The Miner Suite of Bioinformatic Applications

These applications are freely available for public use. Our characterization and analysis of the NCI-60 cancer cell lines, the DNA, RNA, protein, epigenetic and pharmacological levels is accessible through CellMiner, with the expended version including additional cell line sets available through CellMinerCDB.

CellMinerCDB data visualization


CellMiner Cross Database (CDB) is the first web application to allow translational researchers to conduct analyses across all major cancer cell line pharmacogenomic data sources from NCI-DTP NCI-60, Sanger GDSC, and Broad CCLE/CTRP. It provides matched molecular and drug activity profiling data. This data may be used to 1) assess molecular and drug data reproducibility, 2) determine repositioning opportunities for FDA-approved compounds, 3) identify potential drug response and gene regulatory determinants, and 4) identify and validate novel genes associated with phenotypic processes. This data is an important precision medicine resource. (Augustin Luna, Fathi Elloumi, Sudhir Varma, Yanghsin Wang, Vinodh N. Rajapakse, Mirit I. Aladjem, Jacques Robert, Chris Sander, Yves Pommier and William C. Reinhold, Nucleic Acids Research, January 2021)

CellMinerCDB NCATS data visualization

CellMinerCDB: National Center for Advancing Translational Sciences (NCATS)

A powerful tool for precision medicine: CellMinerCDB: NCATS exposes relationships between cancer cells' molecular makeup and their response to potential therapies, using data on thousands of compounds screened at the National Center for Advancing Translational Sciences. ( William C. Reinhold, Kelli Wilson, Fathi Elloumi, Katie R. Bradwell, Michele Ceribelli, Sudhir Varma, Yanghsin Wang, Damien Duveau, Nikhil Menon, Jane Trepel, Xiaohu Zhang, Carleen Klumpp-Thomas, Samuel Michael, Paul Shinn, Augustin Luna, Craig Thomas, Yves Pommier, AACR Journals, May 2023 )

CellMiner data visualization

CellMiner (NCI-60)

A database and query tool designed for the cancer research community to facilitate integration of the molecular datasets generated by the Genomics and Pharmacology Facility and its collaborators on the NCI-60 ( William C. Reinhold, Margot Sunshine, Hongfang Liu, Sudhir Varma, Kurt W. Kohn, Joel Morris, James Doroshow, and Yves Pommier Cancer Research, July 2012 ).

CIMMiner data visualization


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 Jan 1997; 275:343-349)

AbMiner data visualization


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)

MIMMiner data visualization


A Molecular Interaction Map (MIM) is a diagram convention that is capable of unambiguous representation of networks containing multi-protein complexes, protein modifications, and enzymes that are substrates of other enzymes. This graphical representation makes it possible to view all of the many interactions in which a given molecule may be involved, and it can portray competing interactions, which are common in bioregulatory networks. In order to facilitate linkage to databases, each molecular species is represented only once in a diagram. A formal description of the MIM notation can be found in Kohn et al., Molecular Biology of the cell 17, 1-13 2006. The updated formal specification for software implementation can be found in Luna et al., BMC Bioinformatics 2011, 12:167.

splice center data visualization


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. (Ryan, et al., BMC Bioinformatics. 2008 9:13)