Gang Peng1,2, Rashaun Wilson3, Yishuo Tang2, TuKiet T Lam4,5, Angus C Nairn3, Kenneth Williams4, Hongyu Zhao1,2. 1. Department of Biostatistics, School of Medicine, Yale University, New Haven, CT, USA. 2. Department of Genetics, School of Medicine, Yale University, New Haven, CT, USA. 3. Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, USA. 4. Department of Molecular Biophysics and Biochemistry, School of Medicine, Yale University, New Haven, CT, USA. 5. MS & Proteomics Resource, W.M. Keck Biotechnology Resource Laboratory, School of Medicine, Yale University, New Haven, CT, USA.
Abstract
SUMMARY: Large-scale, quantitative proteomics data are being generated at ever increasing rates by high-throughput, mass spectrometry technologies. However, due to the complexity of these large datasets as well as the increasing numbers of post-translational modifications (PTMs) that are being identified, developing effective methods for proteomic visualization has been challenging. ProteomicsBrowser was designed to meet this need for comprehensive data visualization. Using peptide information files exported from mass spectrometry search engines or quantitative tools as input, the peptide sequences are aligned to an internal protein database such as UniProtKB. Each identified peptide ion including those with PTMs is then visualized along the parent protein in the Browser. A unique property of ProteomicsBrowser is the ability to combine overlapping peptides in different ways to focus analysis of sequence coverage, charge state or PTMs. ProteomicsBrowser includes other useful functions, such as a data filtering tool and basic statistical analyses to qualify quantitative data. AVAILABILITY AND IMPLEMENTATION: ProteomicsBrowser is implemented in Java8 and is available at https://medicine.yale.edu/keck/nida/proteomicsbrowser.aspx and https://github.com/peng-gang/ProteomicsBrowser. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: Large-scale, quantitative proteomics data are being generated at ever increasing rates by high-throughput, mass spectrometry technologies. However, due to the complexity of these large datasets as well as the increasing numbers of post-translational modifications (PTMs) that are being identified, developing effective methods for proteomic visualization has been challenging. ProteomicsBrowser was designed to meet this need for comprehensive data visualization. Using peptide information files exported from mass spectrometry search engines or quantitative tools as input, the peptide sequences are aligned to an internal protein database such as UniProtKB. Each identified peptide ion including those with PTMs is then visualized along the parent protein in the Browser. A unique property of ProteomicsBrowser is the ability to combine overlapping peptides in different ways to focus analysis of sequence coverage, charge state or PTMs. ProteomicsBrowser includes other useful functions, such as a data filtering tool and basic statistical analyses to qualify quantitative data. AVAILABILITY AND IMPLEMENTATION: ProteomicsBrowser is implemented in Java8 and is available at https://medicine.yale.edu/keck/nida/proteomicsbrowser.aspx and https://github.com/peng-gang/ProteomicsBrowser. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Georgios Efstathiou; Andreas N Antonakis; Georgios A Pavlopoulos; Theodosios Theodosiou; Peter Divanach; David C Trudgian; Benjamin Thomas; Nikolas Papanikolaou; Michalis Aivaliotis; Oreste Acuto; Ioannis Iliopoulos Journal: Nucleic Acids Res Date: 2017-07-03 Impact factor: 16.971
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