GEO ~ The NCBI Gene Expression Omnibus
Deep Sequencing Core Lab users may be interested in using GEO, the NCBI Gene Expression Omnibus. Click on the above GEO graphic to visit the NIH main page. GEO serves as a repository for data from gene expression experiments which is freely available to all members of the scientific community. Researchers performing experiments which generate large amounts of data (e.g. deep sequencing and microarrays) may deposit their datasets to GEO (even if unpublished). Each dataset is identified by a unique ID number which can be cited in publications; many journals require a GEO ID number now when publishing data generated by high-throughput methods.
GEO accepts sequence data generated by next-generation sequencing methodology (Solexa, now Illumina). The following three components should be zipped or tarred together and transferred directly to GEO by selecting the 'GEOarchive' option on the direct deposit page ( Click Here for Deposit Page ).
Component 1: Metadata spreadsheet
Descriptive information and protocols for the overall experiment and individual samples.
Component 2: The processed data files
Plain text, tab-delimited table that contains filtered, unique sequence reads,
detection counts and sequence mapping information
Component 3: The raw data files
_seq.txt
_prb.txt
_sig2.txt
_qhg.txt
For detailed instruction, please refer to
https://www.ncbi.nlm.nih.gov/geo/info/seq.html
or email to geo@ncbi.nlm.nih.gov
Some useful references
- Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R. NCBI GEO: mining tens of millions of expression profiles -- database and tools update
Nucleic Acids Res. 2007 Jan;35(Database issue):D760-5. Epub 2006 Nov 11 - Edgar R, Domrachev M, Lash AE.
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Nucleic Acids Res. 2002 Jan 1;30(1):207-10
The GEO projects page
https://www.ncbi.nlm.nih.gov/geo/summary/?type=taxfull
Please send your comments, suggestions, and user notes to share with
other core lab users to DeepSequencingCoreLabs@umassmed.edu