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GoMiner and High-Throughput GoMiner have similar processing capabilities, but which version is best for your analysis depends on your situation. The following guidelines will help you chose:
The original GoMiner interface, which we sometimes call Classic or GUI GoMiner, is well-suited if you:
GoMiner can also be used to handle large data sets; To experience a reasonable response time with large data sets, we strongly recommend that you set up a local copy of the GO database.
The web interface of High-Throughput GoMiner, is well suited if you:
High-Throughput GoMiner is particularly useful if you want to process large data sets, but do not want to set up your own local database. High-Throughput GoMiner automates many of the repetitive processing that you would need to do manually in GUI GoMiner, such as saving the various export files.
GoMiner and Hight-Throughput GoMiner both have command-line interfaces as well. These can be useful if you would like to automate processing using scripts. In either case, we would recommend that you set up a local copy of the GO database if you plan to do extensive automated processing.
If you have not already done so, check the system requirements, and install GoMiner
We have provided a Quick Start to guide you through some examples using GoMiner. The following sample input files are referenced in the guide, and can be used on their own to explore the tool.
We have also provided an example that makes use of GoMiner's synonym lookup feature. This is a yeast data set that is annotated using the ORF notation.
Examples formatted for use in the High-Throughput GoMiner are also available.
Some files in PDF format and require Adobe Acrobat Reader to be viewed. Click here to download.
GoMiner™ is a development of the Genomics and Bioinformatics Group, Developmental Therapeutics Branch (DTB), Developmental Therapeutics Program (DTP), Center for Cancer Research (CCR), National Cancer Institute (NCI). We would like to hear from you. You can reach the team via email.
GoMiner was originally developed jointly by the Genomics and Bioinformatics Group (GBG) of LMP, NCI, NIH and the Medical Informatics and Bioimaging group of BME, Georgia Tech/Emory University. It is now maintained and under continuing development by GBG.
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