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Lereim RR, Nytrova P, Guldbrandsen A, Havrdova EK, Myhr KM, Barsnes H, Berven FS. Natalizumab promotes anti-inflammatory and repair effects in multiple sclerosis. PLoS One 2024; 19:e0300914. [PMID: 38527011 PMCID: PMC10962820 DOI: 10.1371/journal.pone.0300914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/06/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND Multiple sclerosis is an inflammatory and degenerative disease of the central nervous system leading to demyelination and axonal loss. Relapsing-remitting multiple sclerosis (RRMS) is commonly treated by anti-inflammatory drugs, where one of the most effective drugs to date is the monoclonal antibody natalizumab. METHODS The cerebrospinal fluid (CSF) proteome was analyzed in 56 patients with RRMS before and after natalizumab treatment, using label-free mass spectrometry and a subset of the changed proteins were verified by parallel reaction monitoring in a new cohort of 20 patients, confirming the majority of observed changes. RESULTS A total of 287 differentially abundant proteins were detected including (i) the decrease of proteins with roles in immunity, such as immunoglobulin heavy constant mu, chitinase-3-like protein 1 and chitotriosidase, (ii) an increase of proteins involved in metabolism, such as lactate dehydrogenase A and B and malate-dehydrogenase cytoplasmic, and (iii) an increase of proteins associated with the central nervous system, including lactadherin and amyloid precursor protein. Comparison with the CSF-PR database provided evidence that natalizumab counters protein changes commonly observed in RRMS. Furthermore, vitamin-D binding protein and apolipoprotein 1 and 2 were unchanged during treatment with natalizumab, implying that these may be involved in disease activity unaffected by natalizumab. CONCLUSIONS Our study revealed that some of the previously suggested biomarkers for MS were affected by the natalizumab treatment while others were not. Proteins not previously suggested as biomarkers were also found affected by the treatment. In sum, the results provide new information on how the natalizumab treatment impacts the CSF proteome of MS patients, and points towards processes affected by the treatment. These findings ought to be explored further to disclose potential novel disease mechanisms and predict treatment responses.
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Affiliation(s)
- Ragnhild Reehorst Lereim
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit (CBU), Department of Informatics, University of Bergen, Bergen, Norway
| | - Petra Nytrova
- Department of Neurology and Center for Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Astrid Guldbrandsen
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit (CBU), Department of Informatics, University of Bergen, Bergen, Norway
| | - Eva Kubala Havrdova
- Department of Neurology and Center for Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Kjell-Morten Myhr
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Harald Barsnes
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit (CBU), Department of Informatics, University of Bergen, Bergen, Norway
| | - Frode S. Berven
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway
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2
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Burger B, Vaudel M, Barsnes H. Automated splitting into batches for observational biomedical studies with sequential processing. Biostatistics 2023; 24:1031-1044. [PMID: 35536588 PMCID: PMC10583723 DOI: 10.1093/biostatistics/kxac014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 10/19/2023] Open
Abstract
Experimental design usually focuses on the setting where treatments and/or other aspects of interest can be manipulated. However, in observational biomedical studies with sequential processing, the set of available samples is often fixed, and the problem is thus rather the ordering and allocation of samples to batches such that comparisons between different treatments can be made with similar precision. In certain situations, this allocation can be done by hand, but this rapidly becomes impractical with more challenging cohort setups. Here, we present a fast and intuitive algorithm to generate balanced allocations of samples to batches for any single-variable model where the treatment variable is nominal. This greatly simplifies the grouping of samples into batches, makes the process reproducible, and provides a marked improvement over completely random allocations. The general challenges of allocation and why good solutions can be hard to find are also discussed, as well as potential extensions to multivariable settings.
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Affiliation(s)
- Bram Burger
- Computational Biology Unit (CBU), Department of Informatics, University of Bergen, 5008 Bergen, Norway, Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5020 Bergen, Norway, and Department of Medical Genetics, Haukeland University Hospital, 5021 Bergen, Norway
| | - Marc Vaudel
- Department of Clinical Science, University of Bergen, 5020 Bergen, Norway
| | - Harald Barsnes
- Computational Biology Unit (CBU), Department of Informatics, University of Bergen, 5008 Bergen, Norway and Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5020 Bergen, Norway
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3
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Hernández Sánchez LF, Burger B, Castro Campos RA, Johansson S, Njølstad PR, Barsnes H, Vaudel M. Extending protein interaction networks using proteoforms and small molecules. Bioinformatics 2023; 39:btad598. [PMID: 37756698 PMCID: PMC10564616 DOI: 10.1093/bioinformatics/btad598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 08/21/2023] [Accepted: 09/25/2023] [Indexed: 09/29/2023] Open
Abstract
MOTIVATION Biological network analysis for high-throughput biomedical data interpretation relies heavily on topological characteristics. Networks are commonly composed of nodes representing genes or proteins that are connected by edges when interacting. In this study, we use the rich information available in the Reactome pathway database to build biological networks accounting for small molecules and proteoforms modeled using protein isoforms and post-translational modifications to study the topological changes induced by this refinement of the network representation. RESULTS We find that improving the interactome modeling increases the number of nodes and interactions, but that isoform and post-translational modification annotation is still limited compared to what can be expected biologically. We also note that small molecule information can distort the topology of the network due to the high connectedness of these molecules, which does not necessarily represent the reality of biology. However, by restricting the connections of small molecules to the context of biochemical reactions, we find that these improve the overall connectedness of the network and reduce the prevalence of isolated components and nodes. Overall, changing the representation of the network alters the prevalence of articulation points and bridges globally but also within and across pathways. Hence, some molecules can gain or lose in biological importance depending on the level of detail of the representation of the biological system, which might in turn impact network-based studies of diseases or druggability. AVAILABILITY AND IMPLEMENTATION Networks are constructed based on data publicly available in the Reactome Pathway knowledgebase: reactome.org.
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Affiliation(s)
- Luis Francisco Hernández Sánchez
- Department of Clinical Science, Mohn Center for Diabetes Precision Medicine, University of Bergen, Bergen 5020, Norway
- Department of Medical Genetics, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen 5020, Norway
| | - Bram Burger
- Department of Clinical Science, Mohn Center for Diabetes Precision Medicine, University of Bergen, Bergen 5020, Norway
- Department of Medical Genetics, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen 5020, Norway
- Department of Biomedicine, Proteomics Unit, University of Bergen, Bergen 5020, Norway
- Department of Informatics, Computational Biology Unit, University of Bergen, Bergen 5020, Norway
| | | | - Stefan Johansson
- Department of Clinical Science, Mohn Center for Diabetes Precision Medicine, University of Bergen, Bergen 5020, Norway
- Department of Medical Genetics, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen 5020, Norway
| | - Pål Rasmus Njølstad
- Department of Clinical Science, Mohn Center for Diabetes Precision Medicine, University of Bergen, Bergen 5020, Norway
- Department of Pediatrics, Haukeland University Hospital, Bergen 5020, Norway
| | - Harald Barsnes
- Department of Biomedicine, Proteomics Unit, University of Bergen, Bergen 5020, Norway
- Department of Informatics, Computational Biology Unit, University of Bergen, Bergen 5020, Norway
| | - Marc Vaudel
- Department of Clinical Science, Mohn Center for Diabetes Precision Medicine, University of Bergen, Bergen 5020, Norway
- Department of Informatics, Computational Biology Unit, University of Bergen, Bergen 5020, Norway
- Department of Genetics and Bioinformatics, Health Data and Digitalization, Norwegian Institute of Public Health, Oslo 0213, Norway
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4
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Farag YM, Horro C, Vaudel M, Barsnes H. PeptideShaker Online: A User-Friendly Web-Based Framework for the Identification of Mass Spectrometry-Based Proteomics Data. J Proteome Res 2021; 20:5419-5423. [PMID: 34709836 PMCID: PMC8650087 DOI: 10.1021/acs.jproteome.1c00678] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Mass spectrometry-based proteomics is a high-throughput technology generating ever-larger amounts of data per project. However, storing, processing, and interpreting these data can be a challenge. A key element in simplifying this process is the development of interactive frameworks focusing on visualization that can greatly simplify both the interpretation of data and the generation of new knowledge. Here we present PeptideShaker Online, a user-friendly web-based framework for the identification of mass spectrometry-based proteomics data, from raw file conversion to interactive visualization of the resulting data. Storage and processing of the data are performed via the versatile Galaxy platform (through SearchGUI, PeptideShaker, and moFF), while the interaction with the results happens via a locally installed web server, thus enabling researchers to process and interpret their own data without requiring advanced bioinformatics skills or direct access to compute-intensive infrastructures. The source code, additional documentation, and a fully functional demo is available at https://github.com/barsnes-group/peptide-shaker-online.
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Affiliation(s)
- Yehia Mokhtar Farag
- Proteomics Unit, Department of Biomedicine, University of Bergen, 5020 Bergen, Norway.,Computational Biology Unit, Department of Informatics, University of Bergen, 5008 Bergen, Norway
| | - Carlos Horro
- Proteomics Unit, Department of Biomedicine, University of Bergen, 5020 Bergen, Norway.,Computational Biology Unit, Department of Informatics, University of Bergen, 5008 Bergen, Norway
| | - Marc Vaudel
- Department of Clinical Sciences, University of Bergen, 5020 Bergen, Norway
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen, 5020 Bergen, Norway.,Computational Biology Unit, Department of Informatics, University of Bergen, 5008 Bergen, Norway
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5
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Oveland E, Ahmad I, Lereim RR, Kroksveen AC, Barsnes H, Guldbrandsen A, Myhr KM, Bø L, Berven FS, Wergeland S. Cuprizone and EAE mouse frontal cortex proteomics revealed proteins altered in multiple sclerosis. Sci Rep 2021; 11:7174. [PMID: 33785790 PMCID: PMC8010076 DOI: 10.1038/s41598-021-86191-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 02/19/2021] [Indexed: 02/06/2023] Open
Abstract
Two pathophysiological different experimental models for multiple sclerosis were analyzed in parallel using quantitative proteomics in attempts to discover protein alterations applicable as diagnostic-, prognostic-, or treatment targets in human disease. The cuprizone model reflects de- and remyelination in multiple sclerosis, and the experimental autoimmune encephalomyelitis (EAE, MOG1-125) immune-mediated events. The frontal cortex, peripheral to severely inflicted areas in the CNS, was dissected and analyzed. The frontal cortex had previously not been characterized by proteomics at different disease stages, and novel protein alterations involved in protecting healthy tissue and assisting repair of inflicted areas might be discovered. Using TMT-labelling and mass spectrometry, 1871 of the proteins quantified overlapped between the two experimental models, and the fold change compared to controls was verified using label-free proteomics. Few similarities in frontal cortex between the two disease models were observed when regulated proteins and signaling pathways were compared. Legumain and C1Q complement proteins were among the most upregulated proteins in cuprizone and hemopexin in the EAE model. Immunohistochemistry showed that legumain expression in post-mortem multiple sclerosis brain tissue (n = 19) was significantly higher in the center and at the edge of white matter active and chronic active lesions. Legumain was associated with increased lesion activity and might be valuable as a drug target using specific inhibitors as already suggested for Parkinson's and Alzheimer's disease. Cerebrospinal fluid levels of legumain, C1q and hemopexin were not significantly different between multiple sclerosis patients, other neurological diseases, or healthy controls.
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Affiliation(s)
- Eystein Oveland
- Proteomics Unit, Department of Biomedicine, University of Bergen (PROBE), Bergen, Norway
| | - Intakhar Ahmad
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Neurology, Norwegian Multiple Sclerosis Competence Centre, Haukeland University Hospital, Jonas Lies vei 65, 5021, Bergen, Norway
| | - Ragnhild Reehorst Lereim
- Proteomics Unit, Department of Biomedicine, University of Bergen (PROBE), Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Ann Cathrine Kroksveen
- Proteomics Unit, Department of Biomedicine, University of Bergen (PROBE), Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen (PROBE), Bergen, Norway
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Astrid Guldbrandsen
- Proteomics Unit, Department of Biomedicine, University of Bergen (PROBE), Bergen, Norway
- Department of Neurology, Norwegian Multiple Sclerosis Competence Centre, Haukeland University Hospital, Jonas Lies vei 65, 5021, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Kjell-Morten Myhr
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Lars Bø
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Neurology, Norwegian Multiple Sclerosis Competence Centre, Haukeland University Hospital, Jonas Lies vei 65, 5021, Bergen, Norway
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Frode S Berven
- Proteomics Unit, Department of Biomedicine, University of Bergen (PROBE), Bergen, Norway
- Department of Neurology, Norwegian Multiple Sclerosis Competence Centre, Haukeland University Hospital, Jonas Lies vei 65, 5021, Bergen, Norway
| | - Stig Wergeland
- Department of Neurology, Norwegian Multiple Sclerosis Competence Centre, Haukeland University Hospital, Jonas Lies vei 65, 5021, Bergen, Norway.
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway.
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6
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Gupta MK, Vethe H, Softic S, Rao TN, Wagh V, Shirakawa J, Barsnes H, Vaudel M, Takatani T, Kahraman S, Sakaguchi M, Martinez R, Hu J, Bjørlykke Y, Raeder H, Kulkarni RN. Leptin Receptor Signaling Regulates Protein Synthesis Pathways and Neuronal Differentiation in Pluripotent Stem Cells. Stem Cell Reports 2020; 15:1067-1079. [PMID: 33125875 PMCID: PMC7664055 DOI: 10.1016/j.stemcr.2020.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 10/01/2020] [Accepted: 10/01/2020] [Indexed: 01/05/2023] Open
Abstract
The role of leptin receptor (OB-R) signaling in linking pluripotency with growth and development and the consequences of dysfunctional leptin signaling on progression of metabolic disease is poorly understood. Using a global unbiased proteomics approach we report that embryonic fibroblasts (MEFs) carrying the db/db mutation exhibit metabolic abnormalities, while their reprogrammed induced pluripotent stem cells (iPSCs) show altered expression of proteins involved in embryonic development. An upregulation in expression of eukaryotic translation initiation factor 4e (Eif4e) and Stat3 binding to the Eif4e promoter was supported by enhanced protein synthesis in mutant iPSCs. Directed differentiation of db/db iPSCs toward the neuronal lineage showed defects. Gene editing to correct the point mutation in db/db iPSCs using CRISPR-Cas9, restored expression of neuronal markers and protein synthesis while reversing the metabolic defects. These data imply a direct role for OB-R in regulating metabolism in embryonic fibroblasts and key developmental pathways in iPSCs. Pluripotency markers are decreased in db/db iPSCs (lacking functional OB-R) Mouse db/db iPSCs exhibit higher protein synthesis mediated by the Stat3/Eif4e axis OB-R signaling regulates neuronal development markers—NOGGIN, NESTIN, GFAP CRISPR correction reverses defects in db/db iPSCs
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Affiliation(s)
- Manoj K Gupta
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Heidrun Vethe
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA; KG Jebsen Center for Diabetes Research, Department of Clinical Medicine, University of Bergen, Bergen 5009, Norway
| | - Samir Softic
- Department of Gastroenterology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA; Section of Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Tata Nageswara Rao
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA; University Clinic of Hematology, Department of Biomedical Research, Inselspital Bern and University of Bern, Bern, Switzerland
| | - Vilas Wagh
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jun Shirakawa
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Harald Barsnes
- KG Jebsen Center for Diabetes Research, Department of Clinical Medicine, University of Bergen, Bergen 5009, Norway; Proteomics Unit, Department of Biomedicine, University of Bergen, Norway
| | - Marc Vaudel
- KG Jebsen Center for Diabetes Research, Department of Clinical Medicine, University of Bergen, Bergen 5009, Norway; Proteomics Unit, Department of Biomedicine, University of Bergen, Norway
| | - Tomozumi Takatani
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Sevim Kahraman
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Masaji Sakaguchi
- Section of Integrative Physiology and Metabolism, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Rachael Martinez
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Jiang Hu
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA
| | - Yngvild Bjørlykke
- KG Jebsen Center for Diabetes Research, Department of Clinical Medicine, University of Bergen, Bergen 5009, Norway; Department of Pediatrics, Haukeland University Hospital, N-5021 Bergen, Norway
| | - Helge Raeder
- KG Jebsen Center for Diabetes Research, Department of Clinical Medicine, University of Bergen, Bergen 5009, Norway; Department of Pediatrics, Haukeland University Hospital, N-5021 Bergen, Norway
| | - Rohit N Kulkarni
- Section on Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA; Harvard Stem Cell Institute, Harvard Medical School, Boston, MA 02215, USA.
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7
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Abstract
![]()
Randomization
is used in experimental design to reduce the prevalence
of unanticipated confounders. Complete randomization can however create
imbalanced designs, for example, grouping all samples of the same
condition in the same batch. Block randomization is an approach that
can prevent severe imbalances in sample allocation with respect to
both known and unknown confounders. This feature provides the reader
with an introduction to blocking and randomization, and insights into
how to effectively organize samples during experimental design, with
special considerations with respect to proteomics.
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Affiliation(s)
- Bram Burger
- Computational Biology Unit (CBU), Department of Informatics, University of Bergen, 5007 Bergen, Norway.,Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5007 Bergen, Norway
| | - Marc Vaudel
- Department of Clinical Sciences, University of Bergen, 5007 Bergen, Norway
| | - Harald Barsnes
- Computational Biology Unit (CBU), Department of Informatics, University of Bergen, 5007 Bergen, Norway.,Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5007 Bergen, Norway
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8
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Guldbrandsen A, Lereim RR, Jacobsen M, Garberg H, Kroksveen AC, Barsnes H, Berven FS. Development of robust targeted proteomics assays for cerebrospinal fluid biomarkers in multiple sclerosis. Clin Proteomics 2020; 17:33. [PMID: 32963504 PMCID: PMC7499868 DOI: 10.1186/s12014-020-09296-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 09/08/2020] [Indexed: 12/25/2022] Open
Abstract
Background Verification of cerebrospinal fluid (CSF) biomarkers for multiple sclerosis and other neurological diseases is a major challenge due to a large number of candidates, limited sample material availability, disease and biological heterogeneity, and the lack of standardized assays. Furthermore, verification studies are often based on a low number of proteins from a single discovery experiment in medium-sized cohorts, where antibodies and surrogate peptides may differ, thus only providing an indication of proteins affected by the disease and not revealing the bigger picture or concluding on the validity of the markers. We here present a standard approach for locating promising biomarker candidates based on existing knowledge, resulting in high-quality assays covering the main biological processes affected by multiple sclerosis for comparable measurements over time. Methods Biomarker candidates were located in CSF-PR (proteomics.uib.no/csf-pr), and further filtered based on estimated concentration in CSF and biological function. Peptide surrogates for internal standards were selected according to relevant criteria, parallel reaction monitoring (PRM) assays created, and extensive assay quality testing performed, i.e. intra- and inter-day variation, trypsin digestion status over time, and whether the peptides were able to separate multiple sclerosis patients and controls. Results Assays were developed for 25 proteins, represented by 72 peptides selected according to relevant guidelines and available literature and tested for assay peptide suitability. Stability testing revealed 64 peptides with low intra- and inter-day variations, with 44 also being stably digested after 16 h of trypsin digestion, and 37 furthermore showing a significant difference between multiple sclerosis and controls, thereby confirming literature findings. Calibration curves and the linear area of measurement have, so far, been determined for 17 of these peptides. Conclusions We present 37 high-quality PRM assays across 21 CSF-proteins found to be affected by multiple sclerosis, along with a recommended workflow for future development of new assays. The assays can directly be used by others, thus enabling better comparison between studies. Finally, the assays can robustly and stably monitor biological processes in multiple sclerosis patients over time, thus potentially aiding in diagnosis and prognosis, and ultimately in treatment decisions.
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Affiliation(s)
- Astrid Guldbrandsen
- Proteomics Unit, PROBE, Department of Biomedicine, University of Bergen, Bergen, Norway.,Computational Biology Unit, CBU, Department of Informatics, University of Bergen, Bergen, Norway
| | - Ragnhild Reehorst Lereim
- Proteomics Unit, PROBE, Department of Biomedicine, University of Bergen, Bergen, Norway.,Computational Biology Unit, CBU, Department of Informatics, University of Bergen, Bergen, Norway
| | - Mari Jacobsen
- Proteomics Unit, PROBE, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Hilde Garberg
- Biobank Haukeland, Haukeland University Hospital, Bergen, Norway
| | | | - Harald Barsnes
- Proteomics Unit, PROBE, Department of Biomedicine, University of Bergen, Bergen, Norway.,Computational Biology Unit, CBU, Department of Informatics, University of Bergen, Bergen, Norway
| | - Frode S Berven
- Proteomics Unit, PROBE, Department of Biomedicine, University of Bergen, Bergen, Norway
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9
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Van Den Bossche T, Verschaffelt P, Schallert K, Barsnes H, Dawyndt P, Benndorf D, Renard BY, Mesuere B, Martens L, Muth T. Connecting MetaProteomeAnalyzer and PeptideShaker to Unipept for Seamless End-to-End Metaproteomics Data Analysis. J Proteome Res 2020; 19:3562-3566. [DOI: 10.1021/acs.jproteome.0c00136] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Tim Van Den Bossche
- VIB-UGent Center for Medical Biotechnology, VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, St. Pietersnieuwstraat 33, 9000 Ghent, Belgium
| | - Pieter Verschaffelt
- VIB-UGent Center for Medical Biotechnology, VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Krijgslaan 281-S9, 9000 Ghent, Belgium
| | - Kay Schallert
- Bioprocess Engineering, Faculty for Process and Systems Engineering, Otto von Guericke University, Universitaetsplatz 2, 39106 Magdeburg, Germany
- Microbiology, Department of Applied Biosciences and Process Technology, Anhalt University of Applied Sciences, Bernburger Straße 55, 06366 Köthen, Germany
| | - Harald Barsnes
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Postboks 7804, NO-5020 Bergen, Norway
- Computational Biology Unit (CBU), Department of Informatics, University of Bergen, Postboks 7804, N-5020 Bergen, Norway
| | - Peter Dawyndt
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Krijgslaan 281-S9, 9000 Ghent, Belgium
| | - Dirk Benndorf
- Bioprocess Engineering, Faculty for Process and Systems Engineering, Otto von Guericke University, Universitaetsplatz 2, 39106 Magdeburg, Germany
- Microbiology, Department of Applied Biosciences and Process Technology, Anhalt University of Applied Sciences, Bernburger Straße 55, 06366 Köthen, Germany
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße, 39106 Magdeburg, Germany
| | - Bernhard Y. Renard
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Nordufer 20, 13353 Berlin, Germany
- Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Straße 2 – 3, 14482 Potsdam, Germany
| | - Bart Mesuere
- VIB-UGent Center for Medical Biotechnology, VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, St. Pietersnieuwstraat 33, 9000 Ghent, Belgium
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Krijgslaan 281-S9, 9000 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Technologiepark-Zwijnaarde 75, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, St. Pietersnieuwstraat 33, 9000 Ghent, Belgium
| | - Thilo Muth
- Bioinformatics Unit (MF 1), Department for Methods Development and Research Infrastructure, Robert Koch Institute, Nordufer 20, 13353 Berlin, Germany
- eScience Division (S.3), Federal Institute for Materials Research and Testing, Unter den Eichen 87, 12205 Berlin, Germany
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10
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Verheggen K, Raeder H, Berven FS, Martens L, Barsnes H, Vaudel M. Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows. Mass Spectrom Rev 2020; 39:292-306. [PMID: 28902424 DOI: 10.1002/mas.21543] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 07/05/2017] [Indexed: 06/07/2023]
Abstract
Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.
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Affiliation(s)
- Kenneth Verheggen
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Helge Raeder
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway
- Department of Pediatrics, Haukeland University Hospital, Bergen, Norway
| | - Frode S Berven
- Proteomics Unit, Department of Biomedicine, University of 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
| | - Harald Barsnes
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway
- Proteomics Unit, Department of Biomedicine, University of Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Norway
| | - Marc Vaudel
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway
- Proteomics Unit, Department of Biomedicine, University of Bergen, Norway
- Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway
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11
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Loo LSW, Vethe H, Soetedjo AAP, Paulo JA, Jasmen J, Jackson N, Bjørlykke Y, Valdez IA, Vaudel M, Barsnes H, Gygi SP, Raeder H, Teo AKK, Kulkarni RN. Dynamic proteome profiling of human pluripotent stem cell-derived pancreatic progenitors. Stem Cells 2020; 38:542-555. [PMID: 31828876 DOI: 10.1002/stem.3135] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 11/15/2019] [Indexed: 12/25/2022]
Abstract
A comprehensive characterization of the molecular processes controlling cell fate decisions is essential to derive stable progenitors and terminally differentiated cells that are functional from human pluripotent stem cells (hPSCs). Here, we report the use of quantitative proteomics to describe early proteome adaptations during hPSC differentiation toward pancreatic progenitors. We report that the use of unbiased quantitative proteomics allows the simultaneous profiling of numerous proteins at multiple time points, and is a valuable tool to guide the discovery of signaling events and molecular signatures underlying cellular differentiation. We also monitored the activity level of pathways whose roles are pivotal in the early pancreas differentiation, including the Hippo signaling pathway. The quantitative proteomics data set provides insights into the dynamics of the global proteome during the transition of hPSCs from a pluripotent state toward pancreatic differentiation.
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Affiliation(s)
- Larry Sai Weng Loo
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), A*STAR, Singapore.,School of Biological Sciences, Nanyang Technological University (NTU), Singapore
| | - Heidrun Vethe
- Section of Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts.,KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | | | - Joao A Paulo
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts
| | - Joanita Jasmen
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), A*STAR, Singapore
| | - Nicholas Jackson
- Section of Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts
| | - Yngvild Bjørlykke
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Ivan A Valdez
- Section of Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts
| | - Marc Vaudel
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Harald Barsnes
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway.,Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts
| | - Helge Raeder
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway.,Department of Pediatrics, Haukeland University Hospital, Bergen, Norway
| | - Adrian Kee Keong Teo
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), A*STAR, Singapore.,School of Biological Sciences, Nanyang Technological University (NTU), Singapore.,Departments of Biochemistry and Medicine, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore
| | - Rohit N Kulkarni
- Section of Islet Cell and Regenerative Biology, Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts
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12
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Hulstaert N, Shofstahl J, Sachsenberg T, Walzer M, Barsnes H, Martens L, Perez-Riverol Y. ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion. J Proteome Res 2019; 19:537-542. [PMID: 31755270 DOI: 10.1021/acs.jproteome.9b00328] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The field of computational proteomics is approaching the big data age, driven both by a continuous growth in the number of samples analyzed per experiment as well as by the growing amount of data obtained in each analytical run. In order to process these large amounts of data, it is increasingly necessary to use elastic compute resources such as Linux-based cluster environments and cloud infrastructures. Unfortunately, the vast majority of cross-platform proteomics tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Here, we present ThermoRawFileParser, an open-source, cross-platform tool that converts Thermo RAW files into open file formats such as MGF and the HUPO-PSI standard file format mzML. To ensure the broadest possible availability and to increase integration capabilities with popular workflow systems such as Galaxy or Nextflow, we have also built Conda package and BioContainers container around ThermoRawFileParser. In addition, we implemented a user-friendly interface (ThermoRawFileParserGUI) for those users not familiar with command-line tools. Finally, we performed a benchmark of ThermoRawFileParser and msconvert to verify that the converted mzML files contain reliable quantitative results.
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Affiliation(s)
- Niels Hulstaert
- VIB-UGent Center for Medical Biotechnology, VIB , Ghent B-9000 , Belgium.,Department of Biomolecular Medicine , Ghent University , Ghent B-9000 , Belgium
| | - Jim Shofstahl
- Thermo Fisher Scientific , 355 River Oaks Parkway , San Jose , California 95134 , United States
| | - Timo Sachsenberg
- Applied Bioinformatics, Department for Computer Science , University of Tuebingen , Sand 14 , 72076 Tuebingen , Germany
| | - Mathias Walzer
- European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD , United Kingdom
| | - Harald Barsnes
- Computational Biology Unit (CBU), Department of Informatics , University of Bergen , Bergen 5020 , Norway.,Proteomics Unit (PROBE), Department of Biomedicine , University of Bergen , Bergen 5020 , Norway
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB , Ghent B-9000 , Belgium.,Department of Biomolecular Medicine , Ghent University , Ghent B-9000 , Belgium
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD , United Kingdom
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13
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Burger B, Lereim RR, Berven FS, Barsnes H. Detecting single amino acids and small peptides by combining isobaric tags and peptidomics. Eur J Mass Spectrom (Chichester) 2019; 25:451-456. [PMID: 31189351 DOI: 10.1177/1469066719857006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Single amino acids and small endogenous peptides play important roles in maintaining a properly functioning organism. These molecules are however currently only routinely identified in targeted approaches. In a small proof-of-concept mass spectrometry experiment, we found that by combining isobaric tags and peptidomics, and by targeting singly charged molecules, we were able to identify a significant amount of single amino acids and small endogenous peptides using a basic mass-based identification approach. While there is still room for improvement, our simple test indicates that a limited amount of extra work when setting up the mass spectrometry experiment could potentially lead to a wealth of additional information.
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Affiliation(s)
- Bram Burger
- Department of Informatics, University of Bergen, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ragnhild Reehorst Lereim
- Department of Informatics, University of Bergen, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Frode S Berven
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Harald Barsnes
- Department of Informatics, University of Bergen, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
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14
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Sánchez LFH, Burger B, Horro C, Fabregat A, Johansson S, Njølstad PR, Barsnes H, Hermjakob H, Vaudel M. PathwayMatcher: proteoform-centric network construction enables fine-granularity multiomics pathway mapping. Gigascience 2019; 8:giz088. [PMID: 31363752 PMCID: PMC6667378 DOI: 10.1093/gigascience/giz088] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 06/03/2019] [Accepted: 06/30/2019] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Mapping biomedical data to functional knowledge is an essential task in bioinformatics and can be achieved by querying identifiers (e.g., gene sets) in pathway knowledge bases. However, the isoform and posttranslational modification states of proteins are lost when converting input and pathways into gene-centric lists. FINDINGS Based on the Reactome knowledge base, we built a network of protein-protein interactions accounting for the documented isoform and modification statuses of proteins. We then implemented a command line application called PathwayMatcher (github.com/PathwayAnalysisPlatform/PathwayMatcher) to query this network. PathwayMatcher supports multiple types of omics data as input and outputs the possibly affected biochemical reactions, subnetworks, and pathways. CONCLUSIONS PathwayMatcher enables refining the network representation of pathways by including proteoforms defined as protein isoforms with posttranslational modifications. The specificity of pathway analyses is hence adapted to different levels of granularity, and it becomes possible to distinguish interactions between different forms of the same protein.
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Affiliation(s)
- Luis Francisco Hernández Sánchez
- K.G. Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Children's Hospital, Haukeland University Hospital, 5021 Bergen, Norway
- Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, P.O Box 1400, 5021 Bergen, Norway
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Bram Burger
- Proteomics Unit, Department of Biomedicine, University of Bergen, Postbox 7804, 5020 Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, P.O. Box 7803, 5020 Bergen, Norway
| | - Carlos Horro
- Proteomics Unit, Department of Biomedicine, University of Bergen, Postbox 7804, 5020 Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, P.O. Box 7803, 5020 Bergen, Norway
| | - Antonio Fabregat
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Stefan Johansson
- K.G. Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Children's Hospital, Haukeland University Hospital, 5021 Bergen, Norway
- Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, P.O Box 1400, 5021 Bergen, Norway
| | - Pål Rasmus Njølstad
- K.G. Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Children's Hospital, Haukeland University Hospital, 5021 Bergen, Norway
- Department of Pediatrics, Haukeland University Hospital, 5021 Bergen, Norway
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen, Postbox 7804, 5020 Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, P.O. Box 7803, 5020 Bergen, Norway
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Beijing Proteome Research Center, National Center for Protein Sciences Beijing, No. 38, Life Science Park Road, Changping District, 102206 Beijing, China
| | - Marc Vaudel
- K.G. Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Children's Hospital, Haukeland University Hospital, 5021 Bergen, Norway
- Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, P.O Box 1400, 5021 Bergen, Norway
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15
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Binz PA, Shofstahl J, Vizcaíno JA, Barsnes H, Chalkley RJ, Menschaert G, Alpi E, Clauser K, Eng JK, Lane L, Seymour SL, Sánchez LFH, Mayer G, Eisenacher M, Perez-Riverol Y, Kapp EA, Mendoza L, Baker PR, Collins A, Van Den Bossche T, Deutsch EW. Proteomics Standards Initiative Extended FASTA Format. J Proteome Res 2019; 18:2686-2692. [PMID: 31081335 DOI: 10.1021/acs.jproteome.9b00064] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Mass-spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs) in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI extended FASTA format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backward compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff .
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Affiliation(s)
- Pierre-Alain Binz
- CHUV Centre Hospitalier Universitaire Vaudois , CH-1011 Lausanne 14 , Switzerland
| | - Jim Shofstahl
- Thermo Fisher Scientific , 355 River Oaks Parkway , San Jose , California 95134 , United States
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD , United Kingdom
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine , University of Bergen , N-5009 Bergen , Norway.,Computational Biology Unit, Department of Informatics , University of Bergen , N-5008 Bergen , Norway
| | - Robert J Chalkley
- University California at San Francisco , San Francisco , California 94143 , United States
| | - Gerben Menschaert
- Biobix, Department of Data Analysis and Mathematical Modelling , Ghent University , 9000 Ghent , Belgium
| | - Emanuele Alpi
- European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD , United Kingdom
| | - Karl Clauser
- Broad Institute , Cambridge , Massachusetts 02142 , United States
| | - Jimmy K Eng
- University of Washington , Seattle , Washington 98195 , United States
| | - Lydie Lane
- SIB Swiss Institute of Bioinformatics , CH-1211 Geneva 4 , Switzerland.,Department of Microbiology and Molecular Medicine, Faculty of Medicine , University of Geneva , CH-1211 Geneva 4 , Switzerland
| | - Sean L Seymour
- Seymour Data Science, LLC , San Francisco , California 95000 , United States
| | - Luis Francisco Hernández Sánchez
- K.G. Jebsen Center for Diabetes Research, Department of Clinical Science , University of Bergen , 5021 Bergen , Norway.,Center for Medical Genetics and Molecular Medicine , Haukeland University Hospital , 5021 Bergen , Norway
| | - Gerhard Mayer
- Medical Faculty, Medizinisches Proteom-Center , Ruhr University Bochum , D-44801 Bochum , Germany
| | - Martin Eisenacher
- Medical Faculty, Medizinisches Proteom-Center , Ruhr University Bochum , D-44801 Bochum , Germany
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD , United Kingdom
| | - Eugene A Kapp
- Walter & Eliza Hall Institute of Medical Research and the University of Melbourne , Melbourne , VIC 3052 , Australia
| | - Luis Mendoza
- Institute for Systems Biology , Seattle , Washington 98109 , United States
| | - Peter R Baker
- University California at San Francisco , San Francisco , California 94143 , United States
| | - Andrew Collins
- Department of Functional and Comparative Genomics, Institute of Integrated Biology , University of Liverpool , Liverpool L69 7ZB , United Kingdom
| | - Tim Van Den Bossche
- VIB-UGent Center for Medical Biotechnology , Ghent University , 9000 Ghent , Belgium
| | - Eric W Deutsch
- Institute for Systems Biology , Seattle , Washington 98109 , United States
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16
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Burger B, Hernández Sánchez LF, Lereim RR, Barsnes H, Vaudel M. Analyzing the Structure of Pathways and Its Influence on the Interpretation of Biomedical Proteomics Data Sets. J Proteome Res 2018; 17:3801-3809. [PMID: 30251541 DOI: 10.1021/acs.jproteome.8b00464] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Biochemical pathways are commonly used as a reference to conduct functional analysis on biomedical omics data sets, where experimental results are mapped to knowledgebases comprising known molecular interactions collected from the literature. Due to their central role, the content of the functional knowledgebases directly influences the outcome of pathway analyses. In this study, we investigate the structure of the current pathway knowledge, as exemplified by Reactome, discuss the consequences for biological interpretation, and outline possible improvements in the use of pathway knowledgebases. By providing a view of the underlying protein interaction network, we aim to help pathway analysis users manage their expectations and better identify possible artifacts in the results.
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Affiliation(s)
- Bram Burger
- Computational Biology Unit (CBU), Department of Informatics , University of Bergen , 5020 Bergen , Norway.,Proteomics Unit (PROBE), Department of Biomedicine , University of Bergen , 5020 Bergen , Norway
| | - Luis Francisco Hernández Sánchez
- KG Jebsen Center for Diabetes Research, Department of Clinical Science , University of Bergen , 5020 Bergen , Norway.,Center for Medical Genetics and Molecular Medicine , Haukeland University Hospital , 5020 Bergen , Norway
| | - Ragnhild Reehorst Lereim
- Computational Biology Unit (CBU), Department of Informatics , University of Bergen , 5020 Bergen , Norway.,Proteomics Unit (PROBE), Department of Biomedicine , University of Bergen , 5020 Bergen , Norway
| | - Harald Barsnes
- Computational Biology Unit (CBU), Department of Informatics , University of Bergen , 5020 Bergen , Norway.,Proteomics Unit (PROBE), Department of Biomedicine , University of Bergen , 5020 Bergen , Norway
| | - Marc Vaudel
- KG Jebsen Center for Diabetes Research, Department of Clinical Science , University of Bergen , 5020 Bergen , Norway.,Center for Medical Genetics and Molecular Medicine , Haukeland University Hospital , 5020 Bergen , Norway
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17
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Abstract
Mass-spectrometry-based proteomics has become the standard approach for identifying and quantifying proteins. A vital step consists of analyzing experimentally generated mass spectra to identify the underlying peptide sequences for later mapping to the originating proteins. We here present the latest developments in SearchGUI, a common open-source interface for the most frequently used freely available proteomics search and de novo engines that has evolved into a central component in numerous bioinformatics workflows.
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Affiliation(s)
| | - Marc Vaudel
- Center for Medical Genetics and Molecular Medicine , Haukeland University Hospital , 5021 Bergen , Norway
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18
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da Veiga Leprevost F, Grüning BA, Alves Aflitos S, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Vera Alvarez R, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics 2018; 33:2580-2582. [PMID: 28379341 PMCID: PMC5870671 DOI: 10.1093/bioinformatics/btx192] [Citation(s) in RCA: 133] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 03/29/2017] [Indexed: 12/16/2022] Open
Abstract
Motivation BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters). Availability and Implementation The software is freely available at github.com/BioContainers/.
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Affiliation(s)
| | - Björn A Grüning
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany.,Albert-Ludwigs-University, Department of Computer Science, Bioinformatics Group, Freiburg, Baden-Württemberg, 79110 Freiburg, Freiburg
| | - Saulo Alves Aflitos
- Wageningen Plant Research, Cluster Bioinformatics, Wageningen, 6700 AD, Gelderland, Netherlands
| | | | - Julian Uszkoreit
- Medizinisches Proteom-Center, Ruhr-University Bochum, Germany, 44801
| | - Harald Barsnes
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway.,Computational Biology Unit (CBU), Department of Informatics, University of Bergen, Bergen, Norway
| | - Marc Vaudel
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway.,(I) KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway, 5020; (II) Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway, 5020
| | - Pablo Moreno
- EMBL Outstation, European Bioinformatics Institute, Proteomics Services, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Laurent Gatto
- Computational Proteomics Unit and Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Jonas Weber
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Mingze Bai
- EMBL Outstation, European Bioinformatics Institute, Proteomics Services, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Rafael C Jimenez
- EMBL Outstation, European Bioinformatics Institute, Proteomics Services, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Timo Sachsenberg
- Universität Tübingen, Wilhelm Schickard Institut für Informatik, Applied Bioinformatics Group,D-72076 Tübingen, Germany
| | - Julianus Pfeuffer
- Eberhard-Karls-Universität Tübingen, Department of Computer Science, Applied bioinformatics, 72076 Tübingen
| | - Roberto Vera Alvarez
- Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Johannes Griss
- EMBL Outstation, European Bioinformatics Institute, Proteomics Services, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.,Division of Immunology, Allergy and Infectious Diseases, Department of Dermatology, Medical University of Vienna, Austria
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yasset Perez-Riverol
- EMBL Outstation, European Bioinformatics Institute, Proteomics Services, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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19
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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20
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Perez‐Riverol Y, Ternent T, Koch M, Barsnes H, Vrousgou O, Jupp S, Vizcaíno JA. OLS Client and OLS Dialog: Open Source Tools to Annotate Public Omics Datasets. Proteomics 2017; 17:1700244. [PMID: 28792687 PMCID: PMC5707441 DOI: 10.1002/pmic.201700244] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Revised: 07/12/2017] [Indexed: 01/12/2023]
Abstract
The availability of user-friendly software to annotate biological datasets and experimental details is becoming essential in data management practices, both in local storage systems and in public databases. The Ontology Lookup Service (OLS, http://www.ebi.ac.uk/ols) is a popular centralized service to query, browse and navigate biomedical ontologies and controlled vocabularies. Recently, the OLS framework has been completely redeveloped (version 3.0), including enhancements in the data model, like the added support for Web Ontology Language based ontologies, among many other improvements. However, the new OLS is not backwards compatible and new software tools are needed to enable access to this widely used framework now that the previous version is no longer available. We here present the OLS Client as a free, open-source Java library to retrieve information from the new version of the OLS. It enables rapid tool creation by providing a robust, pluggable programming interface and common data model to programmatically access the OLS. The library has already been integrated and is routinely used by several bioinformatics resources and related data annotation tools. Secondly, we also introduce an updated version of the OLS Dialog (version 2.0), a Java graphical user interface that can be easily plugged into Java desktop applications to access the OLS. The software and related documentation are freely available at https://github.com/PRIDE-Utilities/ols-client and https://github.com/PRIDE-Toolsuite/ols-dialog.
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Affiliation(s)
- Yasset Perez‐Riverol
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Trust Genome CampusHinxtonCambridgeUK
| | - Tobias Ternent
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Trust Genome CampusHinxtonCambridgeUK
| | - Maximilian Koch
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Trust Genome CampusHinxtonCambridgeUK
| | - Harald Barsnes
- Proteomics Unit, Department of BiomedicineUniversity of BergenBergenNorway
- Computational Biology Unit, Department of InformaticsUniversity of BergenBergenNorway
| | - Olga Vrousgou
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Trust Genome CampusHinxtonCambridgeUK
| | - Simon Jupp
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Trust Genome CampusHinxtonCambridgeUK
| | - Juan Antonio Vizcaíno
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI), Wellcome Trust Genome CampusHinxtonCambridgeUK
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21
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Vizcaíno JA, Mayer G, Perkins S, Barsnes H, Vaudel M, Perez-Riverol Y, Ternent T, Uszkoreit J, Eisenacher M, Fischer L, Rappsilber J, Netz E, Walzer M, Kohlbacher O, Leitner A, Chalkley RJ, Ghali F, Martínez-Bartolomé S, Deutsch EW, Jones AR. The mzIdentML Data Standard Version 1.2, Supporting Advances in Proteome Informatics. Mol Cell Proteomics 2017; 16:1275-1285. [PMID: 28515314 PMCID: PMC5500760 DOI: 10.1074/mcp.m117.068429] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 05/15/2017] [Indexed: 12/31/2022] Open
Abstract
The first stable version of the Proteomics Standards Initiative mzIdentML open data standard (version 1.1) was published in 2012-capturing the outputs of peptide and protein identification software. In the intervening years, the standard has become well-supported in both commercial and open software, as well as a submission and download format for public repositories. Here we report a new release of mzIdentML (version 1.2) that is required to keep pace with emerging practice in proteome informatics. New features have been added to support: (1) scores associated with localization of modifications on peptides; (2) statistics performed at the level of peptides; (3) identification of cross-linked peptides; and (4) support for proteogenomics approaches. In addition, there is now improved support for the encoding of de novo sequencing of peptides, spectral library searches, and protein inference. As a key point, the underlying XML schema has only undergone very minor modifications to simplify as much as possible the transition from version 1.1 to version 1.2 for implementers, but there have been several notable updates to the format specification, implementation guidelines, controlled vocabularies and validation software. mzIdentML 1.2 can be described as backwards compatible, in that reading software designed for mzIdentML 1.1 should function in most cases without adaptation. We anticipate that these developments will provide a continued stable base for software teams working to implement the standard. All the related documentation is accessible at http://www.psidev.info/mzidentml.
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Affiliation(s)
- Juan Antonio Vizcaíno
- From the ‡European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Gerhard Mayer
- §Medizinisches Proteom Center (MPC), Ruhr-Universität Bochum, D-44801 Bochum, Germany
| | - Simon Perkins
- ¶Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Harald Barsnes
- ‖Proteomics Unit, Department of Biomedicine, University of Bergen, Norway
- **Computational Biology Unit, Department of Informatics, University of Bergen, Norway
- ‡‡KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway
| | - Marc Vaudel
- ‖Proteomics Unit, Department of Biomedicine, University of Bergen, Norway
- ‡‡KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Norway
- §§Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway
| | - Yasset Perez-Riverol
- From the ‡European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Tobias Ternent
- From the ‡European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - Julian Uszkoreit
- §Medizinisches Proteom Center (MPC), Ruhr-Universität Bochum, D-44801 Bochum, Germany
| | - Martin Eisenacher
- §Medizinisches Proteom Center (MPC), Ruhr-Universität Bochum, D-44801 Bochum, Germany
| | - Lutz Fischer
- ¶¶Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
| | - Juri Rappsilber
- ¶¶Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
- ‖‖Chair of Bioanalytics, Institute of Biotechnology Technische Universität Berlin, 13355 Berlin, Germany
| | - Eugen Netz
- Biomolecular Interactions group, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
| | - Mathias Walzer
- Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany
| | - Oliver Kohlbacher
- Biomolecular Interactions group, Max Planck Institute for Developmental Biology, Tübingen D-72076, Germany
- Center for Bioinformatics, University of Tübingen, 72076 Tübingen, Germany
- Dept. of Computer Science, University of Tübingen, Germany
- Quantitative Biology Center, University of Tübingen, Germany
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Auguste-Piccard-Hof 1, 8093 Zurich, Switzerland
| | - Robert J Chalkley
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94143
| | - Fawaz Ghali
- ¶Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Salvador Martínez-Bartolomé
- Department of Chemical Physiology, The Scripps Research Institute, 10550, N. Torrey Pines Rd., La Jolla, California, 92037
| | | | - Andrew R Jones
- ¶Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK;
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22
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Kopczynski D, Barsnes H, Njølstad PR, Sickmann A, Vaudel M, Ahrends R. PeptideMapper: efficient and versatile amino acid sequence and tag mapping. Bioinformatics 2017; 33:2042-2044. [DOI: 10.1093/bioinformatics/btx122] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 03/01/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Dominik Kopczynski
- Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V, Dortmund, Germany
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Pål R Njølstad
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Pediatrics, Haukeland University Hospital, Bergen, Norway
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V, Dortmund, Germany
- College of Physical Sciences, University of Aberdeen, Old Aberdeen, UK
- Medizinische Fakultät, Ruhr-Universität Bochum, Bochum, Germany
| | - 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
| | - Robert Ahrends
- Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V, Dortmund, Germany
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23
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Opsahl JA, Vaudel M, Guldbrandsen A, Aasebø E, Van Pesch V, Franciotta D, Myhr KM, Barsnes H, Berle M, Torkildsen Ø, Kroksveen AC, Berven FS. Label-free analysis of human cerebrospinal fluid addressing various normalization strategies and revealing protein groups affected by multiple sclerosis. Proteomics 2016; 16:1154-65. [PMID: 26841090 DOI: 10.1002/pmic.201500284] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Revised: 12/08/2015] [Accepted: 01/28/2016] [Indexed: 11/05/2022]
Abstract
The aims of the study were to: (i) identify differentially regulated proteins in cerebrospinal fluid (CSF) between multiple sclerosis (MS) patients and non-MS controls; (ii) examine the effect of matching the CSF samples on either total protein amount or volume, and compare four protein normalization strategies for CSF protein quantification. CSF from MS patients (n = 37) and controls (n = 64), consisting of other noninflammatory neurological diseases (n = 50) and non neurological spinal anesthetic subjects (n = 14), were analyzed using label-free proteomics, quantifying almost 800 proteins. In total, 122 proteins were significantly regulated (p < 0.05), where 77 proteins had p-value <0.01 or AUC value >0.75. Hierarchical clustering indicated that there were two main groups of MS patients, those with increased levels of inflammatory response proteins and decreased levels of proteins involved in neuronal tissue development (n = 30), and those with normal protein levels for both of these protein groups (n = 7). The main subgroup of controls clustering with the MS patients showing increased inflammation and decreased neuronal tissue development were patients suffering from chronic fatigue. Our data indicate that the preferable way to quantify proteins in CSF is to first match the samples on total protein amount and then normalize the data based on the median intensities, preferably from the CNS-enriched proteins.
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Affiliation(s)
- Jill A Opsahl
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway.,The KG Jebsen Centre for MS-research, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Marc Vaudel
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Astrid Guldbrandsen
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway.,The KG Jebsen Centre for MS-research, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Elise Aasebø
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Vincent Van Pesch
- Neurology Department, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Diego Franciotta
- Laboratory of Neuroimmunology, IRCCS, "C. Mondino" National Neurological Institute, Pavia, Italy
| | - Kjell-Morten Myhr
- The KG Jebsen Centre for MS-research, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Harald Barsnes
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway.,Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Magnus Berle
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway.,Surgical Clinic, Haukeland University Hospital, Bergen, Norway
| | - Øivind Torkildsen
- The KG Jebsen Centre for MS-research, Department of Clinical Medicine, University of Bergen, Bergen, Norway.,The Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Ann C Kroksveen
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway.,The KG Jebsen Centre for MS-research, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Frode S Berven
- Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway.,The KG Jebsen Centre for MS-research, Department of Clinical Medicine, University of Bergen, Bergen, Norway
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24
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Guldbrandsen A, Farag Y, Kroksveen AC, Oveland E, Lereim RR, Opsahl JA, Myhr KM, Berven FS, Barsnes H. CSF-PR 2.0: An Interactive Literature Guide to Quantitative Cerebrospinal Fluid Mass Spectrometry Data from Neurodegenerative Disorders. Mol Cell Proteomics 2016; 16:300-309. [PMID: 27890865 DOI: 10.1074/mcp.o116.064477] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 11/18/2016] [Indexed: 01/23/2023] Open
Abstract
The rapidly growing number of biomedical studies supported by mass spectrometry based quantitative proteomics data has made it increasingly difficult to obtain an overview of the current status of the research field. A better way of organizing the biomedical proteomics information from these studies and making it available to the research community is therefore called for. In the presented work, we have investigated scientific publications describing the analysis of the cerebrospinal fluid proteome in relation to multiple sclerosis, Parkinson's disease and Alzheimer's disease. Based on a detailed set of filtering criteria we extracted 85 data sets containing quantitative information for close to 2000 proteins. This information was made available in CSF-PR 2.0 (http://probe.uib.no/csf-pr-2.0), which includes novel approaches for filtering, visualizing and comparing quantitative proteomics information in an interactive and user-friendly environment. CSF-PR 2.0 will be an invaluable resource for anyone interested in quantitative proteomics on cerebrospinal fluid.
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Affiliation(s)
- Astrid Guldbrandsen
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5009 Bergen, Norway.,§KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway
| | - Yehia Farag
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5009 Bergen, Norway.,§KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway
| | - Ann Cathrine Kroksveen
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5009 Bergen, Norway.,§KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway
| | - Eystein Oveland
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5009 Bergen, Norway.,§KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway
| | - Ragnhild R Lereim
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5009 Bergen, Norway.,§KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway
| | - Jill A Opsahl
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5009 Bergen, Norway.,§KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway
| | - Kjell-Morten Myhr
- §KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway.,¶Norwegian Multiple Sclerosis Registry and Biobank, Haukeland University Hospital, 5021 Bergen, Norway
| | - Frode S Berven
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5009 Bergen, Norway; .,§KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway.,‖Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Harald Barsnes
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, 5009 Bergen, Norway.,**Department of Clinical Science, University of Bergen, 5020 Bergen, Norway.,‡‡Computational Biology Unit, Department of Informatics, University of Bergen, 5020 Bergen, Norway
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25
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Kroksveen AC, Guldbrandsen A, Vaudel M, Lereim RR, Barsnes H, Myhr KM, Torkildsen Ø, Berven FS. In-Depth Cerebrospinal Fluid Quantitative Proteome and Deglycoproteome Analysis: Presenting a Comprehensive Picture of Pathways and Processes Affected by Multiple Sclerosis. J Proteome Res 2016; 16:179-194. [PMID: 27728768 DOI: 10.1021/acs.jproteome.6b00659] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
In the current study, we conducted a quantitative in-depth proteome and deglycoproteome analysis of cerebrospinal fluid (CSF) from relapsing-remitting multiple sclerosis (RRMS) and neurological controls using mass spectrometry and pathway analysis. More than 2000 proteins and 1700 deglycopeptides were quantified, with 484 proteins and 180 deglycopeptides significantly changed between pools of RRMS and pools of controls. Approximately 300 of the significantly changed proteins were assigned to various biological processes including inflammation, extracellular matrix organization, cell adhesion, immune response, and neuron development. Ninety-six significantly changed deglycopeptides mapped to proteins that were not found changed in the global protein study. In addition, four mapped to the proteins oligo-myelin glycoprotein and noelin, which were found oppositely changed in the global study. Both are ligands to the nogo receptor, and the glycosylation of these proteins appears to be affected by RRMS. Our study gives the most extensive overview of the RRMS affected processes observed from the CSF proteome to date, and the list of differential proteins will have great value for selection of biomarker candidates for further verification.
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Affiliation(s)
- Ann Cathrine Kroksveen
- Proteomics Unit (PROBE), Department of Biomedicine, ‡The KG Jebsen Centre for MS Research, Department of Clinical Medicine, §KG Jebsen Center for Diabetes Research, Department of Clinical Science, and ⊥Computational Biology Unit, Department of Informatics, University of Bergen , Bergen N-5009, Norway.,Center for Medical Genetics and Molecular Medicine and ∥The Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital , Bergen N-5021, Norway
| | - Astrid Guldbrandsen
- Proteomics Unit (PROBE), Department of Biomedicine, ‡The KG Jebsen Centre for MS Research, Department of Clinical Medicine, §KG Jebsen Center for Diabetes Research, Department of Clinical Science, and ⊥Computational Biology Unit, Department of Informatics, University of Bergen , Bergen N-5009, Norway.,Center for Medical Genetics and Molecular Medicine and ∥The Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital , Bergen N-5021, Norway
| | - Marc Vaudel
- Proteomics Unit (PROBE), Department of Biomedicine, ‡The KG Jebsen Centre for MS Research, Department of Clinical Medicine, §KG Jebsen Center for Diabetes Research, Department of Clinical Science, and ⊥Computational Biology Unit, Department of Informatics, University of Bergen , Bergen N-5009, Norway.,Center for Medical Genetics and Molecular Medicine and ∥The Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital , Bergen N-5021, Norway
| | - Ragnhild Reehorst Lereim
- Proteomics Unit (PROBE), Department of Biomedicine, ‡The KG Jebsen Centre for MS Research, Department of Clinical Medicine, §KG Jebsen Center for Diabetes Research, Department of Clinical Science, and ⊥Computational Biology Unit, Department of Informatics, University of Bergen , Bergen N-5009, Norway.,Center for Medical Genetics and Molecular Medicine and ∥The Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital , Bergen N-5021, Norway
| | - Harald Barsnes
- Proteomics Unit (PROBE), Department of Biomedicine, ‡The KG Jebsen Centre for MS Research, Department of Clinical Medicine, §KG Jebsen Center for Diabetes Research, Department of Clinical Science, and ⊥Computational Biology Unit, Department of Informatics, University of Bergen , Bergen N-5009, Norway.,Center for Medical Genetics and Molecular Medicine and ∥The Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital , Bergen N-5021, Norway
| | - Kjell-Morten Myhr
- Proteomics Unit (PROBE), Department of Biomedicine, ‡The KG Jebsen Centre for MS Research, Department of Clinical Medicine, §KG Jebsen Center for Diabetes Research, Department of Clinical Science, and ⊥Computational Biology Unit, Department of Informatics, University of Bergen , Bergen N-5009, Norway.,Center for Medical Genetics and Molecular Medicine and ∥The Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital , Bergen N-5021, Norway
| | - Øivind Torkildsen
- Proteomics Unit (PROBE), Department of Biomedicine, ‡The KG Jebsen Centre for MS Research, Department of Clinical Medicine, §KG Jebsen Center for Diabetes Research, Department of Clinical Science, and ⊥Computational Biology Unit, Department of Informatics, University of Bergen , Bergen N-5009, Norway.,Center for Medical Genetics and Molecular Medicine and ∥The Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital , Bergen N-5021, Norway
| | - Frode S Berven
- Proteomics Unit (PROBE), Department of Biomedicine, ‡The KG Jebsen Centre for MS Research, Department of Clinical Medicine, §KG Jebsen Center for Diabetes Research, Department of Clinical Science, and ⊥Computational Biology Unit, Department of Informatics, University of Bergen , Bergen N-5009, Norway.,Center for Medical Genetics and Molecular Medicine and ∥The Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital , Bergen N-5021, Norway
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26
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Yılmaz Ş, Victor B, Hulstaert N, Vandermarliere E, Barsnes H, Degroeve S, Gupta S, Sticker A, Gabriël S, Dorny P, Palmblad M, Martens L. A Pipeline for Differential Proteomics in Unsequenced Species. J Proteome Res 2016; 15:1963-70. [DOI: 10.1021/acs.jproteome.6b00140] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Şule Yılmaz
- Medical Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Bioinformatics
Institute Ghent, Ghent University, B-9052 Ghent, Belgium
| | - Bjorn Victor
- Veterinary
Helminthology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, 2000 Antwerp, Belgium
| | - Niels Hulstaert
- Medical Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Bioinformatics
Institute Ghent, Ghent University, B-9052 Ghent, Belgium
| | - Elien Vandermarliere
- Medical Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Bioinformatics
Institute Ghent, Ghent University, B-9052 Ghent, Belgium
| | - Harald Barsnes
- Proteomics
Unit (PROBE), Department of Biomedicine, University of Bergen, Jonas Liesvei 91, N-5009 Bergen, Norway
| | - Sven Degroeve
- Medical Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Bioinformatics
Institute Ghent, Ghent University, B-9052 Ghent, Belgium
| | - Surya Gupta
- Medical Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Bioinformatics
Institute Ghent, Ghent University, B-9052 Ghent, Belgium
| | - Adriaan Sticker
- Medical Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Bioinformatics
Institute Ghent, Ghent University, B-9052 Ghent, Belgium
- Department
of Applied Mathematics, Computer Science, and Statistics, Ghent University, B-9000 Ghent, Belgium
| | - Sarah Gabriël
- Veterinary
Helminthology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, 2000 Antwerp, Belgium
| | - Pierre Dorny
- Veterinary
Helminthology Unit, Department of Biomedical Sciences, Institute of Tropical Medicine, 2000 Antwerp, Belgium
| | - Magnus Palmblad
- Center
for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Lennart Martens
- Medical Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, B-9000 Ghent, Belgium
- Bioinformatics
Institute Ghent, Ghent University, B-9052 Ghent, Belgium
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Abstract
Targeting subproteomes is a good strategy to decrease the complexity of a sample, for example in body fluid biomarker studies. Glycoproteins are proteins with carbohydrates of varying size and structure attached to the polypeptide chain, and it has been shown that glycosylation plays essential roles in several vital cellular processes, making glycosylation a particularly interesting field of study. Here, we describe a method for the enrichment of glycosylated peptides from trypsin digested proteins in human cerebrospinal fluid. We also describe how to perform the data analysis on the mass spectrometry data for such samples, focusing on site-specific identification of glycosylation sites, using user friendly open source software.
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Affiliation(s)
- Astrid Guldbrandsen
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- KG Jebsen Center for Diabetes Research, Department of Clinical Sciences, University of Bergen, Bergen, Norway
| | - Ann Cathrine Kroksveen
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Frode S Berven
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Marc Vaudel
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway.
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29
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Vaudel M, Verheggen K, Csordas A, Raeder H, Berven FS, Martens L, Vizcaíno JA, Barsnes H. Exploring the potential of public proteomics data. Proteomics 2016; 16:214-25. [PMID: 26449181 PMCID: PMC4738454 DOI: 10.1002/pmic.201500295] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Revised: 08/25/2015] [Accepted: 09/28/2015] [Indexed: 12/22/2022]
Abstract
In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.
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Affiliation(s)
- Marc Vaudel
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Kenneth Verheggen
- Medical Biotechnology Center, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Attila Csordas
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Helge Raeder
- Department of Clinical Science, KG Jebsen Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Frode S Berven
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Department of Clinical Medicine, KG Jebsen Centre for Multiple Sclerosis Research, University of Bergen, Bergen, Norway
| | - Lennart Martens
- Medical Biotechnology Center, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Juan A Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Department of Clinical Science, KG Jebsen Center for Diabetes Research, University of Bergen, Bergen, Norway
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30
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Lereim RR, Oveland E, Berven FS, Vaudel M, Barsnes H. Visualization, Inspection and Interpretation of Shotgun Proteomics Identification Results. Modern Proteomics – Sample Preparation, Analysis and Practical Applications 2016; 919:227-235. [DOI: 10.1007/978-3-319-41448-5_11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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31
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Abstract
In quantitative proteomics, large lists of identified and quantified proteins are used to answer biological questions in a systemic approach. However, working with such extensive datasets can be challenging, especially when complex experimental designs are involved. Here, we demonstrate how to post-process large quantitative datasets, detect proteins of interest, and annotate the data with biological knowledge. The protocol presented can be achieved without advanced computational knowledge thanks to the user-friendly Perseus interface (available from the MaxQuant website, www.maxquant.org ). Various visualization techniques facilitating the interpretation of quantitative results in complex biological systems are also highlighted.
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Affiliation(s)
- Elise Aasebø
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Frode S Berven
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital, Bergen, Norway
| | - Frode Selheim
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Marc Vaudel
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway.
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32
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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] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Verheggen K, Maddelein D, Hulstaert N, Martens L, Barsnes H, Vaudel M. Pladipus Enables Universal Distributed Computing in Proteomics Bioinformatics. J Proteome Res 2015; 15:707-12. [DOI: 10.1021/acs.jproteome.5b00850] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Kenneth Verheggen
- Medical
Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Bioinformatics
Institute Ghent, Ghent University, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
| | - Davy Maddelein
- Medical
Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Bioinformatics
Institute Ghent, Ghent University, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
| | - Niels Hulstaert
- Medical
Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Bioinformatics
Institute Ghent, Ghent University, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
| | - Lennart Martens
- Medical
Biotechnology Center, VIB, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Department
of Biochemistry, Ghent University, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
- Bioinformatics
Institute Ghent, Ghent University, Albert Baertsoenkaai 3, Ghent B-9000, Belgium
| | - Harald Barsnes
- Proteomics
Unit, Department of Biomedicine, University of Bergen, Postboks 7804, N-5020 Bergen, Norway
- KG
Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Postboks 7804, N-5020 Bergen, Norway
| | - Marc Vaudel
- Proteomics
Unit, Department of Biomedicine, University of Bergen, Postboks 7804, N-5020 Bergen, Norway
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Kroksveen AC, Jaffe JD, Aasebø E, Barsnes H, Bjørlykke Y, Franciotta D, Keshishian H, Myhr KM, Opsahl JA, van Pesch V, Teunissen CE, Torkildsen Ø, Ulvik RJ, Vethe H, Carr SA, Berven FS. Quantitative proteomics suggests decrease in the secretogranin-1 cerebrospinal fluid levels during the disease course of multiple sclerosis. Proteomics 2015; 15:3361-9. [DOI: 10.1002/pmic.201400142] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Revised: 03/12/2015] [Accepted: 07/01/2015] [Indexed: 11/10/2022]
Affiliation(s)
- Ann C. Kroksveen
- The KG Jebsen Centre for MS-research; Department of Clinical Medicine; University of Bergen; Bergen Norway
- Proteomics Unit (PROBE); Department of Biomedicine; University of Bergen; Bergen Norway
| | - Jacob D. Jaffe
- Broad Institute of MIT and Harvard; 7 Cambridge Center; Cambridge MA USA
| | - Elise Aasebø
- Proteomics Unit (PROBE); Department of Biomedicine; University of Bergen; Bergen Norway
| | - Harald Barsnes
- Proteomics Unit (PROBE); Department of Biomedicine; University of Bergen; Bergen Norway
- Computational Biology Unit, Department of Informatics; University of Bergen; Bergen Norway
| | - Yngvild Bjørlykke
- Proteomics Unit (PROBE); Department of Biomedicine; University of Bergen; Bergen Norway
- Department of Clinical Science; University of Bergen; Bergen Norway
| | - Diego Franciotta
- Laboratory of Neuroimmunology; “C. Mondino” National Neurological Institute; Pavia Italy
| | - Hasmik Keshishian
- Broad Institute of MIT and Harvard; 7 Cambridge Center; Cambridge MA USA
| | - Kjell-Morten Myhr
- The KG Jebsen Centre for MS-research; Department of Clinical Medicine; University of Bergen; Bergen Norway
- The Norwegian Multiple Sclerosis Competence Centre; Department of Neurology; Haukeland University Hospital; Bergen Norway
| | - Jill A. Opsahl
- The KG Jebsen Centre for MS-research; Department of Clinical Medicine; University of Bergen; Bergen Norway
- Proteomics Unit (PROBE); Department of Biomedicine; University of Bergen; Bergen Norway
| | - Vincent van Pesch
- Neurochemistry Unit; Institute of Neuroscience, Université Catholique de Louvain; Brussels Belgium
| | - Charlotte E. Teunissen
- Neurochemistry Laboratory and Biobank; Department of Clinical Chemistry; VU University Medical Center; Amsterdam The Netherlands
| | - Øivind Torkildsen
- The KG Jebsen Centre for MS-research; Department of Clinical Medicine; University of Bergen; Bergen Norway
- The Norwegian Multiple Sclerosis Competence Centre; Department of Neurology; Haukeland University Hospital; Bergen Norway
| | - Rune J. Ulvik
- Department of Clinical Medicine; University of Bergen; Bergen Norway
- Laboratory of Clinical Biochemistry; Haukeland University Hospital; Bergen Norway
| | - Heidrun Vethe
- Proteomics Unit (PROBE); Department of Biomedicine; University of Bergen; Bergen Norway
- Department of Clinical Science; University of Bergen; Bergen Norway
| | - Steven A. Carr
- Broad Institute of MIT and Harvard; 7 Cambridge Center; Cambridge MA USA
| | - Frode S. Berven
- The KG Jebsen Centre for MS-research; Department of Clinical Medicine; University of Bergen; Bergen Norway
- Proteomics Unit (PROBE); Department of Biomedicine; University of Bergen; Bergen Norway
- The Norwegian Multiple Sclerosis Competence Centre; Department of Neurology; Haukeland University Hospital; Bergen Norway
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Vaudel M, Barsnes H, Bjerkvig R, Bikfalvi A, Selheim F, Berven FS, Daubon T. Practical Considerations for Omics Experiments in Biomedical Sciences. Curr Pharm Biotechnol 2015; 17:105-14. [PMID: 26278526 DOI: 10.2174/1389201016666150817095348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Revised: 07/07/2015] [Accepted: 07/27/2015] [Indexed: 11/22/2022]
Abstract
Modern analytical techniques provide an unprecedented insight to biomedical samples, allowing an in depth characterization of cells or body fluids, to the level of genes, transcripts, peptides, proteins, metabolites, or metallic ions. The fine grained picture provided by such approaches holds the promise for a better understanding of complex pathologies, and consequently the personalization of diagnosis, prognosis and treatment procedures. In practice however, technical limitations restrict the resolution of the acquired data, and thus of downstream biomedical inference. As a result, the study of complex diseases like leukemia and other types of cancer is impaired by the high heterogeneity of pathologies as well as patient profiles. In this review, we propose an introduction to the general approach of characterizing samples and inferring biomedical results. We highlight the main limitations of the technique with regards to complex and heterogeneous pathologies, and provide ways to overcome these by improving the ability of experiments in discriminating samples.
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Affiliation(s)
- Marc Vaudel
- Proteomics Unit, Department of Biomedicine, University of Bergen, Norway; Jones Liesvei 91, N-5009 Bergen, Norway.
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36
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Oveland E, Muth T, Rapp E, Martens L, Berven FS, Barsnes H. Viewing the proteome: how to visualize proteomics data? Proteomics 2015; 15:1341-55. [PMID: 25504833 DOI: 10.1002/pmic.201400412] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2014] [Revised: 10/23/2014] [Accepted: 12/05/2014] [Indexed: 01/18/2023]
Abstract
Proteomics has become one of the main approaches for analyzing and understanding biological systems. Yet similar to other high-throughput analysis methods, the presentation of the large amounts of obtained data in easily interpretable ways remains challenging. In this review, we present an overview of the different ways in which proteomics software supports the visualization and interpretation of proteomics data. The unique challenges and current solutions for visualizing the different aspects of proteomics data, from acquired spectra via protein identification and quantification to pathway analysis, are discussed, and examples of the most useful visualization approaches are highlighted. Finally, we offer our ideas about future directions for proteomics data visualization.
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Affiliation(s)
- Eystein Oveland
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway; KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
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Barsnes H, Vaudel M, Martens L. JSparklines: Making tabular proteomics data come alive. Proteomics 2015; 15:1428-31. [DOI: 10.1002/pmic.201400356] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2014] [Revised: 09/15/2014] [Accepted: 11/20/2014] [Indexed: 12/30/2022]
Affiliation(s)
- Harald Barsnes
- Proteomics Unit, Department of Biomedicine; University of Bergen; Norway
| | - Marc Vaudel
- Proteomics Unit, Department of Biomedicine; University of Bergen; Norway
| | - Lennart Martens
- Department of Medical Protein Research; Ghent University; Ghent Belgium
- Department of Biochemistry; Ghent University; Ghent Belgium
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38
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Bjorlykke Y, Vethe H, Vaudel M, Barsnes H, Berven FS, Tjora E, Raeder H. Carboxyl-Ester Lipase Maturity-Onset Diabetes of the Young Disease Protein Biomarkers in Secretin-Stimulated Duodenal Juice. J Proteome Res 2014; 14:521-30. [DOI: 10.1021/pr500750z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Yngvild Bjorlykke
- KG
Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Jonas Lies Vei 65, Bergen 5021, Norway
- Department
of Pediatrics, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Heidrun Vethe
- KG
Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Jonas Lies Vei 65, Bergen 5021, Norway
- Department
of Pediatrics, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Marc Vaudel
- Proteomics
Unit (PROBE), Department of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen 5009, Norway
| | - Harald Barsnes
- Proteomics
Unit (PROBE), Department of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen 5009, Norway
| | - Frode S. Berven
- Proteomics
Unit (PROBE), Department of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen 5009, Norway
| | - Erling Tjora
- Department
of Pediatrics, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Helge Raeder
- KG
Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Jonas Lies Vei 65, Bergen 5021, Norway
- Department
of Pediatrics, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
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39
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Guldbrandsen A, Vethe H, Farag Y, Oveland E, Garberg H, Berle M, Myhr KM, Opsahl JA, Barsnes H, Berven FS. In-depth characterization of the cerebrospinal fluid (CSF) proteome displayed through the CSF proteome resource (CSF-PR). Mol Cell Proteomics 2014; 13:3152-63. [PMID: 25038066 PMCID: PMC4223498 DOI: 10.1074/mcp.m114.038554] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. All mapping results are freely available via the new CSF Proteome Resource (http://probe.uib.no/csf-pr), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.
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Affiliation(s)
- Astrid Guldbrandsen
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway; §KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Heidrun Vethe
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Yehia Farag
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway; ¶Department of Informatics, University of Bergen, Bergen, Norway
| | - Eystein Oveland
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway; §KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, Bergen, Norway; ‖Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Hilde Garberg
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Magnus Berle
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway; **Surgical Clinic, Haukeland University Hospital, Bergen, Norway
| | - Kjell-Morten Myhr
- §KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, Bergen, Norway; ‡‡Norwegian Multiple Sclerosis Registry and Biobank, Haukeland University Hospital, Bergen, Norway
| | - Jill A Opsahl
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway; §KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Harald Barsnes
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Frode S Berven
- From the ‡Proteomics Unit (PROBE), Department of Biomedicine, University of Bergen, Bergen, Norway; §KG Jebsen Centre for Multiple Sclerosis Research, Department of Clinical Medicine, University of Bergen, Bergen, Norway; §§Norwegian Multiple Sclerosis Competence Centre, Department of Neurology, Haukeland University Hospital, Bergen, Norway.
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Vaudel M, Barsnes H, Martens L, Berven FS. Bioinformatics for proteomics: opportunities at the interface between the scientists, their experiments, and the community. Methods Mol Biol 2014; 1156:239-48. [PMID: 24791993 DOI: 10.1007/978-1-4939-0685-7_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Abstract
Within the last decade, bioinformatics has moved from command line scripts dedicated to single experiments towards production grade software integrated in experimental workflows providing a rich environment for biological investigation. Located at the interface between the scientists, their experiments, and the community, bioinformatics acts as a gateway to a wide source of information. This chapter does not list tools and methods, but rather hints at how bioinformatics can help in improving biological projects, all the way from their initial design to the dissemination of the results.
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Affiliation(s)
- Marc Vaudel
- Proteomics Unit, Department of Biomedicine, University of Bergen, Jonas Liesvei 91, Bergen, 5009, Norway,
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41
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Vaudel M, Venne AS, Berven FS, Zahedi RP, Martens L, Barsnes H. Shedding light on black boxes in protein identification. Proteomics 2014; 14:1001-5. [DOI: 10.1002/pmic.201300488] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Revised: 01/10/2014] [Accepted: 01/22/2014] [Indexed: 12/28/2022]
Affiliation(s)
- Marc Vaudel
- Proteomics Unit; Department of Biomedicine; University of Bergen; Norway
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V; Dortmund Germany
| | - A. Saskia Venne
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V; Dortmund Germany
| | - Frode S. Berven
- Proteomics Unit; Department of Biomedicine; University of Bergen; Norway
- Department of Clinical Medicine; The KG Jebsen Centre for MS-research; University of Bergen; Bergen Norway
- Department of Neurology; The Norwegian Multiple Sclerosis Competence Centre; Haukeland University Hospital; Bergen Norway
| | - René P. Zahedi
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V; Dortmund Germany
| | - Lennart Martens
- Department of Medical Protein Research; VIB; Ghent Belgium
- Department of Biochemistry; Ghent University; Ghent Belgium
| | - Harald Barsnes
- Proteomics Unit; Department of Biomedicine; University of Bergen; Norway
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42
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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] [What about the content of this article? (0)] [Affiliation(s)] [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.
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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
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Muth T, Weilnböck L, Rapp E, Huber CG, Martens L, Vaudel M, Barsnes H. DeNovoGUI: an open source graphical user interface for de novo sequencing of tandem mass spectra. J Proteome Res 2014; 13:1143-6. [PMID: 24295440 PMCID: PMC3923451 DOI: 10.1021/pr4008078] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
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De novo sequencing is a popular technique in proteomics
for identifying peptides from tandem mass spectra without having to
rely on a protein sequence database. Despite the strong potential
of de novo sequencing algorithms, their adoption
threshold remains quite high. We here present a user-friendly and
lightweight graphical user interface called DeNovoGUI for running
parallelized versions of the freely available de novo sequencing software PepNovo+, greatly simplifying the use of de novo sequencing in proteomics. Our platform-independent
software is freely available under the permissible Apache2 open source
license. Source code, binaries, and additional documentation are available
at http://denovogui.googlecode.com.
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Affiliation(s)
- Thilo Muth
- Max Planck Institute for Dynamics of Complex Technical Systems , Sandtorstraße 1, 39106 Magdeburg, Germany
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Verheggen K, Barsnes H, Martens L. Distributed computing and data storage in proteomics: many hands make light work, and a stronger memory. Proteomics 2013; 14:367-77. [PMID: 24285552 DOI: 10.1002/pmic.201300288] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Revised: 09/09/2013] [Accepted: 09/23/2013] [Indexed: 12/25/2022]
Abstract
Modern day proteomics generates ever more complex data, causing the requirements on the storage and processing of such data to outgrow the capacity of most desktop computers. To cope with the increased computational demands, distributed architectures have gained substantial popularity in the recent years. In this review, we provide an overview of the current techniques for distributed computing, along with examples of how the techniques are currently being employed in the field of proteomics. We thus underline the benefits of distributed computing in proteomics, while also pointing out the potential issues and pitfalls involved.
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Affiliation(s)
- Kenneth Verheggen
- Department of Medical Protein Research, VIB, Ghent, Belgium; Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
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Hulstaert N, Reisinger F, Rameseder J, Barsnes H, Vizcaíno JA, Martens L. Pride-asap: automatic fragment ion annotation of identified PRIDE spectra. J Proteomics 2013; 95:89-92. [PMID: 23603108 PMCID: PMC4085470 DOI: 10.1016/j.jprot.2013.04.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2012] [Revised: 03/27/2013] [Accepted: 04/09/2013] [Indexed: 11/13/2022]
Abstract
We present an open source software application and library written in Java that provides a uniform annotation of identified spectra stored in the PRIDE database. Pride-asap can be ran in a command line mode for automated processing of multiple PRIDE experiments, but also has a graphical user interface that allows end users to annotate the spectra in PRIDE experiments and to inspect the results in detail. Pride-asap binaries, source code and additional information can be downloaded from http://pride-asa-pipeline.googlecode.com.This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics. We have built an automatic spectrum annotation pipeline for PRIDE. The tool provides both a GUI and a command-line. The provided annotations are robust and consistent. The tool can be applied easily to thousands of PRIDE experiments. Results are available in the GUI, and as text files for downstream analysis.
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Affiliation(s)
- Niels Hulstaert
- Department of Medical Protein Research, VIB, Ghent, Belgium; Department of Biochemistry, Ghent University, Ghent, Belgium
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Côté RG, Griss J, Dianes JA, Wang R, Wright JC, van den Toorn HWP, van Breukelen B, Heck AJR, Hulstaert N, Martens L, Reisinger F, Csordas A, Ovelleiro D, Perez-Rivevol Y, Barsnes H, Hermjakob H, Vizcaíno JA. The PRoteomics IDEntification (PRIDE) Converter 2 framework: an improved suite of tools to facilitate data submission to the PRIDE database and the ProteomeXchange consortium. Mol Cell Proteomics 2012; 11:1682-9. [PMID: 22949509 PMCID: PMC3518121 DOI: 10.1074/mcp.o112.021543] [Citation(s) in RCA: 100] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The original PRIDE Converter tool greatly simplified the process of submitting mass spectrometry (MS)-based proteomics data to the PRIDE database. However, after much user feedback, it was noted that the tool had some limitations and could not handle several user requirements that were now becoming commonplace. This prompted us to design and implement a whole new suite of tools that would build on the successes of the original PRIDE Converter and allow users to generate submission-ready, well-annotated PRIDE XML files. The PRIDE Converter 2 tool suite allows users to convert search result files into PRIDE XML (the format needed for performing submissions to the PRIDE database), generate mzTab skeleton files that can be used as a basis to submit quantitative and gel-based MS data, and post-process PRIDE XML files by filtering out contaminants and empty spectra, or by merging several PRIDE XML files together. All the tools have both a graphical user interface that provides a dialog-based, user-friendly way to convert and prepare files for submission, as well as a command-line interface that can be used to integrate the tools into existing or novel pipelines, for batch processing and power users. The PRIDE Converter 2 tool suite will thus become a cornerstone in the submission process to PRIDE and, by extension, to the ProteomeXchange consortium of MS-proteomics data repositories.
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Affiliation(s)
- Richard G Côté
- Proteomics Services Team, EMBL Outstation, European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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Aye TT, Low TY, Bjørlykke Y, Barsnes H, Heck AJR, Berven FS. Use of stable isotope dimethyl labeling coupled to selected reaction monitoring to enhance throughput by multiplexing relative quantitation of targeted proteins. Anal Chem 2012; 84:4999-5006. [PMID: 22548487 DOI: 10.1021/ac300596r] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
In this manuscript, we present a proof-of-concept study for targeted relative protein quantitation workflow using chemical labeling in the form of dimethylation, coupled with selected reaction monitoring (dimethyl-SRM). We first demonstrate close to complete isotope incorporation for all peptides tested. The accuracy, reproducibility, and linear dynamic range of quantitation are further assessed based on known ratios of nonhuman standard proteins spiked into human cerebrospinal fluid (CSF) as a model complex matrix. Quantitation reproducibility below 20% (CV < 20%) was obtained for analyte concentrations present at a dynamic range of 4 orders of magnitude lower than that of the background proteins. An error of less than 15% was observed when measuring the abundance of 44 out of 45 major human plasma proteins. Dimethyl-SRM was further examined by comparing the relative quantitation of eight proteins in human CSF with the relative quantitation obtained using synthetic heavy peptides coupled to stable isotope dilution-SRM (SID-SRM). Comparison between the two methods reveals that the correlation between dimethyl-SRM and SID-SRM is within 0.3-33% variation, demonstrating the accuracy of relative quantitation using dimethyl-SRM. Dimethyl labeling coupled with SRM provides a fast, convenient, and cost-effective alternative for relative quantitation of a large number of candidate proteins/peptides.
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Affiliation(s)
- Thin Thin Aye
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway.
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Colaert N, Barsnes H, Vaudel M, Helsens K, Timmerman E, Sickmann A, Gevaert K, Martens L. thermo-msf-parser: An Open Source Java Library to Parse and Visualize Thermo Proteome Discoverer msf Files. J Proteome Res 2011; 10:3840-3. [DOI: 10.1021/pr2005154] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Niklaas Colaert
- Department of Medical Protein Research, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit, Uni Computing, University of Bergen, Bergen, Norway
| | - Marc Vaudel
- Leibniz-Institut für Analytische Wissenschaften - ISAS–e.V., Dortmund, Germany
| | - Kenny Helsens
- Department of Medical Protein Research, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Evy Timmerman
- Department of Medical Protein Research, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften - ISAS–e.V., Dortmund, Germany
- Medizinisches Proteom-Center (MPC), Ruhr - Universität, Bochum, Germany
| | - Kris Gevaert
- Department of Medical Protein Research, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Lennart Martens
- Department of Medical Protein Research, VIB, Ghent, Belgium
- Department of Biochemistry, Ghent University, Ghent, Belgium
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Barsnes H, Vaudel M, Colaert N, Helsens K, Sickmann A, Berven FS, Martens L. compomics-utilities: an open-source Java library for computational proteomics. BMC Bioinformatics 2011; 12:70. [PMID: 21385435 PMCID: PMC3060842 DOI: 10.1186/1471-2105-12-70] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2010] [Accepted: 03/08/2011] [Indexed: 01/10/2023] Open
Abstract
Background The growing interest in the field of proteomics has increased the demand for software tools and applications that process and analyze the resulting data. And even though the purpose of these tools can vary significantly, they usually share a basic set of features, including the handling of protein and peptide sequences, the visualization of (and interaction with) spectra and chromatograms, and the parsing of results from various proteomics search engines. Developers typically spend considerable time and effort implementing these support structures, which detracts from working on the novel aspects of their tool. Results In order to simplify the development of proteomics tools, we have implemented an open-source support library for computational proteomics, called compomics-utilities. The library contains a broad set of features required for reading, parsing, and analyzing proteomics data. compomics-utilities is already used by a long list of existing software, ensuring library stability and continued support and development. Conclusions As a user-friendly, well-documented and open-source library, compomics-utilities greatly simplifies the implementation of the basic features needed in most proteomics tools. Implemented in 100% Java, compomics-utilities is fully portable across platforms and architectures. Our library thus allows the developers to focus on the novel aspects of their tools, rather than on the basic functions, which can contribute substantially to faster development, and better tools for proteomics.
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Affiliation(s)
- Harald Barsnes
- Proteomics Unit, Department of Biomedicine, University of Bergen, Norway
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Barsnes H, Eidhammer I, Martens L. A global analysis of peptide fragmentation variability. Proteomics 2011; 11:1181-8. [PMID: 21328539 DOI: 10.1002/pmic.201000640] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2010] [Revised: 11/25/2010] [Accepted: 11/29/2010] [Indexed: 11/08/2022]
Abstract
Understanding the fragmentation process in MS/MS experiments is vital when trying to validate the results of such experiments, and one way of improving our understanding is to analyze existing data. We here present our findings from an analysis of a large and diverse data set of MS/MS-based peptide identifications, in which each peptide has been identified from multiple spectra, recorded on two commonly used types of electrospray instruments. By analyzing these data we were able to study fragmentation variability on three levels: (i) variation in detection rates and intensities for fragment ions from the same peptide sequence measured multiple times on a single instrument; (ii) consistency of rank-based fragmentation patterns; and (iii) a set of general observations on fragment ion occurrence in MS/MS experiments, regardless of sequence. Our results confirm that substantial variation can be found at all levels, even when high-quality identifications are used and the experimental conditions as well as the peptide sequences are kept constant. Finally, we discuss the observed variability in light of ongoing efforts to create spectral libraries and predictive software for target selection in targeted proteomics.
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Affiliation(s)
- Harald Barsnes
- Department of Informatics, University of Bergen, Bergen, Norway
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