Category Cross-Omics>Knowledge Bases/Databases/Tools

Abstract GoMiner™ is a tool for biological interpretation of 'omic' data - including data from gene expression microarrays.

Omic experiments often generate lists of dozens or hundreds of genes that differ in expression between samples, raising the question -- What does it all mean biologically?

To answer this question, GoMiner leverages the Gene Ontology (GO) to identify the biological processes, functions and components represented in these lists. Instead of analyzing microarray results with a gene-by-gene approach, GoMiner classifies the genes into biologically coherent categories and assesses these categories.

The insights gained through GoMiner can generate hypotheses to guide additional research.

GoMiner displays the genes within the framework of the Gene Ontology (GO) hierarchy in two (2) ways:

1) In the form of a tree, similar to that in AmiGO.

Note: AmiGO is the official tool for searching and browsing the Gene Ontology database, which consists of a controlled vocabulary of terms covering biological concepts, and a large number of genes or gene products whose attributes have been annotated using GO terms. In addition to a text representation of the ontology structure AmiGO provides a graphical view of the ontology.

2) In the form of a 'Directed Acyclic Graph' (DAG) - (a DAG is a directed graph with No directed cycles; that is, for any vertex v, there is No nonempty directed path that starts and ends on v).

GoMiner also provides:

a) Quantitative and statistical analysis.

b) Seamless integration with important public databases such as LocusLink; PubMed; GeneCards (see G6G Abstract Number 20170); NCBI Entrez Structure; BioCarta and KEGG pathway maps as implemented by the NCI Cancer Genome Anatomy Project (CGAP); etc.

These external databases provide GoMiner with a rich set of resources for bioinformatic integration.

For example, the links with CGAP and LocusLink provide interaction with pathway maps, chromosome visualizations, a database of single nucleotide polymorphism (SNP), and the Mammalian Gene Collection (MGC).

High-Throughput GoMiner (HTGM) -- The manufacturers also provide an additional computational resource (HTGM) that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations.

HTGM is implemented with both a command line interface and a web interface.

High-Throughput GoMiner is Not a replacement for GoMiner, but a companion to it. In fact, the original GoMiner is one component of High- Throughput GoMiner.

HTGM additional capabilities include:

1) It efficiently performs the computationally-intensive task of automated batch processing of an arbitrary number of microarrays.

2) Produces a human-or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories.

3) Integrates the multiple microarray results by providing organized, global clustered image map (CIM) visualizations of the relationships of significant GO categories.

4) Provides a fast form of 'false discovery rate' multiple comparisons calculation.

5) Provides annotations and visualizations for relating transcription factor binding sites to genes and GO categories.

New/Recent features include:

Automatically generate Clustered Image Maps -- added a feature to automatically generate Clustered Image Maps (CIM’s) from HTGM. If you select CIM generation from the HTGM menu, the system will generate these images for you if there is sufficient data.

New options for adjusting reports and calculations -- in HTGM the manufacturer has 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 the manufacturer has 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 calculated, although the categories will still be included in most reports.

In the GoMiner GUI interface, these categories will be grayed 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.

Derby Database Support -- added support for a built-in database called Derby. With Derby, users can get the performance advantages of using a local database, without the hassle of installing a separate database tool. Users just download and unpack the GoMiner/Derby download package.

Startup Wizard -- 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.

False Discovery Rate (FDR) in GUI -- GUI and Command-line versions of GoMiner now has the FDR feature. On the GUI version FDRs are calculated automatically as soon as the total and changed files are loaded. User can invoke the FDR calculator from the FDR menu.

SVG Support for Firefox -- The manufacturer has updated the Scalable Vector Graphics (SVG) that is generated from GoMiner so that it will work correctly when viewed in Firefox, which supports SVG natively.

Improved Proteomics Support -- Non-microarray users (e.g. 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.

GO Category Integration with Reactome -- Select a GO category of interest and click to get the link to the corresponding Reactome reaction map.

MatchMiner Supports GoMiner File Formats -- MatchMiner (an additional product) now has two (2) new features that will be of interest to GoMiner users: (1) a second column containing ‘over expression’ (1 or +1) and ‘under expression’ 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.

System Requirements

Contact manufacturer.


GoMiner was originally developed jointly by the Genomics & Bioinformatics Group (GBG) of LMP (Laboratory of Molecular Pharmacology), 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.

Manufacturer Web Site GoMiner

Price Contact manufacturer.

G6G Abstract Number 20261

G6G Manufacturer Number 101841