Datasets and Databases

The Broad Institute/Dana Farber Cancer Institute
Principal Investigator: Todd Golub, M.D.

Cancer Program Datasets Portal
PI: Todd Golub, M.D.
Contact: Michael Reich (michaelr@broadinstitute.org)
Web Link: http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi
     Collection of over 100 datasets from Broad Institute Cancer Program publications, providing gene expression, and SNP microarray assays and RNAi screens of a number of cancer types including breast, colorectal, hepatocellular, leukemia, lung, lymphoma, medulloblastoma, melanoma, nasopharyngeal, neuroblastoma, prostate, and sarcoma.

Molecular Signature Database (MSigDB)
PI: Jill P. Mesirov, Ph.D.
Contact: gsea@broadinstitute.org
Web Link: http://www.broadinstitute.org/gsea/msigdb/

     The Molecular Signatures Database (MSigDB) is a collection of gene sets for use with GSEA software. From this web site, you can search for gene sets, browse gene sets, view pathway annotations, download gene sets, compute overlaps between gene sets, categorize members of a gene set by gene families, and build an expression signature of a gene set using a compendium of expression profiles.

Columbia University

Principal Investigator: Andrea Califano, Ph.D.

B Cell Interactome
Contact: Celine Lefebvre, Ph.D., Columbia University
Web Link: http://amdec-bioinfo.cu-genome.org/html/BCellInteractome.html

     The B cell interactome (BCI) is a network of protein-protein, protein-DNA and modulatory interactions in human B cells. The network contains known interactions (reported in public databases) and predicted interactions by a Bayesian evidence integration framework which integrates a variety of generic and context specific experimental clues about protein-protein and protein-DNA interactions - such as a large collection of B cell expression profiles - with inferences from different reverse engineering algorithms, such as GeneWays and ARACNE. Modulatory interactions are predicted by MINDY, an algorithm for the prediction of modulators of transcriptional interactions.

TransfactomeDB
Contact: Harmen Bussemaker, Ph.D., Columbia University
Web Link: http://bussemakerlab.org/TransfactomeDB/

     Accurate and comprehensive information about the nucleotide sequence specificity of trans-acting factors (TFs) is essential for computational and experimental analyses of gene regulatory networks. The Yeast Transfactome Database is a repository of sequence specificity models and condition-specific regulatory activities for a large number of DNA- and RNA-binding proteins in Saccharomyces cerevisiae. The sequence specificities in TransfactomeDB, represented as position-specific affinity matrices (PSAMs), are directly estimated from genomewide measurements of TF-binding using our previously published MatrixREDUCE algorithm, which is based on a biophysical model. For each mRNA expression profile in the NCBI Gene Expression Omnibus, we used sequence-based regression analysis to estimate the post-translational regulatory activity of each TF for which a PSAM is available. The trans-factor activity profiles across multiple experiments available in TransfactomeDB allow the user to explore potential regulatory roles of hundreds of TFs in any of thousands of microarray experiments.

Georgetown University
Principal Investigator: Robert Clarke, Ph.D.

GEO: GSE8562
Contact: Rebecca Riggins, Ph.D. (rbr7@georgetown.edu)
Web Link: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8562

     XBP1 confers estrogen independence and antiestrogen resistance in breast cancer cells. Microarray gene expression data.

Massachusetts Institute of Technology (MIT)
Principal Investigator: Doug Lauffenburger, Ph.D.

The MIT/ICBP siRNA Database
Contact: Mary Lindstrom, Ph.D. mwernett@mit.edu
Web Link: http://web.mit.edu/sirna

     An on-line database of experimentally validated siRNAs and shRNAs against target genes, with a focus on genes thought to be involved in cancer.

MIT ICBP Website
Contact: Brian Joughin, Ph.D. bjoughin@mit.edu
Web Link: http://web.mit.edu/icbp/data
     All MIT ICBP publications are listed as they are published. Links are provided to abstracts at PubMed, and where appropriate, supplemental data files are hosted as well.

PTMScout
Contact: ptmscout_admin@mit.edu
Web Link: http://ptmscout.mit.edu

     PTMScout is a tool for viewing and analyzing high-throughput post-translational modification proteomic data, with a particular eye towards novel hypothesis generation. External data from Gene Ontology, PFAM, and Scansite are integrated with user data automatically, as well as expression data in a large number of tissue types and cell lines. Additionally, all post-translational proteomic mass spectrometry datasets from the White Lab at MIT are banked here, and other users may include their data on request.

Memorial Sloan-Kettering Cancer Center
Principal Investigator: Chris Sander, Ph.D.

Pathway Commons
Contact: Anil Korkut, Ph.D. (akorkut@cbio.mskcc.org)
Web Link: http://www.pathwaycommons.org

    Pathway Commons is a convenient point of access to biological pathway information collected from public pathway databases, which you can browse or search.

Cancer Genomics Data Portal
Contact: Anil Korkut, Ph.D. (akorkut@cbio.mskcc.org)
Web Link: http://cbio.mskcc.org/cancergenomics-dataportal/

    The cBio Cancer Genomics Data Portal provides direct download and visualization of large-scale cancer genomics data sets, currently Prostate Cancer (MSKCC) and Glioblastoma multiforme (TCGA).

The Ohio State University
Principal Investigator: Tim H-M Huang, Ph.D.

Estrogen Receptor Target Database (ERTargetDB)
Contact: Kun Huang, Ph.D. (Kun.Huang@osumc.edu)
Web Link: http://bioinformatics.wistar.upenn.edu/ERTargetDB/

    ERTargetDB integrates information from the ongoing Chip-on-chip experiments in our laboratory and promoter sequence conservation from the OMGProm database. We will add methylation and acetylation patterns across the target gene promoters in the future versions of the database.

Hormone Receptor Target Binding Loci Database (HRTBLDB)
Contact: Kun Huang, Ph.D. (Kun.Huang@osumc.edu)
Web Link: http://motif.bmi.ohio-state.edu/hrtbldb/
     Data from high through-put lab techniques (ChIP-chip, ChIP-Seq, and ChIP-PET) are integrated in to this unified database to aid research on the hormone signaling pathways that regulate gene expression. Hormone induced differential gene expression has been implicated in numerous pathological conditions. Given this implication, research in hormone receptor binding sites is increasing at a rapid pace, and with the advent of whole genome analysis, the volume of data generated by this research is immense. HRTBLDb is built to help researchers collaborate and prosper from this explosion of data.

mirPromoter
Contact: Yunlong Liu, Ph.D.
Web Link: http://compbio.iupui.edu/group/6/pages/mirpromoter
     ChIP-seq data, microarray data, and microRNA promoter prediction data.

miR2Disease
Contact: Kun Huang, Ph.D. (Kun.Huang@osumc.edu)
Web Link: http://www.mir2disease.org/

    miR2Disease , a manually curated database, aims at providing a comprehensive resource of miRNA deregulation in various human diseases. Each entry in the miR2Disease contains detailed information on a miRNA-disease relationship, including miRNA ID, disease name, a brief description of the miRNA-disease relationship, miRNA expression pattern in the disease state, detection method for miRNA expression, experimentally verified miRNA target gene(s), and literature reference.

Sage Bionetworks
Principal Investigator: Stephen Friend, M.D., Ph.D.

Sage Available and Transition Datasets
Contact: repdata@sagebase.org
Web Link: http://www.sagebase.org/commons/dataset1.php

St. Elizabeth’s Medical Center/Tufts University
Principal Investigator: Lynn Hlatky, Ph.D.

Tumor Dormancy Gene Expression Profiling
Contact: Julia Fox, Ph.D. (julia.l.fox.phd@gmail.com)
Web Link: http://www.ebi.ac.uk/microarray-as/ae/browse.html?keywords=E-TABM-390&species=&array=&exptype%5B%5D=&exptype%5B%5D=&pagesize=25&sortby=releasedate&sortorder=descending

   Whole genome expression profiling (Agilent) of tumor tissue from experiments directed toward tumor dormancy and emergence from dormancy.

Stanford University School of Medicine
Principal Investigator: Sylvia Plevritis, Ph.D.

Cytobank
Contact: Help desk (info@cytobank.org)
Web Link: http://www.cytobank.org/
    Manage, analyze and share flow cytometry data over the web.

Vanderbilt University Medical Center
Principal Investigator: Vito Quaranta, M.D.

Data Sets:

Contact: Jerome Jourquin, Ph.D. (jerome.jourquin@vanderbilt.edu)
Web Link: http://vicbc.vanderbilt.edu/ccsb/research/datasets

    This is a listing of the different datasets that have been generated in our Center. Solely informative, the data itself is not yet directly available. The names of the experimentalists who generated the data are associated with each dataset and can be contacted for more information.