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Harald Barsnes's picture
  • E-mailHarald.Barsnes@uib.no
  • Phone+47 55 58 66 65
  • Visitor Address
    Jonas Lies vei 91
  • Postal Address
    Postboks 7804
    5020 Bergen

Harald Barsnes is a group leader at both the Proteomics Unit (PROBE) at the Department of Biomedicine and at the Computational Biology Unit (CBU) at the Department of Informatics.

The main focus of the group is the development of user-friendly open source bioinformatics tools that enable and empower researchers to analyze and share their own data. For more information about the group see www.cbu.uib.no/barsnes.

Barsnes obtained his PhD from the Department of Informatics at the University of Bergen in 2010, with a focus on bioinformatics and proteomics. Since then he worked at the Proteomics Unit at the Department of Biomedicine, first as a senior engineer and later as a Postdoc funded by the Research Council of Norway. He then worked as a researcher at the Department of Clinical Science as part of the Ræder lab, until starting his own research group in 2016 funded by the Bergen Research Foundation and the Research Council of Norway.

The research has resulted in numerous publications and a long list of freely available software, including PeptideShakerSearchGUI and PRIDE Converter.

Barsnes has also been a guest editor in PROTEOMICS and has co-authored a Wiley text book called Computational and Statistical Methods for Protein Quantification by Mass Spectrometry.

In 2015 he received the prestigious Meltzer Award for Excellent Young Researchers at the University of Bergen, and in 2016 he was awarded a recruitment fellowship from the Bergen Research Foundation.

Academic article
  • 2019. ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion. Journal of Proteome Research.
  • 2019. Proteomics Standards Initiative Extended FASTA Format. Journal of Proteome Research. 2686-2692.
  • 2019. PathwayMatcher: proteoform-centric network construction enables fine-granularity multi-omics pathway mapping. GigaScience. 1-13.
  • 2019. Dynamic proteome profiling of human pluripotent stem cell-derived pancreatic progenitors. Stem Cells.
  • 2019. Detecting single amino acids and small peptides by combining isobaric tags and peptidomics. European Journal of Mass Spectrometry.
  • 2018. SearchGUI: a highly adaptable common interface for proteomics search and de novo engines. Journal of Proteome Research. 2552-2555.
  • 2018. Analyzing the Structure of Pathways and Its Influence on the Interpretation of Biomedical Proteomics Data Sets. Journal of Proteome Research. 3801-3809.
  • 2017. The mzIdentML data standard version 1.2, supporting advances in proteome informatics. Molecular & Cellular Proteomics. 1275-1285.
  • 2017. PeptideMapper: Efficient and versatile amino acid sequence and tag mapping. Bioinformatics. 2042-2044.
  • 2017. OLS Client and OLS Dialog: Open source tools to annotate public omics datasets. Proteomics.
  • 2017. In-depth cerebrospinal fluid quantitative proteome and deglycoproteome analysis: presenting a comprehensive picture of pathways and processes affected by multiple sclerosis. Journal of Proteome Research. 179-194.
  • 2017. BioContainers: An open-source and community-driven framework for software standardization. Bioinformatics.
  • 2016. Using Proteomics Bioinformatics Tools and Resources in Proteogenomic Studies. Advances in Experimental Medicine and Biology. 65-75.
  • 2016. Systemic Analysis of Regulated Functional Networks. Methods in molecular biology. 287-310.
  • 2016. Practical considerations for omics experiments in biomedical sciences. Current Pharmaceutical Biotechnology. 105-114.
  • 2016. Pladipus enables universal distributed computing in proteomics bioinformatics. Journal of Proteome Research. 707-712.
  • 2016. Label-free analysis of human cerebrospinal fluid addressing various normalization strategies and revealing protein groups affected by multiple sclerosis. Proteomics. 1154-1165.
  • 2016. CSF-PR 2.0: an interactive literature guide to quantitative cerebrospinal fluid mass spectrometry data from neurodegenerative disorders. Molecular & Cellular Proteomics. 300-309.
  • 2016. A pipeline for differential proteomics in unsequenced species. Journal of Proteome Research. 1963-1970.
  • 2015. Quantitative proteomics suggests decrease in the secretogranin-1 cerebrospinal fluid levels during the disease course of multiple sclerosis. Proteomics. 3361-3369.
  • 2015. JSparklines: Making tabular proteomics data come alive. Proteomics. 1428-1431.
  • 2015. Distributed and interactive visual analysis of omics data. Journal of Proteomics. 78-82.
  • 2014. Shedding light on black boxes in protein identification. Proteomics. 1001-1005.
  • 2014. In-depth characterization of the cerebrospinal fluid (CSF) proteome displayed through the CSF proteome resource (CSF-PR). Molecular & Cellular Proteomics. 3152-3163.
  • 2014. Distributed computing and data storage in proteomics: many hands make light work, and a stronger memory. Proteomics. 367-377.
  • 2014. DeNovoGUI: an open source graphical user interface for de novo sequencing of tandem mass spectra. Journal of Proteome Research. 1143-1146.
  • 2014. Carboxyl-ester lipase maturity-onset diabetes of the young disease protein biomarkers in secretin-stimulated duodenal juice. Journal of Proteome Research. 521-530.
  • 2013. Discovery and initial verification of differentially abundant proteins between multiple sclerosis patients and controls using iTRAQ and SID-SRM. Journal of Proteomics. 312-325.
  • 2012. Use of stable isotope dimethyl labeling coupled to selected reaction monitoring to enhance throughput by multiplexing relative quantitation of targeted proteins. Analytical Chemistry. 4999-5006.
  • 2012. The PRoteomics IDEntification (PRIDE) Converter 2 Framework: An Improved Suite of Tools to Facilitate Data Submission to the PRIDE Database and the ProteomeXchange Consortium. Molecular & Cellular Proteomics. 1682-1689.
  • 2011. thermo-msf-parser: An Open Source Java Library to Parse and Visualize Thermo Proteome Discoverer msf Files. Journal of Proteome Research. 3840-3843.
  • 2011. compomics-utilities: an open-source Java library for computational proteomics. BMC Bioinformatics. 5 pages.
  • 2011. Submitting proteomics data to PRIDE using PRIDE Converter. Methods in molecular biology. 237-253.
  • 2011. SearchGUI: An open-source graphical user interface for simultaneous OMSSA and X!Tandem searches. Proteomics. 996-999.
  • 2011. IsobariQ: Software for Isobaric Quantitative Proteomics using IPTL, iTRAQ, and TMT. Journal of Proteome Research. 913-920.
  • 2011. A global analysis of peptide fragmentation variability. Proteomics. 1181-1188.
  • 2010. ms_lims, a simple yet powerful open source LIMS for mass spectrometry-driven proteomics. Proteomics. 1261-1264.
  • 2010. XTandem Parser: An open-source library to parse and analyse X!Tandem MS/MS search results. Proteomics. 1522-1524.
  • 2010. The Proteomics Identifications database: 2010 update. Nucleic Acids Research. D736-D742.
  • 2010. The Ontology Lookup Service: bigger and better. Nucleic Acids Research. W155-W160.
  • 2010. OLS Dialog: An open-source front end to the Ontology Lookup Service. BMC Bioinformatics.
  • 2010. Fragmentation Analyzer: An open-source tool to analyze MS/MS fragmentation data. Proteomics. 1087-1090.
  • 2009. Proteomics data collection - 4th ProDaC workshop 15 August 2008, Amsterdam, The Netherlands. Proteomics. 218-222.
  • 2009. Proteomics Data Collection - 5th ProDaC Workshop: 4 March 2009, Kolympari, Crete, Greece. Proteomics. 3626-3629.
  • 2009. OMSSA Parser: an open-source library to parse and extract data from OMSSA MS/MS search results. Proteomics. 3772-3774.
  • 2009. Getting a grip on proteomics data - Proteomics Data Collection (ProDaC). Proteomics. 3928-3933.
  • 2008. Protease-dependent fractional mass and peptide properties. European Journal of Mass Spectrometry. 311-317.
  • 2008. Blind search for post-translational modifications and amino acid substitutions using peptide mass fingerprints from two proteases. BMC Research Notes.
  • 2006. MassSorter: a tool for administrating and analyzing data from mass spectrometry experiments on proteins with known amino acid sequences. BMC Bioinformatics. 9 pages.
Reader opinion piece
  • 2009. PRIDE Converter: making proteomics data-sharing easy. Nature Biotechnology. 598-599.
Short communication
  • 2017. An accessible proteogenomics informatics resource for cancer researchers. Cancer Research. e43-e46.
  • 2013. Pride-asap: automatic fragment ion annotation of identified PRIDE spectra. Journal of Proteomics. 89-92.
Academic monograph
  • 2013. Computational and Statistical Methods for Protein Quantification by Mass Spectrometry.
Letter to the editor
  • 2015. PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nature Biotechnology. 22-24.
Doctoral dissertation
  • 2016. Proteome and deglycopeptide analyses of neurologically healthy and multiple sclerosis affected cerebrospinal fluid: Mass spectrometry experiments, literature mining and data sharing .
Academic chapter/article/Conference paper
  • 2019. Essential Features and Use Cases of the Cerebrospinal Fluid Proteome Resource (CSF-PR). 15 pages.
  • 2016. Visualization, Inspection and Interpretation of Shotgun Proteomics Identification Results. 10 pages.
  • 2016. Tandem Mass Spectrum Sequencing: An Alternative to Database Search Engines in Shotgun Proteomics. 10 pages.
  • 2016. Interpretation of Quantitative Shotgun Proteomic Data. 14 pages.
  • 2016. Database Search Engines: Paradigms, Challenges and Solutions. 10 pages.
  • 2016. A Simple Workflow for Large Scale Shotgun Glycoproteomics. 12 pages.
  • 2014. Bioinformatics for Proteomics: Opportunities at the interface between the scientists, their experiments and the community. 10 pages.
  • 2008. MassSorter: Peptide Mass Fingerprinting Data Analysis. 15 pages.
Poster
  • 2011. IsobariQ: Software for isobaric quantitative proteomics using IPTL, iTRAQ and TMT.
Academic literature review
  • 2017. Anatomy and evolution of database search engines—a central component of mass spectrometry based proteomic workflows. 292-306.
  • 2016. Exploring the potential of public proteomics data. 214-225.
  • 2015. Viewing the proteome: How to visualize proteomics data? 1341-1355.
  • 2014. Machine learning applications in proteomics research: How the past can boost the future. 353-366.
  • 2013. Crowdsourcing in proteomics: public resources lead to better experiments. 1129-1137.

More information in national current research information system (CRIStin)

Research groups