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Two (or more) categories in the High-Throughput GoMiner (HTGM) clustered image map (CIM) may be redundant (ie, contain nearly identical set of genes). RedundancyMiner (RM) helps to resolve that problem by creating a reduced-redundancy CIM and a separate META CIM showing the pattern of redundancies. Here is a link to an article describing RM more fully. The most recent versions of the RedundancyMiner program package and user's manual can be downloaded here.
The database provided by the GO Consortium now includes HGNC gene symbols. What that means internally is that the HGNC gene symbols are in the "symbol" field of the "gene_product" table. This change means that users will no longer need to use our 'Enhanced Names' option, so we have removed it from the 'Lookup Settings'. Anyone who has written scripts involving human HGNC gene symbols will need to modify them. Also, those building their own database no longer need to run the enhancement script. The downloadable script for creating your own local MySQL database has been changed to reflect this.
We have added a feature to automatically generate Clustered Image Maps (CIMs) from HTGM. In the past, we have created a file that would be suitable creating CIM's using these instructions. If you select CIM generation from the HTGM menu, we will generate these images for you if there is sufficient data.
In HTGM we have added a parameter to filter large categories from the CIM's. The parameter is available in both the web and the command-line interface, and it only affects the CIM's -- no other reports or calculations. This feature is useful for generating more compact CIM's. This threshold is compared against the total genes in each category. The number of changed genes is not considered.
In both GUI GoMiner and HTGM we have added a different parameter to filter small categories from the reports and calculations. Categories that are smaller than this threshold will not have p-values, enrichment ratios or FDR's calcualted, although the categories will still be included in most reports. In the GoMiner GUI interface, these categories will be greyed out. This threshold is also used to filter smaller randomized categories when determining the FDR. This feature is useful for minimizing the effect of small categories. This threshold is also compared against the total genes in each category. The number of changed genes is not considered.
A new Startup Wizard is displayed as soon as the GUI GoMiner application is started. This panel provides a consolidated view of the options and their default values available to users when running GoMiner. The user may change any of the default values and may browse to select the total and changed files. When all options are as the user wishes, they may press 'process' to run the application and display the results. At this point the panel will close. The Startup Wizard is available from the file menu for future processing. If the user does not wish to use the wizard, they may simply 'EXIT' from the panel. All previous methods for setting options are still available.
GUI and Command-line version of GoMiner now has FDR feature. On the GUI version FDRs are calculated automatically as soon as the total and changed files are loaded. User could invoke FDR calculator from FDR menu. This menu provides an option to customize parameters like Number Of Random and Root. From Command-Line version user could export FDR summary report (fdrse) by selecting appropriate command-line parameters.
We have updated the SVG that is generated from GoMiner so that it will work correctly when viewed in Firefox, which supports SVG natively. The Firefox SVG implementation is not yet complete, most notably, there is not yet a pan and zoom user interface.
Non-microarray users (eg. proteomics) may not have a natural total-genes file. There is now an Auto-generate function to fill this need by computing an artificial total-genes list. See FAQ for details.
Select a GO category of interest and click to get the link to the corresponding Reactome reaction map.
MatchMiner now has two new features that will be of interest to GoMiner users: (1) a second column containing overexpression (1 or +1) and underexpression indicators (-1) can be carried along 'silently' in the matching process; and (2) there is a new output format option that exactly matches the input format required by GoMiner.
A recently-published tutorial by Jane Lomax, GO Curation Coordinator at the GO Consortium features a screen shot of GoMiner displaying a tree view of GO terms with enrichment for a set of human genes.
Review article 'The Gene Ontology (GO) project in 2006' written by the GO Consortium mentions the mutual involvement of GO and GoMiner in the NCI cancer biomedical informatics grid (caBIG) initiative.
To help molecular biologists see how GoMiner might be useful for their research, we have provided references to relevant publications and reviews.
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. An article on this tool is published in BMC Bioinformatics, 2005, 6:168 by Zeeberg and Qin, et al.
GoMiner has a new feature that allows analysis of splice variants present in your microarray or proteomics data. An example is given in a recent publication in Nature Genetics. Full documentation is given in FAQ.
VennMaster and GoMiner now work together seemlessly. You no longer have the overhead of generating GoMiner export files to see the VennMaster Diagrams. Full details are given in FAQ.