PMID- 34895137
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240214
IS  - 1542-6416 (Print)
IS  - 1559-0275 (Electronic)
IS  - 1542-6416 (Linking)
VI  - 18
IP  - 1
DP  - 2021 Dec 11
TI  - OmicsOne: associate omics data with phenotypes in one-click.
PG  - 29
LID - 10.1186/s12014-021-09334-w [doi]
LID - 29
AB  - BACKGROUND: 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'. 
      MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
CI  - (c) 2021. The Author(s).
FAU - Zhang, Hui
AU  - Zhang H
AD  - School of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA.
FAU - Ao, Minghui
AU  - Ao M
AD  - School of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA.
FAU - Boja, Arianna
AU  - Boja A
AD  - Mount Hebron High School, Ellicott City, MD, 21042, USA.
FAU - Schnaubelt, Michael
AU  - Schnaubelt M
AD  - School of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA.
FAU - Hu, Yingwei
AU  - Hu Y
AUID- ORCID: 0000-0002-4629-0985
AD  - School of Medicine, Johns Hopkins University, Baltimore, MD, 21287, USA. 
      yhu39@jhmi.edu.
LA  - eng
GR  - U01 CA152813/CA/NCI NIH HHS/United States
GR  - U01CA152813/CA/NCI NIH HHS/United States
GR  - U24CA210985/CA/NCI NIH HHS/United States
PT  - Journal Article
DEP - 20211211
PL  - England
TA  - Clin Proteomics
JT  - Clinical proteomics
JID - 101184586
PMC - PMC8903648
OTO - NOTNLM
OT  - Bioinformatics
OT  - Glycoproteomics
OT  - Ovarian cancer
OT  - Phenotype association
OT  - Proteomics
OT  - Software
COIS- The authors declare no financial or commercial conflict of interest.
EDAT- 2021/12/14 06:00
MHDA- 2021/12/14 06:01
PMCR- 2021/12/11
CRDT- 2021/12/13 11:36
PHST- 2021/10/19 00:00 [received]
PHST- 2021/11/22 00:00 [accepted]
PHST- 2021/12/13 11:36 [entrez]
PHST- 2021/12/14 06:00 [pubmed]
PHST- 2021/12/14 06:01 [medline]
PHST- 2021/12/11 00:00 [pmc-release]
AID - 10.1186/s12014-021-09334-w [pii]
AID - 9334 [pii]
AID - 10.1186/s12014-021-09334-w [doi]
PST - epublish
SO  - Clin Proteomics. 2021 Dec 11;18(1):29. doi: 10.1186/s12014-021-09334-w.