Achilles' heel of proteogenomics. Brief Bioinform 2022; 23:6582880. [PMID: 35534181 DOI: 10.1093/bib/bbac163] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/14/2022] [Accepted: 04/12/2022] [Indexed: 12/25/2022] Open
Abstract
Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.
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Affiliation(s)
- Suruchi Aggarwal
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd milestone, PO Box No. 04, Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India
| | - Anurag Raj
- GN Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics & Integrative Biology, South Campus, Mathura Road, New Delhi 110025, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
| | - Dhirendra Kumar
- GN Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics & Integrative Biology, South Campus, Mathura Road, New Delhi 110025, India
| | - Debasis Dash
- GN Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics & Integrative Biology, South Campus, Mathura Road, New Delhi 110025, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India
| | - Amit Kumar Yadav
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd milestone, PO Box No. 04, Faridabad-Gurgaon Expressway, Faridabad-121001, Haryana, India
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Parmar BS, Peeters MKR, Boonen K, Clark EC, Baggerman G, Menschaert G, Temmerman L. Identification of Non-Canonical Translation Products in C. elegans Using Tandem Mass Spectrometry. Front Genet 2021; 12:728900. [PMID: 34759956 PMCID: PMC8575065 DOI: 10.3389/fgene.2021.728900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 09/16/2021] [Indexed: 11/22/2022] Open
Abstract
Transcriptome and ribosome sequencing have revealed the existence of many non-canonical transcripts, mainly containing splice variants, ncRNA, sORFs and altORFs. However, identification and characterization of products that may be translated out of these remains a challenge. Addressing this, we here report on 552 non-canonical proteins and splice variants in the model organism C. elegans using tandem mass spectrometry. Aided by sequencing-based prediction, we generated a custom proteome database tailored to search for non-canonical translation products of C. elegans. Using this database, we mined available mass spectrometric resources of C. elegans, from which 51 novel, non-canonical proteins could be identified. Furthermore, we utilized diverse proteomic and peptidomic strategies to detect 40 novel non-canonical proteins in C. elegans by LC-TIMS-MS/MS, of which 6 were common with our meta-analysis of existing resources. Together, this permits us to provide a resource with detailed annotation of 467 splice variants and 85 novel proteins mapped onto UTRs, non-coding regions and alternative open reading frames of the C. elegans genome.
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Affiliation(s)
- Bhavesh S. Parmar
- Animal Physiology and Neurobiology, University of Leuven (KU Leuven), Leuven, Belgium
| | - Marlies K. R. Peeters
- Laboratory of Bioinformatics and Computational Genomics (BioBix), Department of Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Kurt Boonen
- Centre for Proteomics (CFP), University of Antwerp, Antwerp, Belgium
| | - Ellie C. Clark
- Animal Physiology and Neurobiology, University of Leuven (KU Leuven), Leuven, Belgium
| | - Geert Baggerman
- Centre for Proteomics (CFP), University of Antwerp, Antwerp, Belgium
| | - Gerben Menschaert
- Laboratory of Bioinformatics and Computational Genomics (BioBix), Department of Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Liesbet Temmerman
- Animal Physiology and Neurobiology, University of Leuven (KU Leuven), Leuven, Belgium
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Vitorino R, Choudhury M, Guedes S, Ferreira R, Thongboonkerd V, Sharma L, Amado F, Srivastava S. Peptidomics and proteogenomics: background, challenges and future needs. Expert Rev Proteomics 2021; 18:643-659. [PMID: 34517741 DOI: 10.1080/14789450.2021.1980388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION With available genomic data and related information, it is becoming possible to better highlight mutations or genomic alterations associated with a particular disease or disorder. The advent of high-throughput sequencing technologies has greatly advanced diagnostics, prognostics, and drug development. AREAS COVERED Peptidomics and proteogenomics are the two post-genomic technologies that enable the simultaneous study of peptides and proteins/transcripts/genes. Both technologies add a remarkably large amount of data to the pool of information on various peptides associated with gene mutations or genome remodeling. Literature search was performed in the PubMed database and is up to date. EXPERT OPINION This article lists various techniques used for peptidomic and proteogenomic analyses. It also explains various bioinformatics workflows developed to understand differentially expressed peptides/proteins and their role in disease pathogenesis. Their role in deciphering disease pathways, cancer research, and biomarker discovery using biofluids is highlighted. Finally, the challenges and future requirements to overcome the current limitations for their effective clinical use are also discussed.
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Affiliation(s)
- Rui Vitorino
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal.,iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal.,Laqv/requimte, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Manisha Choudhury
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, Powai, India
| | - Sofia Guedes
- Laqv/requimte, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Rita Ferreira
- Laqv/requimte, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Visith Thongboonkerd
- Medical Proteomics Unit, Office for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | | | - Francisco Amado
- Laqv/requimte, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Sanjeeva Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai, Powai, India
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7
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Fuchs S, Kucklick M, Lehmann E, Beckmann A, Wilkens M, Kolte B, Mustafayeva A, Ludwig T, Diwo M, Wissing J, Jänsch L, Ahrens CH, Ignatova Z, Engelmann S. Towards the characterization of the hidden world of small proteins in Staphylococcus aureus, a proteogenomics approach. PLoS Genet 2021; 17:e1009585. [PMID: 34061833 PMCID: PMC8195425 DOI: 10.1371/journal.pgen.1009585] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 06/11/2021] [Accepted: 05/07/2021] [Indexed: 01/08/2023] Open
Abstract
Small proteins play essential roles in bacterial physiology and virulence, however, automated algorithms for genome annotation are often not yet able to accurately predict the corresponding genes. The accuracy and reliability of genome annotations, particularly for small open reading frames (sORFs), can be significantly improved by integrating protein evidence from experimental approaches. Here we present a highly optimized and flexible bioinformatics workflow for bacterial proteogenomics covering all steps from (i) generation of protein databases, (ii) database searches and (iii) peptide-to-genome mapping to (iv) visualization of results. We used the workflow to identify high quality peptide spectrum matches (PSMs) for small proteins (≤ 100 aa, SP100) in Staphylococcus aureus Newman. Protein extracts from S. aureus were subjected to different experimental workflows for protein digestion and prefractionation and measured with highly sensitive mass spectrometers. In total, 175 proteins with up to 100 aa (SP100) were identified. Out of these 24 (ranging from 9 to 99 aa) were novel and not contained in the used genome annotation.144 SP100 are highly conserved and were found in at least 50% of the publicly available S. aureus genomes, while 127 are additionally conserved in other staphylococci. Almost half of the identified SP100 were basic, suggesting a role in binding to more acidic molecules such as nucleic acids or phospholipids.
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Affiliation(s)
- Stephan Fuchs
- Robert Koch Institute, Methodenentwicklung und Forschungsinfrastruktur (MF), Berlin, Germany
| | - Martin Kucklick
- University of Technical Sciences Braunschweig, Institute for Microbiology, Braunschweig, Germany
- Helmholtz Center for Infection Research GmbH, Microbial Proteomics, Braunschweig, Germany
| | - Erik Lehmann
- University of Technical Sciences Braunschweig, Institute for Microbiology, Braunschweig, Germany
- Helmholtz Center for Infection Research GmbH, Microbial Proteomics, Braunschweig, Germany
| | - Alexander Beckmann
- University of Technical Sciences Braunschweig, Institute for Microbiology, Braunschweig, Germany
- Helmholtz Center for Infection Research GmbH, Microbial Proteomics, Braunschweig, Germany
| | - Maya Wilkens
- Robert Koch Institute, Methodenentwicklung und Forschungsinfrastruktur (MF), Berlin, Germany
- University of Technical Sciences Braunschweig, Institute for Microbiology, Braunschweig, Germany
- Helmholtz Center for Infection Research GmbH, Microbial Proteomics, Braunschweig, Germany
| | - Baban Kolte
- University of Hamburg, Institute of Biochemistry and Molecular Biology, Hamburg, Germany
| | - Ayten Mustafayeva
- University of Technical Sciences Braunschweig, Institute for Microbiology, Braunschweig, Germany
- Helmholtz Center for Infection Research GmbH, Microbial Proteomics, Braunschweig, Germany
| | - Tobias Ludwig
- University of Technical Sciences Braunschweig, Institute for Microbiology, Braunschweig, Germany
- Helmholtz Center for Infection Research GmbH, Microbial Proteomics, Braunschweig, Germany
| | - Maurice Diwo
- University of Technical Sciences Braunschweig, Institute for Microbiology, Braunschweig, Germany
- Helmholtz Center for Infection Research GmbH, Microbial Proteomics, Braunschweig, Germany
| | - Josef Wissing
- Helmholtz Center for Infection Research GmbH, Cellular Proteomics, Braunschweig, Germany
| | - Lothar Jänsch
- Helmholtz Center for Infection Research GmbH, Cellular Proteomics, Braunschweig, Germany
| | - Christian H Ahrens
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Zoya Ignatova
- University of Hamburg, Institute of Biochemistry and Molecular Biology, Hamburg, Germany
| | - Susanne Engelmann
- University of Technical Sciences Braunschweig, Institute for Microbiology, Braunschweig, Germany
- Helmholtz Center for Infection Research GmbH, Microbial Proteomics, Braunschweig, Germany
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8
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Abstract
Mass spectrometry (MS)-based proteomics is currently the most successful approach to measure and compare peptides and proteins in a large variety of biological samples. Modern mass spectrometers, equipped with high-resolution analyzers, provide large amounts of data output. This is the case of shotgun/bottom-up proteomics, which consists in the enzymatic digestion of protein into peptides that are then measured by MS-instruments through a data dependent acquisition (DDA) mode. Dedicated bioinformatic tools and platforms have been developed to face the increasing size and complexity of raw MS data that need to be processed and interpreted for large-scale protein identification and quantification. This chapter illustrates the most popular bioinformatics solution for the analysis of shotgun MS-proteomics data. A general description will be provided on the data preprocessing options and the different search engines available, including practical suggestions on how to optimize the parameters for peptide search, based on hands-on experience.
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Affiliation(s)
- Avinash Yadav
- Department of Experimental Oncology, European Institute of Oncology (IEO), IRCCS, Milan, Italy
| | - Federica Marini
- Department of Experimental Oncology, European Institute of Oncology (IEO), IRCCS, Milan, Italy
| | - Alessandro Cuomo
- Department of Experimental Oncology, European Institute of Oncology (IEO), IRCCS, Milan, Italy
| | - Tiziana Bonaldi
- Department of Experimental Oncology, European Institute of Oncology (IEO), IRCCS, Milan, Italy.
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Tariq MU, Haseeb M, Aledhari M, Razzak R, Parizi RM, Saeed F. Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 9:5497-5516. [PMID: 33537181 PMCID: PMC7853650 DOI: 10.1109/access.2020.3047588] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Big Data Proteogenomics lies at the intersection of high-throughput Mass Spectrometry (MS) based proteomics and Next Generation Sequencing based genomics. The combined and integrated analysis of these two high-throughput technologies can help discover novel proteins using genomic, and transcriptomic data. Due to the biological significance of integrated analysis, the recent past has seen an influx of proteogenomic tools that perform various tasks, including mapping proteins to the genomic data, searching experimental MS spectra against a six-frame translation genome database, and automating the process of annotating genome sequences. To date, most of such tools have not focused on scalability issues that are inherent in proteogenomic data analysis where the size of the database is much larger than a typical protein database. These state-of-the-art tools can take more than half a month to process a small-scale dataset of one million spectra against a genome of 3 GB. In this article, we provide an up-to-date review of tools that can analyze proteogenomic datasets, providing a critical analysis of the techniques' relative merits and potential pitfalls. We also point out potential bottlenecks and recommendations that can be incorporated in the future design of these workflows to ensure scalability with the increasing size of proteogenomic data. Lastly, we make a case of how high-performance computing (HPC) solutions may be the best bet to ensure the scalability of future big data proteogenomic data analysis.
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Affiliation(s)
- Muhammad Usman Tariq
- School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA
| | - Muhammad Haseeb
- School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA
| | - Mohammed Aledhari
- College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA
| | - Rehma Razzak
- College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA
| | - Reza M Parizi
- College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA
| | - Fahad Saeed
- School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA
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10
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Shukla N, Siva N, Malik B, Suravajhala P. Current Challenges and Implications of Proteogenomic Approaches in Prostate Cancer. Curr Top Med Chem 2020; 20:1968-1980. [PMID: 32703135 DOI: 10.2174/1568026620666200722112450] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/30/2020] [Accepted: 06/29/2020] [Indexed: 12/16/2022]
Abstract
In the recent past, next-generation sequencing (NGS) approaches have heralded the omics era. With NGS data burgeoning, there arose a need to disseminate the omic data better. Proteogenomics has been vividly used for characterising the functions of candidate genes and is applied in ascertaining various diseased phenotypes, including cancers. However, not much is known about the role and application of proteogenomics, especially Prostate Cancer (PCa). In this review, we outline the need for proteogenomic approaches, their applications and their role in PCa.
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Affiliation(s)
- Nidhi Shukla
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Statue Circle, Jaipur 302001, RJ, India.,Department of Chemistry, School of Basic Sciences, Manipal University Jaipur, Jaipur, India
| | - Narmadhaa Siva
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Statue Circle, Jaipur 302001, RJ, India
| | - Babita Malik
- Department of Chemistry, School of Basic Sciences, Manipal University Jaipur, Jaipur, India
| | - Prashanth Suravajhala
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Statue Circle, Jaipur 302001, RJ, India
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11
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Waas M, Littrell J, Gundry RL. CIRFESS: An Interactive Resource for Querying the Set of Theoretically Detectable Peptides for Cell Surface and Extracellular Enrichment Proteomic Studies. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:1389-1397. [PMID: 32212654 PMCID: PMC8116119 DOI: 10.1021/jasms.0c00021] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Cell surface transmembrane, extracellular, and secreted proteins are high value targets for immunophenotyping, drug development, and studies related to intercellular communication in health and disease. As the number of specific and validated affinity reagents that target this subproteome are limited, mass spectrometry (MS)-based approaches will continue to play a critical role in enabling discovery and quantitation of these molecules. Given the technical considerations that make MS-based cell surface proteome studies uniquely challenging, it can be difficult to select an appropriate experimental approach. To this end, we have integrated multiple prediction strategies and annotations into a single online resource, Compiled Interactive Resource for Extracellular and Surface Studies (CIRFESS). CIRFESS enables rapid interrogation of the human proteome to reveal the cell surface proteome theoretically detectable by current approaches and highlights where current prediction strategies provide concordant and discordant information. We applied CIRFESS to identify the percentage of various subsets of the proteome which are expected to be captured by targeted enrichment strategies, including two established methods and one that is possible but not yet demonstrated. These results will inform the selection of available proteomic strategies and development of new strategies to enhance coverage of the cell surface and extracellular proteome. CIRFESS is available at www.cellsurfer.net/cirfess.
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Affiliation(s)
- Matthew Waas
- CardiOmics Program, Center for Heart and Vascular Research, Division of Cardiovascular Medicine, and Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, United States
| | - Jack Littrell
- CardiOmics Program, Center for Heart and Vascular Research, Division of Cardiovascular Medicine, and Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, United States
| | - Rebekah L Gundry
- CardiOmics Program, Center for Heart and Vascular Research, Division of Cardiovascular Medicine, and Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, Nebraska 68198, United States
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12
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Waldron R, McGowan J, Gordon N, McCarthy C, Mitchell EB, Fitzpatrick DA. Proteome and allergenome of the European house dust mite Dermatophagoides pteronyssinus. PLoS One 2019; 14:e0216171. [PMID: 31042761 PMCID: PMC6493757 DOI: 10.1371/journal.pone.0216171] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 04/15/2019] [Indexed: 11/18/2022] Open
Abstract
The European house dust mite Dermatophagoides pteronyssinus is of significant medical importance as it is a major elicitor of allergic illnesses. In this analysis we have undertaken comprehensive bioinformatic and proteomic examination of Dermatophagoides pteronyssinus airmid, identified 12,530 predicted proteins and validated the expression of 4,002 proteins. Examination of homology between predicted proteins and allergens from other species revealed as much as 2.6% of the D. pteronyssinus airmid proteins may cause an allergenic response. Many of the potential allergens have evidence for expression (n = 259) and excretion (n = 161) making them interesting targets for future allergen studies. Comparative proteomic analysis of mite body and spent growth medium facilitated qualitative assessment of mite group allergen localisation. Protein extracts from house dust contain a substantial number of uncharacterised D. pteronyssinus proteins in addition to known and putative allergens. Novel D. pteronyssinus proteins were identified to be highly abundant both in house dust and laboratory cultures and included numerous carbohydrate active enzymes that may be involved in cuticle remodelling, bacteriophagy or mycophagy. These data may have clinical applications in the development of allergen-specific immunotherapy that mimic natural exposure. Using a phylogenomic approach utilising a supermatrix and supertree methodologies we also show that D. pteronyssinus is more closely related to Euroglyphus maynei than Dermatophagoides farinae.
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Affiliation(s)
- Rose Waldron
- Department of Biology, National University of Ireland Maynooth, Co. Kildare, Ireland
- Airmid Healthgroup Ltd., Trinity Enterprise Campus, Dublin, Ireland
| | - Jamie McGowan
- Department of Biology, National University of Ireland Maynooth, Co. Kildare, Ireland
- Human Health Research Institute, Maynooth University, Maynooth, Co. Kildare, Ireland
| | - Natasha Gordon
- Airmid Healthgroup Ltd., Trinity Enterprise Campus, Dublin, Ireland
| | - Charley McCarthy
- Department of Biology, National University of Ireland Maynooth, Co. Kildare, Ireland
- Human Health Research Institute, Maynooth University, Maynooth, Co. Kildare, Ireland
| | | | - David A. Fitzpatrick
- Department of Biology, National University of Ireland Maynooth, Co. Kildare, Ireland
- Human Health Research Institute, Maynooth University, Maynooth, Co. Kildare, Ireland
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13
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Hao Y, Zhang L, Niu Y, Cai T, Luo J, He S, Zhang B, Zhang D, Qin Y, Yang F, Chen R. SmProt: a database of small proteins encoded by annotated coding and non-coding RNA loci. Brief Bioinform 2019; 19:636-643. [PMID: 28137767 DOI: 10.1093/bib/bbx005] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Indexed: 11/12/2022] Open
Abstract
Small proteins is the general term for proteins with length shorter than 100 amino acids. Identification and functional studies of small proteins have advanced rapidly in recent years, and several studies have shown that small proteins play important roles in diverse functions including development, muscle contraction and DNA repair. Identification and characterization of previously unrecognized small proteins may contribute in important ways to cell biology and human health. Current databases are generally somewhat deficient in that they have either not collected small proteins systematically, or contain only predictions of small proteins in a limited number of tissues and species. Here, we present a specifically designed web-accessible database, small proteins database (SmProt, http://bioinfo.ibp.ac.cn/SmProt), which is a database documenting small proteins. The current release of SmProt incorporates 255 010 small proteins computationally or experimentally identified in 291 cell lines/tissues derived from eight popular species. The database provides a variety of data including basic information (sequence, location, gene name, organism, etc.) as well as specific information (experiment, function, disease type, etc.). To facilitate data extraction, SmProt supports multiple search options, including species, genome location, gene name and their aliases, cell lines/tissues, ORF type, gene type, PubMed ID and SmProt ID. SmProt also incorporates a service for the BLAST alignment search and provides a local UCSC Genome Browser. Additionally, SmProt defines a high-confidence set of small proteins and predicts the functions of the small proteins.
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Affiliation(s)
- Yajing Hao
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Lili Zhang
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Yiwei Niu
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Tanxi Cai
- Key Laboratory of Protein and Peptide Pharmaceuticals and Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Jianjun Luo
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Shunmin He
- Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Bao Zhang
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Dejiu Zhang
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Yan Qin
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Fuquan Yang
- Key Laboratory of Protein and Peptide Pharmaceuticals and Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Runsheng Chen
- Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
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14
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Guillot L, Delage L, Viari A, Vandenbrouck Y, Com E, Ritter A, Lavigne R, Marie D, Peterlongo P, Potin P, Pineau C. Peptimapper: proteogenomics workflow for the expert annotation of eukaryotic genomes. BMC Genomics 2019; 20:56. [PMID: 30654742 PMCID: PMC6337836 DOI: 10.1186/s12864-019-5431-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 01/03/2019] [Indexed: 01/02/2023] Open
Abstract
Background Accurate structural annotation of genomes is still a challenge, despite the progress made over the past decade. The prediction of gene structure remains difficult, especially for eukaryotic species, and is often erroneous and incomplete. We used a proteogenomics strategy, taking advantage of the combination of proteomics datasets and bioinformatics tools, to identify novel protein coding-genes and splice isoforms, assign correct start sites, and validate predicted exons and genes. Results Our proteogenomics workflow, Peptimapper, was applied to the genome annotation of Ectocarpus sp., a key reference genome for both the brown algal lineage and stramenopiles. We generated proteomics data from various life cycle stages of Ectocarpus sp. strains and sub-cellular fractions using a shotgun approach. First, we directly generated peptide sequence tags (PSTs) from the proteomics data. Second, we mapped PSTs onto the translated genomic sequence. Closely located hits (i.e., PSTs locations on the genome) were then clustered to detect potential coding regions based on parameters optimized for the organism. Third, we evaluated each cluster and compared it to gene predictions from existing conventional genome annotation approaches. Finally, we integrated cluster locations into GFF files to use a genome viewer. We identified two potential novel genes, a ribosomal protein L22 and an aryl sulfotransferase and corrected the gene structure of a dihydrolipoamide acetyltransferase. We experimentally validated the results by RT-PCR and using transcriptomics data. Conclusions Peptimapper is a complementary tool for the expert annotation of genomes. It is suitable for any organism and is distributed through a Docker image available on two public bioinformatics docker repositories: Docker Hub and BioShaDock. This workflow is also accessible through the Galaxy framework and for use by non-computer scientists at https://galaxy.protim.eu. Data are available via ProteomeXchange under identifier PXD010618. Electronic supplementary material The online version of this article (10.1186/s12864-019-5431-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Laetitia Guillot
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.,Protim, Univ Rennes, F-35042, Rennes cedex, France
| | - Ludovic Delage
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France
| | - Alain Viari
- INRIA Grenoble-Rhône-Alpes, F-38330, Montbonnot-Saint-Martin, France
| | - Yves Vandenbrouck
- University Grenoble Alpes, CEA, Inserm, BIG-BGE, 38000, Grenoble, France
| | - Emmanuelle Com
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.,Protim, Univ Rennes, F-35042, Rennes cedex, France
| | - Andrés Ritter
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France.,Present address: Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratory of Computational and Quantitative Biology, F-75005, Paris, France
| | - Régis Lavigne
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France.,Protim, Univ Rennes, F-35042, Rennes cedex, France
| | - Dominique Marie
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France
| | | | - Philippe Potin
- Sorbonne Université, UPMC, CNRS, UMR 8227, Integrative Biology of Marine Models, Biological Station, CS 90074, F-29688, Roscoff, France
| | - Charles Pineau
- Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35042, Rennes cedex, France. .,Protim, Univ Rennes, F-35042, Rennes cedex, France.
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15
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Lee PY, Chin SF, Low TY, Jamal R. Probing the colorectal cancer proteome for biomarkers: Current status and perspectives. J Proteomics 2018; 187:93-105. [PMID: 29953962 DOI: 10.1016/j.jprot.2018.06.014] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 06/13/2018] [Accepted: 06/23/2018] [Indexed: 02/07/2023]
Abstract
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Biomarkers that can facilitate better clinical management of CRC are in high demand to improve patient outcome and to reduce mortality. In this regard, proteomic analysis holds a promising prospect in the hunt of novel biomarkers for CRC and in understanding the mechanisms underlying tumorigenesis. This review aims to provide an overview of the current progress of proteomic research, focusing on discovery and validation of diagnostic biomarkers for CRC. We will summarize the contributions of proteomic strategies to recent discoveries of protein biomarkers for CRC and also briefly discuss the potential and challenges of different proteomic approaches in biomarker discovery and translational applications.
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Affiliation(s)
- Pey Yee Lee
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia.
| | - Siok-Fong Chin
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia
| | - Teck Yew Low
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia
| | - Rahman Jamal
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, 56000 Kuala Lumpur, Malaysia
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16
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Moolhuijzen P, See PT, Hane JK, Shi G, Liu Z, Oliver RP, Moffat CS. Comparative genomics of the wheat fungal pathogen Pyrenophora tritici-repentis reveals chromosomal variations and genome plasticity. BMC Genomics 2018; 19:279. [PMID: 29685100 PMCID: PMC5913888 DOI: 10.1186/s12864-018-4680-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 04/16/2018] [Indexed: 02/08/2023] Open
Abstract
Background Pyrenophora tritici-repentis (Ptr) is a necrotrophic fungal pathogen that causes the major wheat disease, tan spot. We set out to provide essential genomics-based resources in order to better understand the pathogenicity mechanisms of this important pathogen. Results Here, we present eight new Ptr isolate genomes, assembled and annotated; representing races 1, 2 and 5, and a new race. We report a high quality Ptr reference genome, sequenced by PacBio technology with Illumina paired-end data support and optical mapping. An estimated 98% of the genome coverage was mapped to 10 chromosomal groups, using a two-enzyme hybrid approach. The final reference genome was 40.9 Mb and contained a total of 13,797 annotated genes, supported by transcriptomic and proteogenomics data sets. Conclusions Whole genome comparative analysis revealed major chromosomal segmental rearrangements and fusions, highlighting intraspecific genome plasticity in this species. Furthermore, the Ptr race classification was not supported at the whole genome level, as phylogenetic analysis did not cluster the ToxA producing isolates. This expansion of available Ptr genomics resources will directly facilitate research aimed at controlling tan spot disease. Electronic supplementary material The online version of this article (10.1186/s12864-018-4680-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Paula Moolhuijzen
- Centre for Crop Disease and Management, Department of Environment and Agriculture, Curtin University, Bentley, Western Australia, Australia.
| | - Pao Theen See
- Centre for Crop Disease and Management, Department of Environment and Agriculture, Curtin University, Bentley, Western Australia, Australia
| | - James K Hane
- Centre for Crop Disease and Management, Department of Environment and Agriculture, Curtin University, Bentley, Western Australia, Australia
| | - Gongjun Shi
- Department of Plant Pathology, North Dakota State University, Fargo, ND, USA
| | - Zhaohui Liu
- Department of Plant Pathology, North Dakota State University, Fargo, ND, USA
| | - Richard P Oliver
- Centre for Crop Disease and Management, Department of Environment and Agriculture, Curtin University, Bentley, Western Australia, Australia
| | - Caroline S Moffat
- Centre for Crop Disease and Management, Department of Environment and Agriculture, Curtin University, Bentley, Western Australia, Australia
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17
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Li H, Xiao L, Zhang L, Wu J, Wei B, Sun N, Zhao Y. FSPP: A Tool for Genome-Wide Prediction of smORF-Encoded Peptides and Their Functions. Front Genet 2018; 9:96. [PMID: 29675032 PMCID: PMC5896265 DOI: 10.3389/fgene.2018.00096] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Accepted: 03/08/2018] [Indexed: 02/01/2023] Open
Abstract
smORFs are small open reading frames of less than 100 codons. Recent low throughput experiments showed a lot of smORF-encoded peptides (SEPs) played crucial rule in processes such as regulation of transcription or translation, transportation through membranes and the antimicrobial activity. In order to gather more functional SEPs, it is necessary to have access to genome-wide prediction tools to give profound directions for low throughput experiments. In this study, we put forward a functional smORF-encoded peptides predictor (FSPP) which tended to predict authentic SEPs and their functions in a high throughput method. FSPP used the overlap of detected SEPs from Ribo-seq and mass spectrometry as target objects. With the expression data on transcription and translation levels, FSPP built two co-expression networks. Combing co-location relations, FSPP constructed a compound network and then annotated SEPs with functions of adjacent nodes. Tested on 38 sequenced samples of 5 human cell lines, FSPP successfully predicted 856 out of 960 annotated proteins. Interestingly, FSPP also highlighted 568 functional SEPs from these samples. After comparison, the roles predicted by FSPP were consistent with known functions. These results suggest that FSPP is a reliable tool for the identification of functional small peptides. FSPP source code can be acquired at https://www.bioinfo.org/FSPP.
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Affiliation(s)
- Hui Li
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.,School of Computer and Control Engineering, University of Chinese Academy of Sciences (UCAS), Beijing, China
| | - Li Xiao
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Lili Zhang
- School of Computer and Control Engineering, University of Chinese Academy of Sciences (UCAS), Beijing, China.,CAS Key Laboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Jiarui Wu
- Department of Clinical Pharmacology of Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China
| | - Bin Wei
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Ninghui Sun
- State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Yi Zhao
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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18
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Chambers MC, Jagtap PD, Johnson JE, McGowan T, Kumar P, Onsongo G, Guerrero CR, Barsnes H, Vaudel M, Martens L, Grüning B, Cooke IR, Heydarian M, Reddy KL, Griffin TJ. An Accessible Proteogenomics Informatics Resource for Cancer Researchers. Cancer Res 2017; 77:e43-e46. [PMID: 29092937 DOI: 10.1158/0008-5472.can-17-0331] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 04/07/2017] [Accepted: 06/30/2017] [Indexed: 11/16/2022]
Abstract
Proteogenomics has emerged as a valuable approach in cancer research, which integrates genomic and transcriptomic data with mass spectrometry-based proteomics data to directly identify expressed, variant protein sequences that may have functional roles in cancer. This approach is computationally intensive, requiring integration of disparate software tools into sophisticated workflows, challenging its adoption by nonexpert, bench scientists. To address this need, we have developed an extensible, Galaxy-based resource aimed at providing more researchers access to, and training in, proteogenomic informatics. Our resource brings together software from several leading research groups to address two foundational aspects of proteogenomics: (i) generation of customized, annotated protein sequence databases from RNA-Seq data; and (ii) accurate matching of tandem mass spectrometry data to putative variants, followed by filtering to confirm their novelty. Directions for accessing software tools and workflows, along with instructional documentation, can be found at z.umn.edu/canresgithub. Cancer Res; 77(21); e43-46. ©2017 AACR.
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Affiliation(s)
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota
| | - Thomas McGowan
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota
| | - Praveen Kumar
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota.,Bioinformatics and Computational Biology Program, University of Minnesota-Rochester, Rochester, Minnesota
| | - Getiria Onsongo
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota
| | - Candace R Guerrero
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway.,Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Marc Vaudel
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway.,Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.,Department of Biochemistry, Ghent University, Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Björn Grüning
- Department of Computer Science, Albert-Ludwigs-University, Freiburg, Freiburg, Germany.,Center for Biological Systems Analysis (ZBSA), University of Freiburg, Freiburg, Germany
| | - Ira R Cooke
- Comparative Genomics Centre and Department of Molecular and Cell Biology, James Cook University, Queensland, Australia
| | | | - Karen L Reddy
- Department of Biological Chemistry, Center for Epigenetics and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota.
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19
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Omasits U, Varadarajan AR, Schmid M, Goetze S, Melidis D, Bourqui M, Nikolayeva O, Québatte M, Patrignani A, Dehio C, Frey JE, Robinson MD, Wollscheid B, Ahrens CH. An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics. Genome Res 2017; 27:2083-2095. [PMID: 29141959 PMCID: PMC5741054 DOI: 10.1101/gr.218255.116] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Accepted: 10/25/2017] [Indexed: 12/18/2022]
Abstract
Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations. Our strategy toward accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs (a modified six-frame translation considering alternative start codons) in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome. By extending the PeptideClassifier concept of unambiguous peptides for prokaryotes, close to 95% of the identifiable peptides imply one distinct protein, largely simplifying downstream analysis. Searching a comprehensive Bartonella henselae proteomics data set against such an iPtgxDB allowed us to unambiguously identify novel ORFs uniquely predicted by each resource, including lipoproteins, differentially expressed and membrane-localized proteins, novel start sites and wrongly annotated pseudogenes. Most novelties were confirmed by targeted, parallel reaction monitoring mass spectrometry, including unique ORFs and single amino acid variations (SAAVs) identified in a re-sequenced laboratory strain that are not present in its reference genome. We demonstrate the general applicability of our strategy for genomes with varying GC content and distinct taxonomic origin. We release iPtgxDBs for B. henselae, Bradyrhizobium diazoefficiens and Escherichia coli and the software to generate both proteogenomics search databases and integrated annotation files that can be viewed in a genome browser for any prokaryote.
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Affiliation(s)
- Ulrich Omasits
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
| | - Adithi R Varadarajan
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland.,Department of Health Sciences and Technology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology Zurich, CH-8093 Zurich, Switzerland
| | - Michael Schmid
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
| | - Sandra Goetze
- Department of Health Sciences and Technology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology Zurich, CH-8093 Zurich, Switzerland
| | - Damianos Melidis
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
| | - Marc Bourqui
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
| | - Olga Nikolayeva
- Institute for Molecular Life Sciences & SIB Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland
| | | | - Andrea Patrignani
- Functional Genomics Center Zurich, ETH & UZH Zurich, CH-8057 Zurich, Switzerland
| | | | - Juerg E Frey
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
| | - Mark D Robinson
- Institute for Molecular Life Sciences & SIB Swiss Institute of Bioinformatics, University of Zurich, CH-8057 Zurich, Switzerland
| | - Bernd Wollscheid
- Department of Health Sciences and Technology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology Zurich, CH-8093 Zurich, Switzerland
| | - Christian H Ahrens
- Agroscope, Research Group Molecular Diagnostics, Genomics and Bioinformatics & SIB Swiss Institute of Bioinformatics, CH-8820 Wädenswil, Switzerland
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20
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Dimitrakopoulos L, Prassas I, Diamandis EP, Charames GS. Onco-proteogenomics: Multi-omics level data integration for accurate phenotype prediction. Crit Rev Clin Lab Sci 2017; 54:414-432. [DOI: 10.1080/10408363.2017.1384446] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Lampros Dimitrakopoulos
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Ioannis Prassas
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
| | - Eleftherios P. Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Department of Clinical Biochemistry, University Health Network, Toronto, ON, Canada
| | - George S. Charames
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Joseph and Wolf Lebovic Health Complex, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
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21
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Menschaert G, David F. Proteogenomics from a bioinformatics angle: A growing field. MASS SPECTROMETRY REVIEWS 2017; 36:584-599. [PMID: 26670565 PMCID: PMC6101030 DOI: 10.1002/mas.21483] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 09/01/2015] [Indexed: 05/16/2023]
Abstract
Proteogenomics is a research area that combines areas as proteomics and genomics in a multi-omics setup using both mass spectrometry and high-throughput sequencing technologies. Currently, the main goals of the field are to aid genome annotation or to unravel the proteome complexity. Mass spectrometry based identifications of matching or homologues peptides can further refine gene models. Also, the identification of novel proteoforms is also made possible based on detection of novel translation initiation sites (cognate or near-cognate), novel transcript isoforms, sequence variation or novel (small) open reading frames in intergenic or un-translated genic regions by analyzing high-throughput sequencing data from RNAseq or ribosome profiling experiments. Other proteogenomics studies using a combination of proteomics and genomics techniques focus on antibody sequencing, the identification of immunogenic peptides or venom peptides. Over the years, a growing amount of bioinformatics tools and databases became available to help streamlining these cross-omics studies. Some of these solutions only help in specific steps of the proteogenomics studies, e.g. building custom sequence databases (based on next generation sequencing output) for mass spectrometry fragmentation spectrum matching. Over the last few years a handful integrative tools also became available that can execute complete proteogenomics analyses. Some of these are presented as stand-alone solutions, whereas others are implemented in a web-based framework such as Galaxy. In this review we aimed at sketching a comprehensive overview of all the bioinformatics solutions that are available for this growing research area. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:584-599, 2017.
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Affiliation(s)
- Gerben Menschaert
- Lab of Bioinformatics and Computational Genomics, Department of
Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience
Engineering, Ghent University, Ghent, Belgium
- To whom correspondence should be addressed. Tel:
+32 9 264 99 22; Fax: +32 9 264 6220;
| | - Fenyö David
- Center for Health Informatics and Bioinformatics and Department of
Biochemistry and Molecular Pharmacology, New York University School of Medicine, New
York, New York, USA
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22
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Wingo TS, Duong DM, Zhou M, Dammer EB, Wu H, Cutler DJ, Lah JJ, Levey AI, Seyfried NT. Integrating Next-Generation Genomic Sequencing and Mass Spectrometry To Estimate Allele-Specific Protein Abundance in Human Brain. J Proteome Res 2017; 16:3336-3347. [PMID: 28691493 DOI: 10.1021/acs.jproteome.7b00324] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Gene expression contributes to phenotypic traits and human disease. To date, comparatively less is known about regulators of protein abundance, which is also under genetic control and likely influences clinical phenotypes. However, identifying and quantifying allele-specific protein abundance by bottom-up proteomics is challenging since single nucleotide variants (SNVs) that alter protein sequence are not considered in standard human protein databases. To address this, we developed the GenPro software and used it to create personalized protein databases (PPDs) to identify single amino acid variants (SAAVs) at the protein level from whole exome sequencing. In silico assessment of PPDs generated by GenPro revealed only a 1% increase in tryptic search space compared to a direct translation of all human transcripts and an equivalent search space compared to the UniProtKB reference database. To identify a large unbiased number of SAAV peptides, we performed high-resolution mass spectrometry-based proteomics for two human post-mortem brain samples and searched the collected MS/MS spectra against their respective PPD. We found an average of ∼117 000 unique peptides mapping to ∼9300 protein groups for each sample, and of these, 977 were unique variant peptides. We found that over 400 reference and SAAV peptide pairs were, on average, equally abundant in human brain by label-free ion intensity measurements and confirmed the absolute levels of three reference and SAAV peptide pairs using heavy labeled peptides standards coupled with parallel reaction monitoring (PRM). Our results highlight the utility of integrating genomic and proteomic sequencing data to identify sample-specific SAAV peptides and support the hypothesis that most alleles are equally expressed in human brain.
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Affiliation(s)
- Thomas S Wingo
- Division of Neurology, Department of Veterans Affairs Medical Center , Decatur, Georgia 30033, United States
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23
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Hernandez-Valladares M, Vaudel M, Selheim F, Berven F, Bruserud Ø. Proteogenomics approaches for studying cancer biology and their potential in the identification of acute myeloid leukemia biomarkers. Expert Rev Proteomics 2017; 14:649-663. [DOI: 10.1080/14789450.2017.1352474] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Maria Hernandez-Valladares
- Department of Clinical Science, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway
- Proteomics Unit, Department of Biomedicine, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway
| | - Marc Vaudel
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway
- Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway
| | - Frode Selheim
- Proteomics Unit, Department of Biomedicine, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway
| | - Frode Berven
- Proteomics Unit, Department of Biomedicine, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway
| | - Øystein Bruserud
- Department of Clinical Science, Faculty of Medicine and Dentistry, University of Bergen, Bergen, Norway
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24
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Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines. Genome Med 2017; 9:62. [PMID: 28716134 PMCID: PMC5514513 DOI: 10.1186/s13073-017-0454-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 06/22/2017] [Indexed: 02/07/2023] Open
Abstract
Background Onco-proteogenomics aims to understand how changes in a cancer’s genome influences its proteome. One challenge in integrating these molecular data is the identification of aberrant protein products from mass-spectrometry (MS) datasets, as traditional proteomic analyses only identify proteins from a reference sequence database. Methods We established proteomic workflows to detect peptide variants within MS datasets. We used a combination of publicly available population variants (dbSNP and UniProt) and somatic variations in cancer (COSMIC) along with sample-specific genomic and transcriptomic data to examine proteome variation within and across 59 cancer cell-lines. Results We developed a set of recommendations for the detection of variants using three search algorithms, a split target-decoy approach for FDR estimation, and multiple post-search filters. We examined 7.3 million unique variant tryptic peptides not found within any reference proteome and identified 4771 mutations corresponding to somatic and germline deviations from reference proteomes in 2200 genes among the NCI60 cell-line proteomes. Conclusions We discuss in detail the technical and computational challenges in identifying variant peptides by MS and show that uncovering these variants allows the identification of druggable mutations within important cancer genes. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0454-9) contains supplementary material, which is available to authorized users.
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25
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26
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Fu S, Liu X, Luo M, Xie K, Nice EC, Zhang H, Huang C. Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification. Expert Rev Proteomics 2017; 14:351-362. [PMID: 28276747 DOI: 10.1080/14789450.2017.1299006] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
INTRODUCTION Chemoresistance is a major obstacle for current cancer treatment. Proteogenomics is a powerful multi-omics research field that uses customized protein sequence databases generated by genomic and transcriptomic information to identify novel genes (e.g. noncoding, mutation and fusion genes) from mass spectrometry-based proteomic data. By identifying aberrations that are differentially expressed between tumor and normal pairs, this approach can also be applied to validate protein variants in cancer, which may reveal the response to drug treatment. Areas covered: In this review, we will present recent advances in proteogenomic investigations of cancer drug resistance with an emphasis on integrative proteogenomic pipelines and the biomarker discovery which contributes to achieving the goal of using precision/personalized medicine for cancer treatment. Expert commentary: The discovery and comprehensive understanding of potential biomarkers help identify the cohort of patients who may benefit from particular treatments, and will assist real-time clinical decision-making to maximize therapeutic efficacy and minimize adverse effects. With the development of MS-based proteomics and NGS-based sequencing, a growing number of proteogenomic tools are being developed specifically to investigate cancer drug resistance.
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Affiliation(s)
- Shuyue Fu
- a State Key Laboratory of Biotherapy and Cancer Center , West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy , Chengdu , P.R. China
| | - Xiang Liu
- b Department of Pathology , Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital , Chengdu , P.R. China
| | - Maochao Luo
- c West China School of Public Health, Sichuan University , Chengdu , P.R.China
| | - Ke Xie
- d Department of Oncology , Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital , Chengdu , P.R. China
| | - Edouard C Nice
- e Department of Biochemistry and Molecular Biology , Monash University , Clayton , Australia
| | - Haiyuan Zhang
- f School of Medicine , Yangtze University , P. R. China
| | - Canhua Huang
- a State Key Laboratory of Biotherapy and Cancer Center , West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy , Chengdu , P.R. China
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27
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Kumar D, Yadav AK, Dash D. Choosing an Optimal Database for Protein Identification from Tandem Mass Spectrometry Data. Methods Mol Biol 2017; 1549:17-29. [PMID: 27975281 DOI: 10.1007/978-1-4939-6740-7_3] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Database searching is the preferred method for protein identification from digital spectra of mass to charge ratios (m/z) detected for protein samples through mass spectrometers. The search database is one of the major influencing factors in discovering proteins present in the sample and thus in deriving biological conclusions. In most cases the choice of search database is arbitrary. Here we describe common search databases used in proteomic studies and their impact on final list of identified proteins. We also elaborate upon factors like composition and size of the search database that can influence the protein identification process. In conclusion, we suggest that choice of the database depends on the type of inferences to be derived from proteomics data. However, making additional efforts to build a compact and concise database for a targeted question should generally be rewarding in achieving confident protein identifications.
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Affiliation(s)
- Dhirendra Kumar
- G.N. Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, Mathura Road, Delhi, 110025, India
| | - Amit Kumar Yadav
- G.N. Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, Mathura Road, Delhi, 110025, India
| | - Debasis Dash
- G.N. Ramachandran Knowledge Centre for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, Mathura Road, Delhi, 110025, India.
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28
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29
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Prasad TSK, Mohanty AK, Kumar M, Sreenivasamurthy SK, Dey G, Nirujogi RS, Pinto SM, Madugundu AK, Patil AH, Advani J, Manda SS, Gupta MK, Dwivedi SB, Kelkar DS, Hall B, Jiang X, Peery A, Rajagopalan P, Yelamanchi SD, Solanki HS, Raja R, Sathe GJ, Chavan S, Verma R, Patel KM, Jain AP, Syed N, Datta KK, Khan AA, Dammalli M, Jayaram S, Radhakrishnan A, Mitchell CJ, Na CH, Kumar N, Sinnis P, Sharakhov IV, Wang C, Gowda H, Tu Z, Kumar A, Pandey A. Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes. Genome Res 2016; 27:133-144. [PMID: 28003436 PMCID: PMC5204337 DOI: 10.1101/gr.201368.115] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Accepted: 11/10/2016] [Indexed: 01/05/2023]
Abstract
Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted “noncoding RNAs” to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.
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Affiliation(s)
- T S Keshava Prasad
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India.,NIMHANS-IOB Proteomics and Bioinformatics Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka 560029, India
| | - Ajeet Kumar Mohanty
- National Institute of Malaria Research, Field Station, Goa 403001, India.,Department of Zoology, Goa University, Taleigao Plateau, Goa 403206, India
| | - Manish Kumar
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Sreelakshmi K Sreenivasamurthy
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Gourav Dey
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Raja Sekhar Nirujogi
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Centre for Bioinformatics, Pondicherry University, Puducherry 605014, India
| | - Sneha M Pinto
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India
| | - Anil K Madugundu
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Centre for Bioinformatics, Pondicherry University, Puducherry 605014, India
| | - Arun H Patil
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Jayshree Advani
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Srikanth S Manda
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Centre for Bioinformatics, Pondicherry University, Puducherry 605014, India
| | - Manoj Kumar Gupta
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Sutopa B Dwivedi
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Dhanashree S Kelkar
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Brantley Hall
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Xiaofang Jiang
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Ashley Peery
- Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Pavithra Rajagopalan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Soujanya D Yelamanchi
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Hitendra S Solanki
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Remya Raja
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Gajanan J Sathe
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Sandip Chavan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Renu Verma
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Krishna M Patel
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Ankit P Jain
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Nazia Syed
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Department of Biochemistry and Molecular Biology, Pondicherry University, Puducherry 605014, India
| | - Keshava K Datta
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Aafaque Ahmed Khan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Manjunath Dammalli
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Department of Biotechnology, Siddaganga Institute of Technology, Tumkur, Karnataka 572103, India
| | - Savita Jayaram
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Aneesha Radhakrishnan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Department of Biochemistry and Molecular Biology, Pondicherry University, Puducherry 605014, India
| | - Christopher J Mitchell
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Chan-Hyun Na
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland 21205, USA
| | - Nirbhay Kumar
- Department of Tropical Medicine, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana 70112, USA
| | - Photini Sinnis
- Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
| | - Igor V Sharakhov
- Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Charles Wang
- Center for Genomics and Department of Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, California 92350, USA
| | - Harsha Gowda
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India
| | - Zhijian Tu
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Ashwani Kumar
- National Institute of Malaria Research, Field Station, Goa 403001, India
| | - Akhilesh Pandey
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
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30
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Weisser H, Wright JC, Mudge JM, Gutenbrunner P, Choudhary JS. Flexible Data Analysis Pipeline for High-Confidence Proteogenomics. J Proteome Res 2016; 15:4686-4695. [PMID: 27786492 PMCID: PMC5703597 DOI: 10.1021/acs.jproteome.6b00765] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Proteogenomics leverages information
derived from proteomic data
to improve genome annotations. Of particular interest are “novel”
peptides that provide direct evidence of protein expression for genomic
regions not previously annotated as protein-coding. We present a modular,
automated data analysis pipeline aimed at detecting such “novel”
peptides in proteomic data sets. This pipeline implements criteria
developed by proteomics and genome annotation experts for high-stringency
peptide identification and filtering. Our pipeline is based on the
OpenMS computational framework; it incorporates multiple database
search engines for peptide identification and applies a machine-learning
approach (Percolator) to post-process search results. We describe
several new and improved software tools that we developed to facilitate
proteogenomic analyses that enhance the wealth of tools provided by
OpenMS. We demonstrate the application of our pipeline to a human
testis tissue data set previously acquired for the Chromosome-Centric
Human Proteome Project, which led to the addition of five new gene
annotations on the human reference genome.
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Affiliation(s)
| | | | | | - Petra Gutenbrunner
- School of Informatics, Communications, and Media, University of Applied Sciences Upper Austria , Hagenberg 4232, Austria
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Zhang J, Yang MK, Zeng H, Ge F. GAPP: A Proteogenomic Software for Genome Annotation and Global Profiling of Post-translational Modifications in Prokaryotes. Mol Cell Proteomics 2016; 15:3529-3539. [PMID: 27630248 DOI: 10.1074/mcp.m116.060046] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Indexed: 11/06/2022] Open
Abstract
Although the number of sequenced prokaryotic genomes is growing rapidly, experimentally verified annotation of prokaryotic genome remains patchy and challenging. To facilitate genome annotation efforts for prokaryotes, we developed an open source software called GAPP for genome annotation and global profiling of post-translational modifications (PTMs) in prokaryotes. With a single command, it provides a standard workflow to validate and refine predicted genetic models and discover diverse PTM events. We demonstrated the utility of GAPP using proteomic data from Helicobacter pylori, one of the major human pathogens that is responsible for many gastric diseases. Our results confirmed 84.9% of the existing predicted H. pylori proteins, identified 20 novel protein coding genes, and corrected four existing gene models with regard to translation initiation sites. In particular, GAPP revealed a large repertoire of PTMs using the same proteomic data and provided a rich resource that can be used to examine the functions of reversible modifications in this human pathogen. This software is a powerful tool for genome annotation and global discovery of PTMs and is applicable to any sequenced prokaryotic organism; we expect that it will become an integral part of ongoing genome annotation efforts for prokaryotes. GAPP is freely available at https://sourceforge.net/projects/gappproteogenomic/.
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Affiliation(s)
- Jia Zhang
- From the ‡Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Ming-Kun Yang
- From the ‡Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Honghui Zeng
- §Wuhan Branch, Supercomputing Center, Chinese Academy of Sciences, China
| | - Feng Ge
- From the ‡Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China; .,§Wuhan Branch, Supercomputing Center, Chinese Academy of Sciences, China
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Kumar D, Bansal G, Narang A, Basak T, Abbas T, Dash D. Integrating transcriptome and proteome profiling: Strategies and applications. Proteomics 2016; 16:2533-2544. [PMID: 27343053 DOI: 10.1002/pmic.201600140] [Citation(s) in RCA: 107] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 06/12/2016] [Accepted: 06/23/2016] [Indexed: 12/17/2022]
Abstract
Discovering the gene expression signature associated with a cellular state is one of the basic quests in majority of biological studies. For most of the clinical and cellular manifestations, these molecular differences may be exhibited across multiple layers of gene regulation like genomic variations, gene expression, protein translation and post-translational modifications. These system wide variations are dynamic in nature and their crosstalk is overwhelmingly complex, thus analyzing them separately may not be very informative. This necessitates the integrative analysis of such multiple layers of information to understand the interplay of the individual components of the biological system. Recent developments in high throughput RNA sequencing and mass spectrometric (MS) technologies to probe transcripts and proteins made these as preferred methods for understanding global gene regulation. Subsequently, improvements in "big-data" analysis techniques enable novel conclusions to be drawn from integrative transcriptomic-proteomic analysis. The unified analyses of both these data types have been rewarding for several biological objectives like improving genome annotation, predicting RNA-protein quantities, deciphering gene regulations, discovering disease markers and drug targets. There are different ways in which transcriptomics and proteomics data can be integrated; each aiming for different research objectives. Here, we review various studies, approaches and computational tools targeted for integrative analysis of these two high-throughput omics methods.
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Affiliation(s)
- Dhirendra Kumar
- G.N. Ramachandran Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, INDIA
| | - Gourja Bansal
- G.N. Ramachandran Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, INDIA
| | - Ankita Narang
- G.N. Ramachandran Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, INDIA
| | - Trayambak Basak
- G.N. Ramachandran Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, INDIA.,Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB South Campus, New Delhi, India
| | - Tahseen Abbas
- G.N. Ramachandran Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, INDIA.,Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB South Campus, New Delhi, India
| | - Debasis Dash
- G.N. Ramachandran Knowledge Center for Genome Informatics, CSIR-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, INDIA. , .,Academy of Scientific & Innovative Research (AcSIR), CSIR-IGIB South Campus, New Delhi, India. ,
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Li Y, Wang X, Cho JH, Shaw TI, Wu Z, Bai B, Wang H, Zhou S, Beach TG, Wu G, Zhang J, Peng J. JUMPg: An Integrative Proteogenomics Pipeline Identifying Unannotated Proteins in Human Brain and Cancer Cells. J Proteome Res 2016; 15:2309-20. [PMID: 27225868 DOI: 10.1021/acs.jproteome.6b00344] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Proteogenomics is an emerging approach to improve gene annotation and interpretation of proteomics data. Here we present JUMPg, an integrative proteogenomics pipeline including customized database construction, tag-based database search, peptide-spectrum match filtering, and data visualization. JUMPg creates multiple databases of DNA polymorphisms, mutations, splice junctions, partially trypticity, as well as protein fragments translated from the whole transcriptome in all six frames upon RNA-seq de novo assembly. We use a multistage strategy to search these databases sequentially, in which the performance is optimized by re-searching only unmatched high-quality spectra and reusing amino acid tags generated by the JUMP search engine. The identified peptides/proteins are displayed with gene loci using the UCSC genome browser. Then, the JUMPg program is applied to process a label-free mass spectrometry data set of Alzheimer's disease postmortem brain, uncovering 496 new peptides of amino acid substitutions, alternative splicing, frame shift, and "non-coding gene" translation. The novel protein PNMA6BL specifically expressed in the brain is highlighted. We also tested JUMPg to analyze a stable-isotope labeled data set of multiple myeloma cells, revealing 991 sample-specific peptides that include protein sequences in the immunoglobulin light chain variable region. Thus, the JUMPg program is an effective proteogenomics tool for multiomics data integration.
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Affiliation(s)
| | | | | | | | | | | | - Hong Wang
- Integrated Biomedical Sciences Program, University of Tennessee Health Science Center , 920 Madison Avenue, Memphis, Tennessee 38163, United States
| | | | - Thomas G Beach
- Banner Sun Health Research Institute , Sun City, Arizona 85351, United States
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Sheynkman GM, Shortreed MR, Cesnik AJ, Smith LM. Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2016; 9:521-45. [PMID: 27049631 PMCID: PMC4991544 DOI: 10.1146/annurev-anchem-071015-041722] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Mass spectrometry-based proteomics has emerged as the leading method for detection, quantification, and characterization of proteins. Nearly all proteomic workflows rely on proteomic databases to identify peptides and proteins, but these databases typically contain a generic set of proteins that lack variations unique to a given sample, precluding their detection. Fortunately, proteogenomics enables the detection of such proteomic variations and can be defined, broadly, as the use of nucleotide sequences to generate candidate protein sequences for mass spectrometry database searching. Proteogenomics is experiencing heightened significance due to two developments: (a) advances in DNA sequencing technologies that have made complete sequencing of human genomes and transcriptomes routine, and (b) the unveiling of the tremendous complexity of the human proteome as expressed at the levels of genes, cells, tissues, individuals, and populations. We review here the field of human proteogenomics, with an emphasis on its history, current implementations, the types of proteomic variations it reveals, and several important applications.
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Affiliation(s)
- Gloria M Sheynkman
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215;
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706; ,
| | - Michael R Shortreed
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706; ,
| | - Anthony J Cesnik
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706; ,
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706; ,
- Genome Center of Wisconsin, University of Wisconsin, Madison, Wisconsin 53706;
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Next Generation Sequencing Data and Proteogenomics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 926:11-19. [DOI: 10.1007/978-3-319-42316-6_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Proteogenomic Tools and Approaches to Explore Protein Coding Landscapes of Eukaryotic Genomes. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 926:1-10. [DOI: 10.1007/978-3-319-42316-6_1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Vaudel M, Barsnes H, Ræder H, Berven FS. Using Proteomics Bioinformatics Tools and Resources in Proteogenomic Studies. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 926:65-75. [DOI: 10.1007/978-3-319-42316-6_5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Abstract
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Every
molecular player in the cast of biology’s central
dogma is being sequenced and quantified with increasing ease and coverage.
To bring the resulting genomic, transcriptomic, and proteomic data
sets into coherence, tools must be developed that do not constrain
data acquisition and analytics in any way but rather provide simple
links across previously acquired data sets with minimal preprocessing
and hassle. Here we present such a tool: PGx, which supports proteogenomic
integration of mass spectrometry proteomics data with next-generation
sequencing by mapping identified peptides onto their putative genomic
coordinates.
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Affiliation(s)
- Manor Askenazi
- Biomedical Hosting LLC, 33 Lewis Avenue, Arlington, Massachusetts 02474, United States
| | - Kelly V Ruggles
- NYU Langone Medical Center , 227 East 30th Street, New York, New York 10016, United States
| | - David Fenyö
- NYU Langone Medical Center , 227 East 30th Street, New York, New York 10016, United States
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Mayne J, Ning Z, Zhang X, Starr AE, Chen R, Deeke S, Chiang CK, Xu B, Wen M, Cheng K, Seebun D, Star A, Moore JI, Figeys D. Bottom-Up Proteomics (2013-2015): Keeping up in the Era of Systems Biology. Anal Chem 2015; 88:95-121. [PMID: 26558748 DOI: 10.1021/acs.analchem.5b04230] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Janice Mayne
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Zhibin Ning
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Xu Zhang
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Amanda E Starr
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Rui Chen
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Shelley Deeke
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Cheng-Kang Chiang
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Bo Xu
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Ming Wen
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Kai Cheng
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Deeptee Seebun
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Alexandra Star
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Jasmine I Moore
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
| | - Daniel Figeys
- Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology and Immunology, University of Ottawa , 451 Smyth Rd., Ottawa, Ontario, Canada , K1H8M5
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Low TY, Heck AJ. Reconciling proteomics with next generation sequencing. Curr Opin Chem Biol 2015; 30:14-20. [PMID: 26590485 DOI: 10.1016/j.cbpa.2015.10.023] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 10/22/2015] [Indexed: 11/19/2022]
Abstract
Both genomics and proteomics technologies have matured in the last decade to a level where they are able to deliver system-wide data on the qualitative and quantitative abundance of their respective molecular entities, that is DNA/RNA and proteins. A next logical step is the collective use of these technologies, ideally gathering data on matching samples. The first large scale so-called proteogenomics studies are emerging, and display the benefits each of these layers of analysis has on the other layers to together generate more meaningful insight into the connection between the phenotype/physiology and genotype of the system under study. Here we review a selected number of these studies, highlighting what they can uniquely deliver. We also discuss the future potential and remaining challenges, from a somewhat proteome biased perspective.
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Affiliation(s)
- Teck Yew Low
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands; Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Albert Jr Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands; Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands.
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Kumar D, Yadav AK, Jia X, Mulvenna J, Dash D. Integrated Transcriptomic-Proteomic Analysis Using a Proteogenomic Workflow Refines Rat Genome Annotation. Mol Cell Proteomics 2015; 15:329-39. [PMID: 26560066 DOI: 10.1074/mcp.m114.047126] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2014] [Indexed: 11/06/2022] Open
Abstract
Proteogenomic re-annotation and mRNA splicing information can lead to the discovery of various protein forms for eukaryotic model organisms like rat. However, detection of novel proteoforms using mass spectrometry proteomics data remains a formidable challenge. We developed EuGenoSuite, an open source multiple algorithmic proteomic search tool and utilized it in our in-house integrated transcriptomic-proteomic pipeline to facilitate automated proteogenomic analysis. Using four proteogenomic pipelines (integrated transcriptomic-proteomic, Peppy, Enosi, and ProteoAnnotator) on publicly available RNA-sequence and MS proteomics data, we discovered 363 novel peptides in rat brain microglia representing novel proteoforms for 249 gene loci in the rat genome. These novel peptides aided in the discovery of novel exons, translation of annotated untranslated regions, pseudogenes, and splice variants for various loci; many of which have known disease associations, including neurological disorders like schizophrenia, amyotrophic lateral sclerosis, etc. Novel isoforms were also discovered for genes implicated in cardiovascular diseases and breast cancer for which rats are considered model organisms. Our integrative multi-omics data analysis not only enables the discovery of new proteoforms but also generates an improved reference for human disease studies in the rat model.
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Affiliation(s)
- Dhirendra Kumar
- From the ‡G. N. Ramachandran Knowledge Centre for Genome Informatics, Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, India
| | - Amit Kumar Yadav
- From the ‡G. N. Ramachandran Knowledge Centre for Genome Informatics, Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, India
| | - Xinying Jia
- ¶Infectious Diseases Program, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Jason Mulvenna
- ¶Infectious Diseases Program, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Debasis Dash
- From the ‡G. N. Ramachandran Knowledge Centre for Genome Informatics, Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, India;
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Tabb DL. The SEQUEST family tree. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2015; 26:1814-9. [PMID: 26122518 PMCID: PMC4607603 DOI: 10.1007/s13361-015-1201-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 05/14/2015] [Accepted: 05/19/2015] [Indexed: 06/04/2023]
Abstract
Since its introduction in 1994, SEQUEST has gained many important new capabilities, and a host of successor algorithms have built upon its successes. This Account and Perspective maps the evolution of this important tool and charts the relationships among contributions to the SEQUEST legacy. Many of the changes represented improvements in computing speed by clusters and graphics cards. Mass spectrometry innovations in mass accuracy and activation methods led to shifts in fragment modeling and scoring strategies. These changes, as well as the movement of laboratories and lab members, have led to great diversity among the members of the SEQUEST family. Graphical Abstract ᅟ.
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Affiliation(s)
- David L Tabb
- School of Medicine, Vanderbilt University, Nashville, TN, 37232-8575, USA.
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Heyer R, Kohrs F, Reichl U, Benndorf D. Metaproteomics of complex microbial communities in biogas plants. Microb Biotechnol 2015; 8:749-63. [PMID: 25874383 PMCID: PMC4554464 DOI: 10.1111/1751-7915.12276] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Revised: 02/05/2015] [Accepted: 02/11/2015] [Indexed: 11/29/2022] Open
Abstract
Production of biogas from agricultural biomass or organic wastes is an important source of renewable energy. Although thousands of biogas plants (BGPs) are operating in Germany, there is still a significant potential to improve yields, e.g. from fibrous substrates. In addition, process stability should be optimized. Besides evaluating technical measures, improving our understanding of microbial communities involved into the biogas process is considered as key issue to achieve both goals. Microscopic and genetic approaches to analyse community composition provide valuable experimental data, but fail to detect presence of enzymes and overall metabolic activity of microbial communities. Therefore, metaproteomics can significantly contribute to elucidate critical steps in the conversion of biomass to methane as it delivers combined functional and phylogenetic data. Although metaproteomics analyses are challenged by sample impurities, sample complexity and redundant protein identification, and are still limited by the availability of genome sequences, recent studies have shown promising results. In the following, the workflow and potential pitfalls for metaproteomics of samples from full-scale BGP are discussed. In addition, the value of metaproteomics to contribute to the further advancement of microbial ecology is evaluated. Finally, synergistic effects expected when metaproteomics is combined with advanced imaging techniques, metagenomics, metatranscriptomics and metabolomics are addressed.
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Affiliation(s)
- Robert Heyer
- Bioprocess Engineering, Otto von Guericke University MagdeburgUniversitätsplatz 2, Magdeburg, 39106, Germany
- Max Planck Institute for Dynamics of Complex Technical SystemsSandtorstr. 1, Magdeburg, 39106, Germany
| | - Fabian Kohrs
- Bioprocess Engineering, Otto von Guericke University MagdeburgUniversitätsplatz 2, Magdeburg, 39106, Germany
- Max Planck Institute for Dynamics of Complex Technical SystemsSandtorstr. 1, Magdeburg, 39106, Germany
| | - Udo Reichl
- Bioprocess Engineering, Otto von Guericke University MagdeburgUniversitätsplatz 2, Magdeburg, 39106, Germany
- Max Planck Institute for Dynamics of Complex Technical SystemsSandtorstr. 1, Magdeburg, 39106, Germany
| | - Dirk Benndorf
- Bioprocess Engineering, Otto von Guericke University MagdeburgUniversitätsplatz 2, Magdeburg, 39106, Germany
- Max Planck Institute for Dynamics of Complex Technical SystemsSandtorstr. 1, Magdeburg, 39106, Germany
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44
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Onco-proteogenomics: cancer proteomics joins forces with genomics. Nat Methods 2015; 11:1107-13. [PMID: 25357240 DOI: 10.1038/nmeth.3138] [Citation(s) in RCA: 106] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2013] [Accepted: 06/26/2014] [Indexed: 12/21/2022]
Abstract
The complexities of tumor genomes are rapidly being uncovered, but how they are regulated into functional proteomes remains poorly understood. Standard proteomics workflows use databases of known proteins, but these databases do not capture the uniqueness of the cancer transcriptome, with its point mutations, unusual splice variants and gene fusions. Onco-proteogenomics integrates mass spectrometry-generated data with genomic information to identify tumor-specific peptides. Linking tumor-derived DNA, RNA and protein measurements into a central-dogma perspective has the potential to improve our understanding of cancer biology.
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45
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Uszkoreit J, Plohnke N, Rexroth S, Marcus K, Eisenacher M. The bacterial proteogenomic pipeline. BMC Genomics 2014; 15 Suppl 9:S19. [PMID: 25521444 PMCID: PMC4290607 DOI: 10.1186/1471-2164-15-s9-s19] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Background Proteogenomics combines the cutting-edge methods from genomics and proteomics. While it has become cheap to sequence whole genomes, the correct annotation of protein coding regions in the genome is still tedious and error prone. Mass spectrometry on the other hand relies on good characterizations of proteins derived from the genome, but can also be used to help improving the annotation of genomes or find species specific peptides. Additionally, proteomics is widely used to find evidence for differential expression of proteins under different conditions, e.g. growth conditions for bacteria. The concept of proteogenomics is not altogether new, in-house scripts are used by different labs and some special tools for eukaryotic and human analyses are available. Results The Bacterial Proteogenomic Pipeline, which is completely written in Java, alleviates the conducting of proteogenomic analyses of bacteria. From a given genome sequence, a naïve six frame translation is performed and, if desired, a decoy database generated. This database is used to identify MS/MS spectra by common peptide identification algorithms. After combination of the search results and optional flagging for different experimental conditions, the results can be browsed and further inspected. In particular, for each peptide the number of identifications for each condition and the positions in the corresponding protein sequences are shown. Intermediate and final results can be exported into GFF3 format for visualization in common genome browsers. Conclusions To facilitate proteogenomics analyses the Bacterial Proteogenomic Pipeline is a set of comprehensive tools running on common desktop computers, written in Java and thus platform independent. The pipeline allows integrating peptide identifications from various algorithms and emphasizes the visualization of spectral counts from different experimental conditions.
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46
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Ghali F, Krishna R, Perkins S, Collins A, Xia D, Wastling J, Jones AR. ProteoAnnotator - Open source proteogenomics annotation software supporting PSI standards. Proteomics 2014; 14:2731-41. [DOI: 10.1002/pmic.201400265] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Revised: 09/10/2014] [Accepted: 10/02/2014] [Indexed: 11/08/2022]
Affiliation(s)
- Fawaz Ghali
- Institute of Integrative Biology; University of Liverpool; Liverpool UK
| | - Ritesh Krishna
- Institute of Integrative Biology; University of Liverpool; Liverpool UK
| | - Simon Perkins
- Institute of Integrative Biology; University of Liverpool; Liverpool UK
| | - Andrew Collins
- Institute of Integrative Biology; University of Liverpool; Liverpool UK
| | - Dong Xia
- Department of Infection Biology; Institute of Infection and Global Health; University of Liverpool; Liverpool UK
| | - Jonathan Wastling
- Department of Infection Biology; Institute of Infection and Global Health; University of Liverpool; Liverpool UK
- Health Protection Research Unit in Emerging and Zoonotic Infections; The National Institute for Health Research; University of Liverpool; Liverpool UK
| | - Andrew R. Jones
- Institute of Integrative Biology; University of Liverpool; Liverpool UK
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Kucharova V, Wiker HG. Proteogenomics in microbiology: taking the right turn at the junction of genomics and proteomics. Proteomics 2014; 14:2360-675. [PMID: 25263021 DOI: 10.1002/pmic.201400168] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Revised: 08/18/2014] [Accepted: 09/23/2014] [Indexed: 12/14/2022]
Abstract
High-accuracy and high-throughput proteomic methods have completely changed the way we can identify and characterize proteins. MS-based proteomics can now provide a unique supplement to genomic data and add a new level of information to the interpretation of genomic sequences. Proteomics-driven genome annotation has become especially relevant in microbiology where genomes are sequenced on a daily basis and limitations of an in silico driven annotation process are well recognized. In this review paper, we outline different strategies on how one can design a proteogenomic experiment, for example on genome-sequenced (synonymous proteogenomics) versus unsequenced organisms (ortho-proteogenomics) or with the aid of other "omic" data such as RNA-seq. We touch upon many challenges that are encountered during a typical proteogenomic study, mostly concerning bioinformatics methods and downstream data analysis, but also related to creation and use of sequence databases. A large list of proteogenomic case studies of different microorganisms is provided to illustrate the mapping of MS/MS-derived peptide spectra to genomic DNA sequences. These investigations have led to accurate determination of translational initiation sites, pointed out eventual read-throughs or programmed frameshifts, detected signal peptide processing or other protein maturation events, removed questionable annotation assignments, and provided evidence for predicted hypothetical proteins.
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Affiliation(s)
- Veronika Kucharova
- Department of Clinical Science, The Gade Research Group for Infection and Immunity, University of Bergen, Norway
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48
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Jagtap PD, Johnson JE, Onsongo G, Sadler FW, Murray K, Wang Y, Shenykman GM, Bandhakavi S, Smith LM, Griffin TJ. Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework. J Proteome Res 2014; 13:5898-908. [PMID: 25301683 PMCID: PMC4261978 DOI: 10.1021/pr500812t] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
![]()
Proteogenomics combines large-scale
genomic and transcriptomic
data with mass-spectrometry-based proteomic data to discover novel
protein sequence variants and improve genome annotation. In contrast
with conventional proteomic applications, proteogenomic analysis requires
a number of additional data processing steps. Ideally, these required
steps would be integrated and automated via a single software platform
offering accessibility for wet-bench researchers as well as flexibility
for user-specific customization and integration of new software tools
as they emerge. Toward this end, we have extended the Galaxy bioinformatics
framework to facilitate proteogenomic analysis. Using analysis of
whole human saliva as an example, we demonstrate Galaxy’s flexibility
through the creation of a modular workflow incorporating both established
and customized software tools that improve depth and quality of proteogenomic
results. Our customized Galaxy-based software includes automated,
batch-mode BLASTP searching and a Peptide Sequence Match Evaluator
tool, both useful for evaluating the veracity of putative novel peptide
identifications. Our complex workflow (approximately 140 steps) can
be easily shared using built-in Galaxy functions, enabling their use
and customization by others. Our results provide a blueprint for the
establishment of the Galaxy framework as an ideal solution for the
emerging field of proteogenomics.
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Affiliation(s)
- Pratik D Jagtap
- Center for Mass Spectrometry and Proteomics, University of Minnesota , 43 Gortner Laboratory, 1479 Gortner Avenue, St. Paul, Minnesota 55108, United States
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Cooke IR, Jones D, Bowen JK, Deng C, Faou P, Hall NE, Jayachandran V, Liem M, Taranto AP, Plummer KM, Mathivanan S. Proteogenomic analysis of the Venturia pirina (Pear Scab Fungus) secretome reveals potential effectors. J Proteome Res 2014; 13:3635-44. [PMID: 24965097 DOI: 10.1021/pr500176c] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
A proteogenomic analysis is presented for Venturia pirina, a fungus that causes scab disease on European pear (Pyrus communis). V. pirina is host-specific, and the infection is thought to be mediated by secreted effector proteins. Currently, only 36 V. pirina proteins are catalogued in GenBank, and the genome sequence is not publicly available. To identify putative effectors, V. pirina was grown in vitro on and in cellophane sheets mimicking its growth in infected leaves. Secreted extracts were analyzed by tandem mass spectrometry, and the data (ProteomeXchange identifier PXD000710) was queried against a protein database generated by combining in silico predicted transcripts with six frame translations of a whole genome sequence of V. pirina (GenBank Accession JEMP00000000 ). We identified 1088 distinct V. pirina protein groups (FDR 1%) including 1085 detected for the first time. Thirty novel (not in silico predicted) proteins were found, of which 14 were identified as potential effectors based on characteristic features of fungal effector protein sequences. We also used evidence from semitryptic peptides at the protein N-terminus to corroborate in silico signal peptide predictions for 22 proteins, including several potential effectors. The analysis highlights the utility of proteogenomics in the study of secreted effectors.
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Affiliation(s)
- Ira R Cooke
- Department of Biochemistry, La Trobe Institute for Molecular Science, La Trobe University , Melbourne, Victoria 3086, Australia
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50
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Wang X, Zhang B. Integrating genomic, transcriptomic, and interactome data to improve Peptide and protein identification in shotgun proteomics. J Proteome Res 2014; 13:2715-23. [PMID: 24792918 PMCID: PMC4059263 DOI: 10.1021/pr500194t] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
![]()
Mass spectrometry (MS)-based shotgun
proteomics is an effective
technology for global proteome profiling. The ultimate goal is to
assign tandem MS spectra to peptides and subsequently infer proteins
and their abundance. In addition to database searching and protein
assembly algorithms, computational approaches have been developed
to integrate genomic, transcriptomic, and interactome information
to improve peptide and protein identification. Earlier efforts focus
primarily on making databases more comprehensive using publicly available
genomic and transcriptomic data. More recently, with the increasing
affordability of the Next Generation Sequencing (NGS) technologies,
personalized protein databases derived from sample-specific genomic
and transcriptomic data have emerged as an attractive strategy. In
addition, incorporating interactome data not only improves protein
identification but also puts identified proteins into their functional
context and thus facilitates data interpretation. In this paper, we
survey the major integrative bioinformatics approaches that have been
developed during the past decade and discuss their merits and demerits.
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Affiliation(s)
- Xiaojing Wang
- Department of Biomedical Informatics, ‡Vanderbilt-Ingram Cancer Center, and §Department of Cancer Biology, Vanderbilt University School of Medicine , Nashville, Tennessee 37232, United States
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