We have previously developed GoMiner™, a program that organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. High-Throughput GoMiner is an enhancement of GoMiner which efficiently performs the computationally-challenging task of automated batch processing of an arbitrary number of microarray experiments. High-Throughput GoMiner is implemented with both a command line interface and a web interface.

When should you use High-Throughput GoMiner?
  • Multiple changed-gene files
    • Time course on multiple microarrays (See image on right)
    • Multiple drug derivatives generated from combinatorial chemistry
  • Rapid estimation and adjustment of statistical parameters for effect of testing multiple GO categories
  • Integration of relationship of genes and GO categories in multiple experiments
  • Relationship of transcription factor binding sites and GO categories

High-Throughput GoMiner can also be useful if you want to have the performance benefit of co-locating GoMiner and its companion database, but do not want to set up your own local database.

A description of the relationship of this new version of the tool and the original version is available.

An article about this new version of the tool is available from BMC Bioinformatics (2005, 6:168). Supplementary Materials are also available

Example of a Clustered Image Map of Categories in a Time Series
Example of a Clustered Image Map of Categories in a Time Series

Generated with High-Throughput GoMiner and CIMminer.

Thanks to Eldad Elnekave, Danielle M Hari, and Thomas A Wynn for the data used to generate the image.

GoMiner™ is a development of the Genomics and Pharmacology Facility, Developmental Therapeutics Branch (DTB), Center for Cancer Research (CCR), National Cancer Institute (NCI).

Please email us with any problems, questions or feedback on the tool.

Notice and Disclaimer