These authors contributed equally to this study.
LiverAtlas: a unique integrated knowledge database for systems-level research of liver and hepatic disease
Article first published online: 21 APR 2013
© 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
Volume 33, Issue 8, pages 1239–1248, September 2013
How to Cite
Liver Int. 2013: 33: 1239–1248
- Issue published online: 11 AUG 2013
- Article first published online: 21 APR 2013
- Accepted manuscript online: 28 MAR 2013 06:46AM EST
- Manuscript Accepted: 10 MAR 2013
- Manuscript Received: 2 AUG 2012
- Chinese National Basic Research Program. Grant Number: 2013CB910800
- National High-Tech Research and Development Program. Grant Numbers: 2012AA020201, 2012AA020409
- National Natural Science Foundation of China. Grant Numbers: 21105121, 21275160
- Beijing Municipal Natural Science Foundation. Grant Number: 5122013
- biomarker discovery;
- liver pathology;
- liver physiology;
A large amount of liver-related physiological and pathological data exist in publicly available biological and bibliographic databases, which are usually far from comprehensive or integrated. Data collection, integration and mining processes pose a great challenge to scientific researchers and clinicians interested in the liver.
To address these problems, we constructed LiverAtlas (http://liveratlas.hupo.org.cn), a comprehensive resource of biomedical knowledge related to the liver and various hepatic diseases by incorporating 53 databases.
In the present version, LiverAtlas covers data on liver-related genomics, transcriptomics, proteomics, metabolomics and hepatic diseases. Additionally, LiverAtlas provides a wealth of manually curated information, relevant literature citations and cross-references to other databases. Importantly, an expert-confirmed Human Liver Disease Ontology, including relevant information for 227 types of hepatic disease, has been constructed and is used to annotate LiverAtlas data. Furthermore, we have demonstrated two examples of applying LiverAtlas data to identify candidate markers for hepatocellular carcinoma (HCC) at the systems level and to develop a systems biology-based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC differential diagnosis.
LiverAtlas is the most comprehensive liver and hepatic disease resource, which helps biologists and clinicians to analyse their data at the systems level and will contribute much to the biomarker discovery and diagnostic performance enhancement for liver diseases.