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Featured article - OmicsOne: associate omics data with phenotypes in one-click

OmicsOne Featured Article

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.


Archival content

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.

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.

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Editor's profile

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).

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