Bioinformatics Core

About the Bioinformatics Core

Welcome to the Bioinformatics Core (BIC)! The range of high-throughput technologies available for studying the mechanisms of epigenetic modification is rapidly expanding. As this expansion progresses, the importance of epigenetics researchers having access to advanced bioinformatics support is growing. The goal of the Bioinformatics Core (BIC) of the University of Michigan NIEHS P30 Center is to enhance the interpretation of experimental and clinical results from a broad range of epigenetic studies. Our objective is to provide advanced-level bioinformatics support for studies conducted by University of Michigan NIEHS P30 Center investigators.

The BIC is available to help members of the Center throughout the experimental process, from identifying and articulating their bioinformatics needs, through experimental design, to providing analyses required to successfully address challenges inherent to high-throughput data. Bioinformatics is now widely recognized as essential for these kinds of studies; it is separate from, but complementary with biostatistics.

We make use of a wealth of available bioinformatics resources and also provide novel or custom analyses tailored specifically to the needs of Center researchers. To enhance the effectiveness of this research, the BIC offers an introductory level of analysis for free. For more extensive projects, we offer discounted bioinformatics services to Center members through the UM Center for Computational Medicine and Bioinformatics (CCMB). The BIC also maintains close working relationships with the UM DNA Sequencing Core and the UM CTSA.

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Location and Contact Information

We are located at:
2044 Palmer Commons & NCRC, Building 10, Suite A121
University of Michigan
Ann Arbor, MI

To contact us, email Maureen Sartor or Rich McEachin.

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Membership

Leader:  Maureen A. Sartor, Ph.D.
Maureen A. Sartor, Ph.D.
Research Assistant Professor
Center for Computational Medicine and Bioinformatics
sartorma@umich.edu

Co-Leader:  Gilbert S. Omenn, M.D., Ph.D.
Gilbert S. Omenn, M.D., Ph.D.
Director of Center for Computational Medicine and Bioinformatics
Professor of Internal Medicine, Human Genetics, & Public Health
gomenn@med.umich.edu

Member:  Richard McEachin, Ph.D.
Richard McEachin, Ph.D.
Research Investigator
Center for Computational Medicine and Bioinformatics
mceachin@umich.edu

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Core Services

While we expect most inquiries to involve high-throughput analysis [1-3], we are not limited to those technologies. We offer guidance in the use of bioinformatics tools related to, but not limited to, transcription factor binding motifs/modules, gene/toxin relationships, functions and biological processes, public high-throughput data repositories, genome visualizations, regulatory prediction, and protein interaction networks. We also have tools for natural language search and processing of the biomedical literature.

Bioinformatics expertise is available in areas including:

  • Epigenomics [4, 5]: DNA methylation (microarrays, Methyl-Seq, MeDIP-Seq), histone modifications (ChIP-Seq), and microRNA analyses
  • Genomics [4, 6, 7]: microarrays, RNA-Seq, Genome Wide Association, linkage
  • Proteomics [8]
  • Metabolomics[9]
  • Regulatory mechanisms and transcriptomics [10]
  • Phenotypes [11]
  • Data management[8]
  • Integrative analyses and Systems Biology [6, 9, 12-17] (pathways, annotation)

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Bioinformatics Training

Classes and/or one-on-one sessions are available (see below, for resource details). Recent sessions include training on Cytoscape (hands-on and webinar formats), ConceptGen, Gene2MeSH, and MiMI. Suggestions for future offerings are welcome. For more information on training sessions, contact Marci Brandenburg, Maureen Sartor, or Rich McEachin.

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Selected Tools & Resources

Cytoscape - Cytoscape is an open source bioinformatics platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data. The Cytoscape core distribution provides a basic set of features for data integration and visualization. Additional features are available as plugins. (http://www.cytoscape.org)

MetScape - The MetScape plugin for Cytoscape provides a bioinformatics framework for the visualization and interpretation of metabolomic and gene expression profiling data, in the context of human metabolism. It allows users to build and analyze networks of genes and compounds, identify enriched pathways from expression profiling data, and visualize changes in metabolite data. (http://metscape.ncibi.org)

MiMI - The MiMI database comprehensively includes protein interaction information that has been integrated and merged from diverse protein interaction databases and other biological sources. MiMi Web gives you an easy-to-use interface to a rich data repository for conducting your systems biology analyses. This repository includes the MiMI database, PubMed resources updated nightly, and text mined from biomedical research literature. (http://mimi.ncibi.org)

ConceptGen and LRpath - ConceptGen is a gene set enrichment and gene set relation mapping tool that can help you identify, explore, and visualize relationships and significant overlaps among sets of genes (concepts). ConceptGen is built on a repository of gene and protein annotation data drawn from diverse sources. (http://conceptgen.ncibi.org).
LRpath is an alternative, statistically powerful gene set enrichment testing method that can aid in interpreting high-throughput results, such as from gene expression or DNA methylation experiments (http://lrpath.ncibi.org).

Gene2MeSH - Gene2MeSH uses a statistical approach to reliably and automatically annotate genes with the concepts defined in Medical Subject Headings (MeSH), the National Library of Medicine's controlled vocabulary for biology and medicine. The Gene2MeSH web application searches gene symbols or MeSH terms and displays resulting pairs of genes and MeSH terms that match the search term. The gene / MeSH term pairs displayed are those that are significantly over-represented in PubMed abstracts. (http://gene2mesh.ncibi.org)

Epigenomics web portal (Bisphenol A) - Genomics Portals is an integrative, web-based computational platform for the analysis and mining of genomics data, developed at the University of Cincinnati by a BIC external advisor. It is a useful resource especially for those studying BPA. (http://eh3.uc.edu/GenomicsPortals)

Comparative Toxicogenomic Database (CTD) - CTD advances understanding of the effects of environmental chemicals on human health. CTD includes curated data describing cross-species chemical–gene/protein interactions and chemical– and gene–disease relationships to illuminate molecular mechanisms underlying variable susceptibility and environmentally influenced diseases. These data provide insights into complex chemical–gene and protein interaction networks. (http://ctd.mdibl.org)

GeneGo - Need a tool to make biological sense of high-throughput data? GeneGo provides a commercial solution for using gene/protein/miRNA/RNA/drug/metabolite lists and “omics” data to generate and prioritize hypotheses. MetaCore is an integrated knowledge database and software suite for pathway analysis of experimental data and gene lists. The scope of data types includes microarray and sequence-based gene expression, SNPs and CGH arrays, proteomics, metabolomics, Co-IP pull-out and other custom interactions. ToxHunter is a systems toxicology knowledgebase and data analysis package, designed for the assessment of safety liabilities of drugs, environmental contaminants and other xenobiotics at all stages of discovery and development. CCMB has a license for use of GeneGo, which is a western Michigan company. (www.genego.com)

GenePattern – GenePattern is a powerful genomic analysis platform developed at the Broad Institute. It provides access to more than 150 tools for gene expression, RNA-seq, proteomics, and SNP analysis; flow cytometry; and common data processing tasks. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research. (http://www.broadinstitute.org/genepattern)

Genomatix - Genomatix is a leading commercial supplier of technologies to analyze and interpret genomic data. It is based in Munich, Germany and has its US office in Ann Arbor. In particular, MatInspector is Genomatix' widely used program for identification of transcription factor binding sites. MatBase is a comprehensive transcription factor knowledge base. (www.genomatix.de)

Additional Resources:
The Center for Computational Medicine and Bioinformatics (CCMB)
The National Center for Integrative Biomedical Informatics (NCIBI)

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References

  1. Wu, G., et al., Statistical quantification of methylation levels by next-generation sequencing. PLoS One, 2011. 6(6): p. e21034. PMID: 21698242
  2. Ma, W. and W.H. Wong, The analysis of ChIP-Seq data. Methods Enzymol, 2011. 497: p. 51-73. PMID: 21601082
  3. Sartor, M.A., et al., Genome-wide methylation and expression differences in HPV(+) and HPV(-) squamous cell carcinoma cell lines are consistent with divergent mechanisms of carcinogenesis. Epigenetics, 2011. 6(6): p. 777-87. PMID: 21613826
  4. Sartor, M.A., et al., Genomewide analysis of aryl hydrocarbon receptor binding targets reveals an extensive array of gene clusters that control morphogenetic and developmental programs. Environ Health Perspect, 2009. 117(7): p. 1139-46. PMID: 19654925
  5. Ren, X.P., et al., MicroRNA-320 is involved in the regulation of cardiac ischemia/reperfusion injury by targeting heat-shock protein 20. Circulation, 2009. 119(17): p. 2357-66. PMID: 19380620
  6. McEachin, R.C., et al., A genetic network model of cellular responses to lithium treatment and cocaine abuse in bipolar disorder. BMC Syst Biol, 2010. 4: p. 158. PMID: 21092101
  7. Kivela, R., et al., Gene expression centroids that link with low intrinsic aerobic exercise capacity and complex disease risk. FASEB J, 2010. 24(11): p. 4565-74. PMID: 20643908
  8. Omenn, G.S., Data management and data integration in the HUPO plasma proteome project. Methods Mol Biol, 2011. 696: p. 247-57. PMID: 21063952
  9. Sartor, M.A., et al., ConceptGen: a gene set enrichment and gene set relation mapping tool. Bioinformatics, 2010. 26(4): p. 456-63. PMID: 20007254
  10. O'Geen, H., et al., Genome-wide binding of the orphan nuclear receptor TR4 suggests its general role in fundamental biological processes. BMC Genomics, 2010. 11: p. 689. PMID: 21126370
  11. Keller, B.J. and R.C. McEachin, Identifying hypothetical genetic influences on complex disease phenotypes. BMC Bioinformatics, 2009. 10 Suppl 2: p. S13. PMID: 19208188
  12. McEachin, R.C., et al., Modeling complex genetic and environmental influences on comorbid bipolar disorder with tobacco use disorder. BMC Med Genet, 2010. 11: p. 14. PMID: 20102619
  13. Ma, J., M.A. Sartor, and H.V. Jagadish, Appearance frequency modulated gene set enrichment testing. BMC Bioinformatics, 2011. 12: p. 81. PMID: 21418606
  14. Wang, X.S., et al., An integrative approach to reveal driver gene fusions from paired-end sequencing data in cancer. Nat Biotechnol, 2009. 27(11): p. 1005-11.PMID: 19881495
  15. Sartor, M.A., G.D. Leikauf, and M. Medvedovic, LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics, 2009. 25(2): p. 211-7. PMID: 19038984
  16. Tian, Y., et al., SAGA: a subgraph matching tool for biological graphs. Bioinformatics, 2007. 23(2): p. 232-9. PMID: 17110368
  17. Wang, P., et al., SNP Function Portal: a web database for exploring the function implication of SNP alleles. Bioinformatics, 2006. 22(14): p. e523-9. PMID: 16873516

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