The Computational Genomics Core (CGC) supports the Einstein community by providing essential informatics resources and infrastructure for the analysis and interpretation of large genomic and epigenomic datasets, providing for timely and standardized delivery of data to investigators, and to organize and present tutorials for data retrieval and analysis using the provided tools and methodologies. The CGC will develop primary analysis pipelines, analysis and visualization tools for application-specific handling of data using open-source and commercial analysis tools. All tools will be maintained and deployed in a manner that optimally supports the research activities of individual investigators.
- Data management: storage and delivery of large genomic data sets.
- Data analysis: primary and secondary interpretation of microarrays and next-generation sequencing data.
- Pipeline development: assist researchers and core facilities with the development of reproducible, accurate and time-saving pipelines and workflows for analysis of genome-scale experimental data.
- Consultation: work with investigators to provide access to the appropriate tools for analysis of their data.
- Training: to aid investigators in the analysis of their own data through tutorials covering software and analysis methodologies.
Individuals will receive a first hour of consultation with a bioinformaticist without charge. Subsequent work will be charged at a rate of $125/hour ($150/hour external). Ongoing or long-term consultation will be eligible for negotiation of a lower hourly rate.
Please email email@example.com to submit an analysis request.
John Greally, M.B., PhD
R. F. Thompson, M. Suzuki, K. W. Lau, and J. M. Greally. A pipeline for the quantitative analysis of CG dinucleotide methylation using mass spectrometry. Bioinformatics, 25(17):2164–70, Sep 2009.
R. F. Thompson, M. Reimers, B. Khulan, M. Gissot, T. A. Richmond, Q. Chen, X. Zheng, K. Kim, and J. M. Greally. An analytical pipeline for genomic representations used for cytosine methylation studies. Bioinformatics, 24(9):1161–7, May 2008.
R. B. Calder, R. B. Beems, H. van Steeg, I. S. Mian, P. H. M. Lohman, and J. Vijg. MPHASYS: a mouse phenotype analysis system. BMC Bioinformatics, 8:183, 2007.