1
|
Ercan H, Resch U, Hsu F, Mitulovic G, Bileck A, Gerner C, Yang JW, Geiger M, Miller I, Zellner M. A Practical and Analytical Comparative Study of Gel-Based Top-Down and Gel-Free Bottom-Up Proteomics Including Unbiased Proteoform Detection. Cells 2023; 12:747. [PMID: 36899884 PMCID: PMC10000902 DOI: 10.3390/cells12050747] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
Proteomics is an indispensable analytical technique to study the dynamic functioning of biological systems via different proteins and their proteoforms. In recent years, bottom-up shotgun has become more popular than gel-based top-down proteomics. The current study examined the qualitative and quantitative performance of these two fundamentally different methodologies by the parallel measurement of six technical and three biological replicates of the human prostate carcinoma cell line DU145 using its two most common standard techniques, label-free shotgun and two-dimensional differential gel electrophoresis (2D-DIGE). The analytical strengths and limitations were explored, finally focusing on the unbiased detection of proteoforms, exemplified by discovering a prostate cancer-related cleavage product of pyruvate kinase M2. Label-free shotgun proteomics quickly yields an annotated proteome but with reduced robustness, as determined by three times higher technical variation compared to 2D-DIGE. At a glance, only 2D-DIGE top-down analysis provided valuable, direct stoichiometric qualitative and quantitative information from proteins to their proteoforms, even with unexpected post-translational modifications, such as proteolytic cleavage and phosphorylation. However, the 2D-DIGE technology required almost 20 times as much time per protein/proteoform characterization with more manual work. Ultimately, this work should expose both techniques' orthogonality with their different contents of data output to elucidate biological questions.
Collapse
Affiliation(s)
- Huriye Ercan
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
- Immunology Outpatient Clinic, 1090 Vienna, Austria
| | - Ulrike Resch
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - Felicia Hsu
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - Goran Mitulovic
- Proteomics Core Facility, Clinical Department of Laboratory Medicine, Medical University of Vienna, 1090 Vienna, Austria
| | - Andrea Bileck
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria
- Joint Metabolome Facility, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria
| | - Christopher Gerner
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria
- Joint Metabolome Facility, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria
| | - Jae-Won Yang
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - Margarethe Geiger
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - Ingrid Miller
- Institute of Medical Biochemistry, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
| | - Maria Zellner
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| |
Collapse
|
2
|
Miller RM, Jordan BT, Mehlferber MM, Jeffery ED, Chatzipantsiou C, Kaur S, Millikin RJ, Dai Y, Tiberi S, Castaldi PJ, Shortreed MR, Luckey CJ, Conesa A, Smith LM, Deslattes Mays A, Sheynkman GM. Enhanced protein isoform characterization through long-read proteogenomics. Genome Biol 2022; 23:69. [PMID: 35241129 PMCID: PMC8892804 DOI: 10.1186/s13059-022-02624-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 02/02/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g., PacBio or Oxford Nanopore) provides full-length transcripts which can be used to predict full-length protein isoforms. RESULTS We describe here a long-read proteogenomics approach for integrating sample-matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data to enable detection of protein isoforms previously intractable to MS-based detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis. CONCLUSIONS Our work suggests that the incorporation of long-read sequencing and proteomic data can facilitate improved characterization of human protein isoform diversity. Our first-generation pipeline provides a strong foundation for future development of long-read proteogenomics and its adoption for both basic and translational research.
Collapse
Affiliation(s)
- Rachel M. Miller
- grid.14003.360000 0001 2167 3675Department of Chemistry, University of Wisconsin-Madison, Madison, WI USA
| | - Ben T. Jordan
- grid.27755.320000 0000 9136 933XDepartment of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA USA
| | - Madison M. Mehlferber
- grid.27755.320000 0000 9136 933XDepartment of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA USA ,grid.27755.320000 0000 9136 933XDepartment of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA USA
| | - Erin D. Jeffery
- grid.27755.320000 0000 9136 933XDepartment of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA USA
| | | | - Simi Kaur
- grid.14003.360000 0001 2167 3675Department of Chemistry, University of Wisconsin-Madison, Madison, WI USA
| | - Robert J. Millikin
- grid.14003.360000 0001 2167 3675Department of Chemistry, University of Wisconsin-Madison, Madison, WI USA
| | - Yunxiang Dai
- grid.14003.360000 0001 2167 3675Department of Chemistry, University of Wisconsin-Madison, Madison, WI USA
| | - Simone Tiberi
- grid.7400.30000 0004 1937 0650Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland ,grid.7400.30000 0004 1937 0650Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Peter J. Castaldi
- grid.62560.370000 0004 0378 8294Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA USA ,grid.62560.370000 0004 0378 8294Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA USA
| | - Michael R. Shortreed
- grid.14003.360000 0001 2167 3675Department of Chemistry, University of Wisconsin-Madison, Madison, WI USA
| | - Chance John Luckey
- grid.27755.320000 0000 9136 933XDepartment of Pathology, University of Virginia, Charlottesville, VA USA
| | - Ana Conesa
- grid.4711.30000 0001 2183 4846Institute for Integrative Systems Biology, Spanish National Research Council (CSIC), Paterna, Spain ,grid.15276.370000 0004 1936 8091Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL USA
| | - Lloyd M. Smith
- grid.14003.360000 0001 2167 3675Department of Chemistry, University of Wisconsin-Madison, Madison, WI USA
| | - Anne Deslattes Mays
- grid.420089.70000 0000 9635 8082 Office of Data Science and Sharing, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Rockville, MD USA
| | - Gloria M. Sheynkman
- grid.27755.320000 0000 9136 933XDepartment of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA USA ,grid.27755.320000 0000 9136 933XCenter for Public Health Genomics, University of Virginia, Charlottesville, VA USA ,grid.27755.320000 0000 9136 933XUVA Cancer Center, University of Virginia, Charlottesville, VA USA
| |
Collapse
|
3
|
Schessner JP, Voytik E, Bludau I. A practical guide to interpreting and generating bottom-up proteomics data visualizations. Proteomics 2022; 22:e2100103. [PMID: 35107884 DOI: 10.1002/pmic.202100103] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 12/22/2021] [Accepted: 01/20/2022] [Indexed: 11/10/2022]
Abstract
Mass-spectrometry based bottom-up proteomics is the main method to analyze proteomes comprehensively and the rapid evolution of instrumentation and data analysis has made the technology widely available. Data visualization is an integral part of the analysis process and it is crucial for the communication of results. This is a major challenge due to the immense complexity of MS data. In this review, we provide an overview of commonly used visualizations, starting with raw data of traditional and novel MS technologies, then basic peptide and protein level analyses, and finally visualization of highly complex datasets and networks. We specifically provide guidance on how to critically interpret and discuss the multitude of different proteomics data visualizations. Furthermore, we highlight Python-based libraries and other open science tools that can be applied for independent and transparent generation of customized visualizations. To further encourage programmatic data visualization, we provide the Python code used to generate all data Figures in this review on GitHub (https://github.com/MannLabs/ProteomicsVisualization). This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Julia Patricia Schessner
- Max-Planck-Institute of Biochemistry, Department of Proteomics and Signal Transduction, Planegg, Germany
| | - Eugenia Voytik
- Max-Planck-Institute of Biochemistry, Department of Proteomics and Signal Transduction, Planegg, Germany
| | - Isabell Bludau
- Max-Planck-Institute of Biochemistry, Department of Proteomics and Signal Transduction, Planegg, Germany
| |
Collapse
|
4
|
Comparative database search engine analysis on massive tandem mass spectra of pork-based food products for halal proteomics. J Proteomics 2021; 241:104240. [PMID: 33894373 DOI: 10.1016/j.jprot.2021.104240] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 04/05/2021] [Accepted: 04/09/2021] [Indexed: 11/22/2022]
Abstract
Mass spectrometry-based proteomics relies on dedicated software for peptide and protein identification. These software include open-source or commercial-based search engines; wherein, they employ different algorithms to establish their scoring and identified proteins. Although previous comparative studies have differentiated the proteomics results from different software, there are still yet studies specifically been conducted to compare and evaluate the search engine in the field of halal analysis. This is important because the halal analysis is often using commercial meat samples that have been subjected to various processing, further complicating its analysis. Thus, this study aimed to assess three open-source search engines (Comet, X! Tandem, and ProteinProspector) and a commercial-based search engine (ProteinPilot™) against 135 raw tandem mass spectrometry data files from 15 types of pork-based food products for halal analysis. Each database search engine contained high false-discovery rate (FDR); however, a post-searching algorithm called PeptideProphet managed to reduce the FDR, except for ProteinProspector and ProteinPilot™. From this study, the combined database search engine (executed by iProphet) reveals a thorough protein list for pork-based food products; wherein the most abundant proteins are myofibrillar proteins. Thus, this proteomics study will aid the identification of potential peptide and protein biomarkers for future precision halal analysis. SIGNIFICANCE: A critical challenge of halal proteomics is the availability of a database to confirm the inferential peptides as well as proteins. Currently, the established database such as UniProtKB is related to animal proteome; however, the halal proteomics is related to the highly processed meat-based food products. This study highlights the use of different database search engines (Comet, X! Tandem, ProteinProspector, and ProteinPilot™) and their respective algorithms to analyse 135 raw tandem mass spectrometry data files from 15 types of pork-based food products. This is the first attempt that has compared different database search engines in the context of halal proteomics to ensure the effectiveness of controlling the FDR. Previous studies were just focused on the advantages of a certain algorithm over another. Moreover, other previous studies also have mainly reported the use of mass spectrometry-based shotgun proteomics for meat authentication (the most similar field to halal analysis), but none of the studies were reported on halal aspects that used samples originated from highly processed food products. Hence, a systematic comparative study is duly needed for a more comprehensive and thorough proteomics analysis for such samples. In this study, our combinatorial approach for halal proteomics results from the different search engines used (Comet, X! Tandem, and ProteinProspector) has successfully generated a comprehensive spectral library for the pork-based meat products. This combined spectral library is freely available at https://data.mendeley.com/datasets/6dmm8659rm/3. Thus far, this is the first and new attempt at establishing a spectral library for halal proteomics. We also believe this study is a pioneer for halal proteomics that aimed at non-conventional and non-model organism proteomics, protein analytics, protein bioinformatics, and potential biomarker discovery.
Collapse
|
5
|
Zhong CQ, Wu J, Qiu X, Chen X, Xie C, Han J. Generation of a murine SWATH-MS spectral library to quantify more than 11,000 proteins. Sci Data 2020; 7:104. [PMID: 32218446 PMCID: PMC7099061 DOI: 10.1038/s41597-020-0449-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 03/06/2020] [Indexed: 12/16/2022] Open
Abstract
Targeted SWATH-MS data analysis is critically dependent on the spectral library. Comprehensive spectral libraries of human or several other organisms have been published, but the extensive spectral library for mouse, a widely used model organism is not available. Here, we present a large murine spectral library covering more than 11,000 proteins and 240,000 proteotypic peptides, which included proteins derived from 9 murine tissue samples and one murine L929 cell line. This resource supports the quantification of 67% of all murine proteins annotated by UniProtKB/Swiss-Prot. Furthermore, we applied the spectral library to SWATH-MS data from murine tissue samples. Data are available via SWATHAtlas (PASS01441). Measurement(s) | Mouse Protein • mass spectrum • spectral library | Technology Type(s) | mass spectrometry • combined ms-ms + spectral library search | Sample Characteristic - Organism | Mus musculus |
Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11968230
Collapse
Affiliation(s)
- Chuan-Qi Zhong
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen, China.
| | - Jianfeng Wu
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen, China
| | - Xingfeng Qiu
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Xiamen University, Xiamen, China
| | - Xi Chen
- Medical Research Institute, Wuhan University, Wuhan, China.,SpecAlly Life Technology Co., Ltd, Wuhan, China
| | - Changchuan Xie
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen, China
| | - Jiahuai Han
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen, China.
| |
Collapse
|
6
|
|
7
|
Hesse AM, Dupierris V, Adam C, Court M, Barthe D, Emadali A, Masselon C, Ferro M, Bruley C. hEIDI: An Intuitive Application Tool To Organize and Treat Large-Scale Proteomics Data. J Proteome Res 2016; 15:3896-3903. [PMID: 27560970 DOI: 10.1021/acs.jproteome.5b00853] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Advances in high-throughput proteomics have led to a rapid increase in the number, size, and complexity of the associated data sets. Managing and extracting reliable information from such large series of data sets require the use of dedicated software organized in a consistent pipeline to reduce, validate, exploit, and ultimately export data. The compilation of multiple mass-spectrometry-based identification and quantification results obtained in the context of a large-scale project represents a real challenge for developers of bioinformatics solutions. In response to this challenge, we developed a dedicated software suite called hEIDI to manage and combine both identifications and semiquantitative data related to multiple LC-MS/MS analyses. This paper describes how, through a user-friendly interface, hEIDI can be used to compile analyses and retrieve lists of nonredundant protein groups. Moreover, hEIDI allows direct comparison of series of analyses, on the basis of protein groups, while ensuring consistent protein inference and also computing spectral counts. hEIDI ensures that validated results are compliant with MIAPE guidelines as all information related to samples and results is stored in appropriate databases. Thanks to the database structure, validated results generated within hEIDI can be easily exported in the PRIDE XML format for subsequent publication. hEIDI can be downloaded from http://biodev.extra.cea.fr/docs/heidi .
Collapse
Affiliation(s)
- Anne-Marie Hesse
- Univ. Grenoble Alpes, BIG-BGE, F-38000 Grenoble, France.,CEA, BIG-BGE, F-38000 Grenoble, France.,Inserm U1038, BGE, F-38000 Grenoble, France
| | - Véronique Dupierris
- Univ. Grenoble Alpes, BIG-BGE, F-38000 Grenoble, France.,CEA, BIG-BGE, F-38000 Grenoble, France.,Inserm U1038, BGE, F-38000 Grenoble, France
| | - Claire Adam
- Univ. Grenoble Alpes, BIG-BGE, F-38000 Grenoble, France.,CEA, BIG-BGE, F-38000 Grenoble, France.,Inserm U1038, BGE, F-38000 Grenoble, France
| | - Magali Court
- Univ. Grenoble Alpes, BIG-BGE, F-38000 Grenoble, France.,CEA, BIG-BGE, F-38000 Grenoble, France.,Inserm U1038, BGE, F-38000 Grenoble, France
| | - Damien Barthe
- Univ. Grenoble Alpes, BIG-BGE, F-38000 Grenoble, France.,CEA, BIG-BGE, F-38000 Grenoble, France.,Inserm U1038, BGE, F-38000 Grenoble, France
| | - Anouk Emadali
- Univ. Grenoble Alpes, BIG-BGE, F-38000 Grenoble, France.,CEA, BIG-BGE, F-38000 Grenoble, France.,Inserm U1038, BGE, F-38000 Grenoble, France
| | - Christophe Masselon
- Univ. Grenoble Alpes, BIG-BGE, F-38000 Grenoble, France.,CEA, BIG-BGE, F-38000 Grenoble, France.,Inserm U1038, BGE, F-38000 Grenoble, France
| | - Myriam Ferro
- Univ. Grenoble Alpes, BIG-BGE, F-38000 Grenoble, France.,CEA, BIG-BGE, F-38000 Grenoble, France.,Inserm U1038, BGE, F-38000 Grenoble, France
| | - Christophe Bruley
- Univ. Grenoble Alpes, BIG-BGE, F-38000 Grenoble, France.,CEA, BIG-BGE, F-38000 Grenoble, France.,Inserm U1038, BGE, F-38000 Grenoble, France
| |
Collapse
|
8
|
Protein inference: A protein quantification perspective. Comput Biol Chem 2016; 63:21-29. [DOI: 10.1016/j.compbiolchem.2016.02.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Accepted: 02/01/2016] [Indexed: 01/04/2023]
|
9
|
Shortreed MR, Frey BL, Scalf M, Knoener RA, Cesnik AJ, Smith LM. Elucidating Proteoform Families from Proteoform Intact-Mass and Lysine-Count Measurements. J Proteome Res 2016; 15:1213-21. [PMID: 26941048 PMCID: PMC4917391 DOI: 10.1021/acs.jproteome.5b01090] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
![]()
Proteomics
is presently dominated by the “bottom-up”
strategy, in which proteins are enzymatically digested into peptides
for mass spectrometric identification. Although this approach is highly
effective at identifying large numbers of proteins present in complex
samples, the digestion into peptides renders it impossible to identify
the proteoforms from which they were derived. We present here a powerful
new strategy for the identification of proteoforms and the elucidation
of proteoform families (groups of related proteoforms) from the experimental
determination of the accurate proteoform mass and number of lysine
residues contained. Accurate proteoform masses are determined by standard
LC–MS analysis of undigested protein mixtures in an Orbitrap
mass spectrometer, and the lysine count is determined using the NeuCode
isotopic tagging method. We demonstrate the approach in analysis of
the yeast proteome, revealing 8637 unique proteoforms and 1178 proteoform
families. The elucidation of proteoforms and proteoform families afforded
here provides an unprecedented new perspective upon proteome complexity
and dynamics.
Collapse
Affiliation(s)
- Michael R Shortreed
- Department of Chemistry, University of Wisconsin , 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Brian L Frey
- Department of Chemistry, University of Wisconsin , 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Mark Scalf
- Department of Chemistry, University of Wisconsin , 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Rachel A Knoener
- Department of Chemistry, University of Wisconsin , 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Anthony J Cesnik
- Department of Chemistry, University of Wisconsin , 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin , 1101 University Avenue, Madison, Wisconsin 53706, United States.,Genome Center of Wisconsin, University of Wisconsin , 425G Henry Mall, Room 3420, Madison, Wisconsin 53706, United States
| |
Collapse
|
10
|
Zickmann F, Renard BY. MSProGene: integrative proteogenomics beyond six-frames and single nucleotide polymorphisms. Bioinformatics 2015; 31:i106-15. [PMID: 26072472 PMCID: PMC4765881 DOI: 10.1093/bioinformatics/btv236] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Summary: Ongoing advances in high-throughput technologies have facilitated accurate proteomic measurements and provide a wealth of information on genomic and transcript level. In proteogenomics, this multi-omics data is combined to analyze unannotated organisms and to allow more accurate sample-specific predictions. Existing analysis methods still mainly depend on six-frame translations or reference protein databases that are extended by transcriptomic information or known single nucleotide polymorphisms (SNPs). However, six-frames introduce an artificial sixfold increase of the target database and SNP integration requires a suitable database summarizing results from previous experiments. We overcome these limitations by introducing MSProGene, a new method for integrative proteogenomic analysis based on customized RNA-Seq driven transcript databases. MSProGene is independent from existing reference databases or annotated SNPs and avoids large six-frame translated databases by constructing sample-specific transcripts. In addition, it creates a network combining RNA-Seq and peptide information that is optimized by a maximum-flow algorithm. It thereby also allows resolving the ambiguity of shared peptides for protein inference. We applied MSProGene on three datasets and show that it facilitates a database-independent reliable yet accurate prediction on gene and protein level and additionally identifies novel genes. Availability and implementation: MSProGene is written in Java and Python. It is open source and available at http://sourceforge.net/projects/msprogene/. Contact:renardb@rki.de
Collapse
Affiliation(s)
- Franziska Zickmann
- Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany
| | - Bernhard Y Renard
- Research Group Bioinformatics (NG4), Robert Koch Institute, 13353 Berlin, Germany
| |
Collapse
|
11
|
Zhao C, Liu D, Teng B, He Z. BagReg: Protein inference through machine learning. Comput Biol Chem 2015; 57:12-20. [DOI: 10.1016/j.compbiolchem.2015.02.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Accepted: 02/03/2015] [Indexed: 10/24/2022]
|
12
|
Farina A, Delhaye M, Lescuyer P, Dumonceau JM. Bile proteome in health and disease. Compr Physiol 2014; 4:91-108. [PMID: 24692135 DOI: 10.1002/cphy.c130016] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The study of bile proteins could improve the understanding of physiological processes involved in the regulation of the hepato-biliary system. Researchers have tried for years to investigate the bile proteome but, until recently, only a few tens of proteins were known. The advent of proteomics, availing of large-scale analytical devices paired with potent bioinformatic resources, lately allowed the identification of thousands of proteins in bile. Nevertheless, the knowledge of their role in the hepato-biliary system still represents almost a "blank page in the book of physiology." In this review, we first guide the reader through the historical phases of the analysis of bile protein content, emphasizing the recent progresses achieved through the use of proteomic techniques. Thereafter, we deeply explore the involvement of bile proteins in health and disease, with a particular focus on the discovery of biomarkers for biliary tract malignancies.
Collapse
Affiliation(s)
- Annarita Farina
- Biomedical Proteomics Research Group, Department of Human Protein Sciences, Geneva University, Geneva, Switzerland
| | | | | | | |
Collapse
|
13
|
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.
Collapse
Affiliation(s)
- Veronika Kucharova
- Department of Clinical Science, The Gade Research Group for Infection and Immunity, University of Bergen, Norway
| | | |
Collapse
|
14
|
Rosenberger G, Koh CC, Guo T, Röst HL, Kouvonen P, Collins BC, Heusel M, Liu Y, Caron E, Vichalkovski A, Faini M, Schubert OT, Faridi P, Ebhardt HA, Matondo M, Lam H, Bader SL, Campbell DS, Deutsch EW, Moritz RL, Tate S, Aebersold R. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci Data 2014; 1:140031. [PMID: 25977788 PMCID: PMC4322573 DOI: 10.1038/sdata.2014.31] [Citation(s) in RCA: 298] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Accepted: 08/06/2014] [Indexed: 12/30/2022] Open
Abstract
Mass spectrometry is the method of choice for deep and reliable exploration of the (human) proteome. Targeted mass spectrometry reliably detects and quantifies pre-determined sets of proteins in a complex biological matrix and is used in studies that rely on the quantitatively accurate and reproducible measurement of proteins across multiple samples. It requires the one-time, a priori generation of a specific measurement assay for each targeted protein. SWATH-MS is a mass spectrometric method that combines data-independent acquisition (DIA) and targeted data analysis and vastly extends the throughput of proteins that can be targeted in a sample compared to selected reaction monitoring (SRM). Here we present a compendium of highly specific assays covering more than 10,000 human proteins and enabling their targeted analysis in SWATH-MS datasets acquired from research or clinical specimens. This resource supports the confident detection and quantification of 50.9% of all human proteins annotated by UniProtKB/Swiss-Prot and is therefore expected to find wide application in basic and clinical research. Data are available via ProteomeXchange (PXD000953-954) and SWATHAtlas (SAL00016-35).
Collapse
Affiliation(s)
- George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland ; PhD Program in Systems Biology, University of Zurich and ETH Zurich , CH-8093 Zurich, Switzerland
| | - Ching Chiek Koh
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland ; Ruprecht Karls University of Heidelberg , DE-69117 Heidelberg, Germany
| | - Tiannan Guo
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland
| | - Hannes L Röst
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland ; PhD Program in Systems Biology, University of Zurich and ETH Zurich , CH-8093 Zurich, Switzerland
| | - Petri Kouvonen
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland
| | - Ben C Collins
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland
| | - Moritz Heusel
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland ; PhD Program in Molecular and Translational Biomedicine, Competence Centre for Systems Physiology and Metabolic Diseases (CC-SPMD), University of Zurich and ETH Zurich , CH-8093 Zurich, Switzerland
| | - Yansheng Liu
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland
| | - Etienne Caron
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland
| | - Anton Vichalkovski
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland
| | - Marco Faini
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland
| | - Olga T Schubert
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland ; PhD Program in Systems Biology, University of Zurich and ETH Zurich , CH-8093 Zurich, Switzerland
| | - Pouya Faridi
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland ; Department of Phytopharmaceuticals (Traditional Pharmacy), School of Pharmacy and Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, 71345-1583 Shiraz, Iran
| | - H Alexander Ebhardt
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland
| | - Mariette Matondo
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland
| | - Henry Lam
- Division of Biomedical Engineering and Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay , Hong Kong, China
| | - Samuel L Bader
- Institute for Systems Biology , Seattle, Washington 98109-5234, USA
| | - David S Campbell
- Institute for Systems Biology , Seattle, Washington 98109-5234, USA
| | - Eric W Deutsch
- Institute for Systems Biology , Seattle, Washington 98109-5234, USA
| | - Robert L Moritz
- Institute for Systems Biology , Seattle, Washington 98109-5234, USA
| | | | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology , ETH Zurich, CH-8093 Zurich, Switzerland ; Faculty of Science, University of Zurich , CH-8057 Zurich, Switzerland
| |
Collapse
|
15
|
Chen C, Liu X, Zheng W, Zhang L, Yao J, Yang P. Screening of missing proteins in the human liver proteome by improved MRM-approach-based targeted proteomics. J Proteome Res 2014; 13:1969-78. [PMID: 24597967 DOI: 10.1021/pr4010986] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
To completely annotate the human genome, the task of identifying and characterizing proteins that currently lack mass spectrometry (MS) evidence is inevitable and urgent. In this study, as the first effort to screen missing proteins in large scale, we developed an approach based on SDS-PAGE followed by liquid chromatography-multiple reaction monitoring (LC-MRM), for screening of those missing proteins with only a single peptide hit in the previous liver proteome data set. Proteins extracted from normal human liver were separated in SDS-PAGE and digested in split gel slice, and the resulting digests were then subjected to LC-schedule MRM analysis. The MRM assays were developed through synthesized crude peptides for target peptides. In total, the expressions of 57 target proteins were confirmed from 185 MRM assays in normal human liver tissues. Among the proved 57 one-hit wonders, 50 proteins are of the minimally redundant set in the PeptideAtlas database, 7 proteins even have none MS-based information previously in various biological processes. We conclude that our SDS-PAGE-MRM workflow can be a powerful approach to screen missing or poorly characterized proteins in different samples and to provide their quantity if detected. The MRM raw data have been uploaded to ISB/SRM Atlas/PASSEL (PXD000648).
Collapse
Affiliation(s)
- Chen Chen
- Department of Chemistry, Fudan University , Shanghai 200032, P. R. China
| | | | | | | | | | | |
Collapse
|
16
|
Kelchtermans P, Bittremieux W, De Grave K, Degroeve S, Ramon J, Laukens K, Valkenborg D, Barsnes H, Martens L. Machine learning applications in proteomics research: how the past can boost the future. Proteomics 2014; 14:353-66. [PMID: 24323524 DOI: 10.1002/pmic.201300289] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Revised: 09/24/2013] [Accepted: 10/14/2013] [Indexed: 01/22/2023]
Abstract
Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.
Collapse
Affiliation(s)
- Pieter Kelchtermans
- Department of Medical Protein Research, VIB, Ghent, Belgium; Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium; Flemish Institute for Technological Research (VITO), Boeretang, Mol, Belgium
| | | | | | | | | | | | | | | | | |
Collapse
|
17
|
Hudler P, Kocevar N, Komel R. Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics. ScientificWorldJournal 2014; 2014:260348. [PMID: 24550697 PMCID: PMC3914447 DOI: 10.1155/2014/260348] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Accepted: 10/08/2013] [Indexed: 12/14/2022] Open
Abstract
Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.
Collapse
Affiliation(s)
- Petra Hudler
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
| | - Nina Kocevar
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
| | - Radovan Komel
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
| |
Collapse
|
18
|
Tanca A, Palomba A, Deligios M, Cubeddu T, Fraumene C, Biosa G, Pagnozzi D, Addis MF, Uzzau S. Evaluating the impact of different sequence databases on metaproteome analysis: insights from a lab-assembled microbial mixture. PLoS One 2013; 8:e82981. [PMID: 24349410 PMCID: PMC3857319 DOI: 10.1371/journal.pone.0082981] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2013] [Accepted: 10/30/2013] [Indexed: 01/10/2023] Open
Abstract
Metaproteomics enables the investigation of the protein repertoire expressed by complex microbial communities. However, to unleash its full potential, refinements in bioinformatic approaches for data analysis are still needed. In this context, sequence databases selection represents a major challenge. This work assessed the impact of different databases in metaproteomic investigations by using a mock microbial mixture including nine diverse bacterial and eukaryotic species, which was subjected to shotgun metaproteomic analysis. Then, both the microbial mixture and the single microorganisms were subjected to next generation sequencing to obtain experimental metagenomic- and genomic-derived databases, which were used along with public databases (namely, NCBI, UniProtKB/SwissProt and UniProtKB/TrEMBL, parsed at different taxonomic levels) to analyze the metaproteomic dataset. First, a quantitative comparison in terms of number and overlap of peptide identifications was carried out among all databases. As a result, only 35% of peptides were common to all database classes; moreover, genus/species-specific databases provided up to 17% more identifications compared to databases with generic taxonomy, while the metagenomic database enabled a slight increment in respect to public databases. Then, database behavior in terms of false discovery rate and peptide degeneracy was critically evaluated. Public databases with generic taxonomy exhibited a markedly different trend compared to the counterparts. Finally, the reliability of taxonomic attribution according to the lowest common ancestor approach (using MEGAN and Unipept software) was assessed. The level of misassignments varied among the different databases, and specific thresholds based on the number of taxon-specific peptides were established to minimize false positives. This study confirms that database selection has a significant impact in metaproteomics, and provides critical indications for improving depth and reliability of metaproteomic results. Specifically, the use of iterative searches and of suitable filters for taxonomic assignments is proposed with the aim of increasing coverage and trustworthiness of metaproteomic data.
Collapse
Affiliation(s)
- Alessandro Tanca
- Porto Conte Ricerche Srl, Tramariglio, Alghero, Italy
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
| | - Antonio Palomba
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
| | - Massimo Deligios
- Porto Conte Ricerche Srl, Tramariglio, Alghero, Italy
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
| | | | | | - Grazia Biosa
- Porto Conte Ricerche Srl, Tramariglio, Alghero, Italy
| | | | - Maria Filippa Addis
- Porto Conte Ricerche Srl, Tramariglio, Alghero, Italy
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
- * E-mail: (MFA); (SU)
| | - Sergio Uzzau
- Porto Conte Ricerche Srl, Tramariglio, Alghero, Italy
- Dipartimento di Scienze Biomediche, Università di Sassari, Sassari, Italy
- * E-mail: (MFA); (SU)
| |
Collapse
|
19
|
Proteomics of model and crop plant species: Status, current limitations and strategic advances for crop improvement. J Proteomics 2013; 93:5-19. [DOI: 10.1016/j.jprot.2013.05.036] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Revised: 05/20/2013] [Accepted: 05/29/2013] [Indexed: 12/22/2022]
|
20
|
Resende VMF, Vasilj A, Santos KS, Palma MS, Shevchenko A. Proteome and phosphoproteome of Africanized and European honeybee venoms. Proteomics 2013; 13:2638-48. [DOI: 10.1002/pmic.201300038] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2013] [Revised: 05/03/2013] [Accepted: 06/03/2013] [Indexed: 11/11/2022]
Affiliation(s)
- Virgínia Maria Ferreira Resende
- Division of Clinical Immunology and Allergy; Department of Medicine, University of São Paulo; São Paulo SP Brazil
- Institute for Investigation in Immunology (iii-INCT); São Paulo SP Brazil
- MPI of Molecular Cell Biology and Genetics; Dresden Germany
| | - Andrej Vasilj
- MPI of Molecular Cell Biology and Genetics; Dresden Germany
| | - Keity Souza Santos
- Division of Clinical Immunology and Allergy; Department of Medicine, University of São Paulo; São Paulo SP Brazil
- Institute for Investigation in Immunology (iii-INCT); São Paulo SP Brazil
| | - Mario Sergio Palma
- Institute for Investigation in Immunology (iii-INCT); São Paulo SP Brazil
- Institute of Biosciences of Rio Claro; Sao Paulo State University (UNESP); Rio Claro SP Brazil
| | | |
Collapse
|
21
|
Bruce C, Stone K, Gulcicek E, Williams K. Proteomics and the analysis of proteomic data: 2013 overview of current protein-profiling technologies. ACTA ACUST UNITED AC 2013; Chapter 13:13.21.1-13.21.17. [PMID: 23504934 DOI: 10.1002/0471250953.bi1321s41] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Mass spectrometry has become a major tool in the study of proteomes. The analysis of proteolytic peptides and their fragment ions by this technique enables the identification and quantitation of the precursor proteins in a mixture. However, deducing chemical structures and then protein sequences from mass-to-charge ratios is a challenging computational task. Software tools incorporating powerful algorithms and statistical methods improved our ability to process the large quantities of proteomics data. Repositories of spectral data make both data analysis and experimental design more efficient. New approaches in quantitative and statistical proteomics make possible a greater coverage of the proteome, the identification of more post-translational modifications, and a greater sensitivity in the quantitation of targeted proteins.
Collapse
Affiliation(s)
- Can Bruce
- W.M. Keck Foundation Biotechnology Resource Laboratory and Molecular Biochemistry and Biophysics Department, Yale University, New Haven, Connecticut, USA
| | | | | | | |
Collapse
|
22
|
|
23
|
Sandin M, Teleman J, Malmström J, Levander F. Data processing methods and quality control strategies for label-free LC-MS protein quantification. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2013; 1844:29-41. [PMID: 23567904 DOI: 10.1016/j.bbapap.2013.03.026] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2012] [Revised: 01/18/2013] [Accepted: 03/08/2013] [Indexed: 12/20/2022]
Abstract
Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.
Collapse
Affiliation(s)
- Marianne Sandin
- Department of Immunotechnology, Lund University, BMC D13, 22184 Lund, Sweden
| | | | | | | |
Collapse
|
24
|
Messana I, Cabras T, Iavarone F, Vincenzoni F, Urbani A, Castagnola M. Unraveling the different proteomic platforms. J Sep Sci 2012; 36:128-39. [PMID: 23212829 DOI: 10.1002/jssc.201200830] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2012] [Revised: 10/05/2012] [Accepted: 10/06/2012] [Indexed: 01/06/2023]
Abstract
This review is addressed to scientists working outside the field of proteomics and wishes to shed a light on the possibility offered by the latest proteomics strategies. Bottom-up and top-down platforms are critically examined outlining advantages and limitations of their application to qualitative and quantitative investigations. Discovery, directed and targeted proteomics as different options for the management of the MS instrument are defined emphasizing their integration in the experimental plan to accomplish meaningful results. The issue of data validation is analyzed and discussed. The most common qualitative proteomic platforms are described, with a particular emphasis on enrichment methods to elucidate PTMs codes (i.e. ubiquitin and histone codes). Label-free and labeled methods for relative and absolute quantification are critically compared. The possible contribution of proteomics platforms to the transition from structural proteomics to functional proteomics (study of the functional connections between different proteins) and to the challenging system biology (integrated study of all the functional cellular functions) is also briefly discussed.
Collapse
Affiliation(s)
- Irene Messana
- Dipartimento di Scienze della Vita e dell'Ambiente, Università di Cagliari, Cagliari, Italy
| | | | | | | | | | | |
Collapse
|