OmicsOne: associate omics data with phenotypes in one-click.


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. Many individual software tools have been developed for data analysis and visualization. However, it still lacks an efficient way to investigate the phenotypes with 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 '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 implemented in OmicsOne: (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.

We developed an integrated software solution, OmicsOne, for the phenotype association analysis on multi-omics data sets. The application of OmicsOne on the public data set of ovarian cancer data showed that the software could confirm the previous observations consistently and discover new evidence for HNRNPU and a glycopeptide of HYOU1 as potential biomarkers for HGSOC data sets. The performance of OmicsOne was further demonstrated in the Tumor and NAT comparison study on the proteome data set of LSCC.

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.

  • Ao M
  • Boja A
  • Hu Y
  • Schnaubelt M
  • Zhang H
PubMed ID
Appears In
Clin Proteomics, 2021, 18 (1)