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Nucleic Acids Research 2009 37(Database issue):D880-D884; doi:10.1093/nar/gkn878
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Nucleic Acids Research, 2009, Vol. 37, Database issue D880-D884
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article appears in the following Nucleic Acids Research issue: Database issue [View the issue table of contents]

Articles

CleanEx: new data extraction and merging tools based on MeSH term annotation

Viviane Praz1,* and Philipp Bucher2

1ISREC, Swiss Institute of Bioinformatics, Boveresses 155, Epalinges, VD 1066 and 2ISREC/EPFL, Swiss Institute of Bioinformatics, Epalinges, VD, Switzerland

*To whom correspondence should be addressed. Tel/Fax: 4121 692 5956; Email: viviane.praz{at}unil.ch

Received September 16, 2008. Revised October 16, 2008. Accepted October 17, 2008.

The CleanEx expression database (http://www.cleanex.isb-sib.ch) provides access to public gene expression data via unique gene names as well as via experiments biomedical characteristics. To reach this, a dual annotation of both sequences and experiments has been generated. First, the system links official gene symbols to any kind of sequences used for gene expression measurements (cDNA, Affymetrix, oligonucleotide arrays, SAGE or MPSS tags, Expressed Sequence Tags or other mRNA sequences, etc.). For the biomedical annotation, we re-annotate each experiment from the CleanEx database with the MeSH (Medical Subject Headings) terms, primarily used by NLM (National Library of Medicine) for indexing articles for the MEDLINE/PubMED database. This annotation allows a fast and easy retrieval of expression data with common biological or medical features. The numerical data can then be exported as matrix-like tab-delimited text files. Data can be extracted from either one dataset or from heterogeneous datasets.


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