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| SpliceCenter |
a suite of very user-friendly tools designed for use by every bench biologist who needs to check for the impact of gene splice
variation on common molecular biology technologies including RT-PCR, RNAi, expression microarrays, and peptide-based assays.
(BMC Bioinformatics, in press) |
| CIMminer |
Generates color-coded Clustered Image Maps (CIMs) (“heat maps”) to represent high-dimensional data sets such as
gene expression profiles. We introduced CIMs in the mid-1990’s for data on drug activity, target expression, gene expression,
and proteomic profiles. Clustering of the axes brings like together with like to create patterns of color.
(Weinstein, et al., Science 1997; 275:343-349) |
| LeFEminer |
Aids in the interpretation of gene microarray data. LeFEminer uses independently generated gene categories defined by GO, KEGG or
other analogous resource. LeFEminer creates non-linear multivariate random forest models to determine which gene categories are most strongly
associated with the experiment's high-level phenotypic data. Support for LeFEminer's intensive computational requirements is provided by the
NIH's Advanced Biomedical Computing Facility (ABCC).
(Eichler GS, et al., Genome Biol. 2007 Sep 10;8(9):R187) |
| AffyProbeMiner |
AffyProbeMiner is to re-define chip definition files (CDFs) for Affymetrix chips taking into account the most recent genomic sequence
information. It re-groups probes in Affymetrix chips into probe sets according to a list of non-redundant verified complete coding
sequences available in GenBank and RefSeq. Pre-computed CDFs for several chips are available for download.
(Liu HF, et al., Bioinformatics. 2007 Sep 15;23(18):2385-90.) |
| SpliceMiner |
Provides an intuitive non-redundant display of a gene's splice variants and may be searched by gene symbol,
chromosomal position, or probe sequence. SpliceMiner is particularly useful in determining which splice variants are
targeted (or missed) by a microarray probe, PCR primer, or siRNA. A high-throughput interface is available for batch
processing of large numbers of queries. (Kahn AB, et al., BMC Bioinformatics. 2007 Mar 5;8(1):75) |
| CellMiner |
A database and query tools for molecular profile information on the NCI 60 human cancer cell lines
and the DU145/RC0.1 prostate cancer cell line pair. |
| AbMiner |
A relational database of information on antibodies that we have screened specificity against
the NCI-60 cancer cell lines. The database includes results of screenings by western blot, practical information for purchase,
identifiers such as UniGene cluster and gene name for each antibody, and out-links to major public bioinformatics resources.
(Major, et al., BMC Bioinformatics 2006, 7:192) |
| MatchMiner |
Translates among gene identifier types for lists of hundreds or thousands of genes. Included: GenBank
accession numbers, IMAGE clone IDs, common gene names, HUGO names, gene symbols, UniGene clusters, FISH-mapped BAC clones,
Affymetrix identifiers, and chromosome locations. MatchMiner can also find the intersection of two lists of genes specified by
different identifiers. (Bussey, et al., Genome Biology 2003; 4:R27) |
| GoMiner |
Addresses the question, “Now that I’ve done the gene expression
experiment and identified a set of ‘interesting’ genes, what do those genes mean
biologically?” GoMiner batch-processes and organizes lists of thousands or
tens of thousands of genes and provides two fluent, robust visualizations of
the genes in the framework of the Gene Ontology hierarchy.
(Zeeberg, et al., Genome Biology 2003; 4:R28) |
| High-Throughput GoMiner |
High-Throughput GoMiner has the capabilities of GoMiner and a number of others. It automates the analysis of
multiple microarrays and integrates results across all of the microarrays, and will be useful in a wide range of applications,
including the study of time-courses, evaluation of multiple drug treatments, comparison of multiple gene knock-outs or
knock-downs, and screening of large numbers of chemical derivatives generated from a promising lead compound.
(Zeeberg and Qin, et al., BMC Bioinformatics. 2005 Jul 5;6(1):168.) |
| SmudgeMiner |
Highlights regional biases and other artifacts on Affymetrix and other microarrays to enable quality assessment.
The analysis supplements the standard Affymetrix quality measures and often identifies chips that are confusing to the
analysis but pass the standard qulity control. (Reimers, et al., BMC Bioinformatics. 2005 Jul 1;6(1):166.) |
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