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Associate Research Scientist
November 18, 2017 - December 18, 2017
The Department of Pathology and Cell Biology at the Columbia University Medical Center is recruiting an Associate Research Scientist in the laboratory of Dr. Kevin Gardner. The Gardner lab studies basic mechanisms of epigenetic gene regulation in breast cancer. The lab approaches basic questions in breast cancer biology with an integrated interdisciplinary focus on fundamental mechanisms of chromatin-based transcriptional control, epigenetic regulation and the influence of the environment diet and metabolic imbalance (Proc Natl Acad Sci U S A. 2009 Nov 17;106(46):19286-91; Nat Struct Mol Biol. 2010 Dec;17(12):1406-13; Nat Commun. 2012 Jan 17;3:633; Nat Commun. 2013;4:1449). Major goals include understanding the role of metabolism and the transcriptional cross-talk between epigenetic co-regulators and hormone receptor-mediated signaling pathways in both enhancer function and promoter-targeted mechanisms of gene control. An essential component of this work will involve developing omic perspectives that will integrate next-generation sequencing with genomics, transcriptomics, epigenomics, proteomics and metabolomics using model systems and patient-derived samples. The projects will be supported by access to an established cohort of diverse breast cancer patient samples with extensive molecular and genomic characterization to aid in developing translation perspectives in gene-network and pathway discovery that will have practical implications for improving breast cancer diagnosis, treatment, and prevention.
Postdoctoral experience and an extensive background in bioinformatics including high throughput analysis pipelines such as, ChIPSeq, RNASeq, ExomeSeq, microarray analysis, peak calling and comparative enrichment analyses. The candidate will provide support for routine CHIPSeq, DNAse Hypersensitivity, ATAC-seq and MethylIP Analysis and provide bioinformatics support on DNA motif analysis. The candidate should have expertise in both peak calling of ChIP-seq data as well as RNA-seq analysis for splicing changes. Experience with off the shelf tools, including Bowtie, Scripture, Cufflinks, TopHat, SICER, and MI Knowledge in statistics is essential. Proficiency in one, preferably two, of the following programming languages: Shell, Perl, Python, R, Java and C/C++ is essential. Experience and familiarity with public databases: NCBI, Ensembl, TCGA, cBioPortal, Broad FireHose is essential. Central goals in the projects are to identify meaningful genome-wide or locus/DNA-feature-specific changes. The ability to generate novel scripts for specific applications, including SNP calling for allele-specific effects and distance mapping for positional effects is highly desired. Experience in applied statistics and design of experiments is desirable. Experience in submitting data sets to public repositories is required. In addition, a general knowledge of the computational biology literature in the epigenomics and splicing fields will be highly competitive.