ArticleClinical proteomics2021
OmicsOne: associate omics data with phenotypes in one-click.
Article in Clinical proteomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Genetic and epigenetic insights into perioperative neurocognitive disorders: a narrative review.British journal of anaesthesia · 2026Review
- Geometric multidimensional representation of omic signatures.Frontiers in bioinformatics · 2026Article
- DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge.Briefings in bioinformatics · 2024Article
- Neoplastic cell enrichment of tumor tissues using coring and laser microdissection for proteomic and genomic analyses of pancreatic ductal adenocarcinoma.Clinical proteomics · 2022Article
- Proteomic characterization of primary and metastatic prostate cancer reveals reduced proteinase activity in aggressive tumors.Scientific reports · 2021Article
- Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma.Cell reports · 2020Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe 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'. MATERIALS AND
methodsOmicsOne 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.
resultsWe 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.
conclusionsOmicsOne 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.