In 2011, Clinical Proteomics converted from a subscription publication to a fully open access journal. The journal's back content can be viewed on SpringerLink.
The rapid advancements of high throughput “omics” technologies have brought a massive amount of data to process during and after experiments. Multi-omic analysis facilitates a deeper interrogation of a dataset and the discovery of interesting genes, proteins, lipids, glycans, metabolites, or pathways related to the corresponding phenotypes in a study.
However, it still lacks an efficient and simple way to investigate the phenotypes with integrated multiple omics data. Here, we present OmicsOne as an interactive web-based framework for rapid phenotype association analysis of multi-omic data by integrating quality control, statistical analysis, and interactive data visualization on a simple ‘one-click’.
OmicsOne was applied on the previously published proteomic and glycoproteomic data sets of high-grade serous ovarian carcinoma (HGSOC) and the published proteome data set of lung squamous cell carcinoma (LSCC) to confirm its performance. The data was analyzed through six main functional modules: (1) phenotype profiling, (2) data preprocessing and quality control, (3) knowledge annotation, (4) phenotype associated features discovery, (5) correlation and regression model analysis for phenotype association analysis on individual features, and (6) enrichment analysis for phenotype association analysis on interested feature sets.
The application of OmicsOne on the public data set showed that OmicsOne can effectively simplify data analysis and reveal the significant associations between phenotypes and potential biomarkers, including genes, proteins, and glycopeptides, in minutes to assist users to understand aberrant biological processes.
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Aims and scope
Clinical Proteomics encompasses all aspects of translational proteomics. Special emphasis will be placed on the application of proteomic technology to all aspects of clinical research and molecular medicine. The journal is committed to rapid scientific review and timely publication of submitted manuscripts.
Dr Daniel W Chan is currently Professor of Pathology, Oncology, Urology, and Radiology at Johns Hopkins Medical Institutions, and Director of the Clinical Chemistry Division, Co-Director at the Pathology Core Laboratory, and Director at the Center for Biomarker Discovery and Translation at the Johns Hopkins University School of Medicine. He has worked extensively on the development and application of proteomic and immunologic techniques in the understanding of cancer. As the author of five books and over 300 articles, Dr Chan has become established as a leading expert in clinical proteomics and cancer research. Dr Chan is an active member of US Human Proteome Organization (USHUPO), American Association for Cancer Research (AACR) and American Association for Clinical Chemistry (AACC).
Guest edited by Vera Ignjavtovic, Allen Everett and Hanno Steen
The Paediatric Proteomics (PediOme) is an initiative of HUPO that aims to advance the use of proteomic techniques to solve major issues in Paediatric medicine.
Proteomics in India
Guest edited by Harsha Gowda and Akhilesh Pandey
This collections features the latest advances in Proteomics research from India.
Glycoproteomics and glycomics
Guest edited by Punit Shah and Hui Zhang
This thematic series publishes both solicited and unsolicited content on the topic of protein glycosylation; one of the most common protein modifications in both normal biological processes and diseases. This series focuses on glycoproteomic or glycomic methods and their clinical applications.
Clinical Proteomics is affiliated with the Human Proteome Organization (HUPO). HUPO is an international scientific organization representing and promoting proteomics through international cooperation and collaborations by fostering the development of new technologies, techniques and training.
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Annual Journal Metrics
38 days to first decision for all manuscripts
102 days to first decision for reviewed manuscripts only
192 days from submission to acceptance
16 days from acceptance to publication
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