Evidence map›Paper›PMID 37342652›Full record

ArticleCancer informatics2023

Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud.

Trinh Nguyen, Xiaopeng Bian, David Roberson, Rakesh Khanna, Qingrong Chen, Chunhua Yan, Rowan Beck, Zelia Worman, Daoud Meerzaman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Trinh NguyenThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA.
Xiaopeng BianThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA.
David RobersonSeven Bridges, Charlestown, MA, USA.
Rakesh KhannaThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA.
Qingrong ChenThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA.
Chunhua YanThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA.
Rowan BeckSeven Bridges, Charlestown, MA, USA.
Zelia WormanSeven Bridges, Charlestown, MA, USA.
Daoud MeerzamanThe Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently. Methods: We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow. Results: The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing. Conclusion: Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.

Indexed as

automatic workflowCGCcloudMulti-omicspathway analysisTCGA

Identifiers

PMID37342652
PMCPMC10278438

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.