MetPA: a web-based metabolomics tool for pathway analysis and visualization
Clicks: 3
ID: 307549
2010
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
0.6
/100
3 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #315 of 829 articles by views in BMC Bioinformatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 829 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Summary: MetPA (Metabolomics Pathway Analysis) is a user-friendly, web-based tool dedicated to the analysis and visualization of metabolomic data within the biological context of metabolic pathways. MetPA combines several advanced pathway enrichment analysis procedures along with the analysis of pathway topological characteristics to help identify the most relevant metabolic pathways involved in a given metabolomic study. The results are presented in a Google-map style network visualization system that supports intuitive and interactive data exploration through point-and-click, dragging and lossless zooming. Additional features include a comprehensive compound library for metabolite name conversion, automatic generation of analysis report, as well as the implementation of various univariate statistical procedures that can be accessed when users click on any metabolite node on a pathway map. MetPA currently enables analysis and visualization of 874 metabolic pathways, covering 11 common model organisms. Availability: Freely available at http://metpa.metabolomics.ca Contact: david.wishart@ualberta.ca
| Reference Key |
openalex_W2167540852
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Jianguo Xia, David S. Wishart |
| Journal | BMC Bioinformatics |
| Year | 2010 |
| DOI |
10.1093/bioinformatics/btq418
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.