Furey Lab Research Interests
Chromatin Structure and Relationship to Gene Expression
Chromosomes are compacted into increasingly complex chromatin structures within eukaryotic nuclei. In collaboration with Greg Crawford and his lab, we analyze data from genome-wide DNaseI hypersensitivity experiments using high-throughput Illumina sequencing and whole-genome microarrays to identify regions of open chromatin as described in our recent publication in Cell. The computational integration of these data with related gene expression, transcription factor binding, and epigenetic data will provide a more complete picture of the complex process of gene transcription.
We are members of the ENCODE Consortium whose goal is to identify all functional elements in the human genome. Along with the Crawford, our group includes Jason Lieb's lab at UNC-Chapel Hill, Vishy Iyer's lab at Univ Texas-Austin, and Ewan Birney's lab at the EBI. The goal of our group is to create an open chromatin map of the human genome in several diverse cell types, and to provide an initial functional annotation of these regions. The Furey lab is developing computational methods to integrate substantial sequence and microarray data from DNase I hypersensitivity, FAIRE, and ChIP experiments to create these maps.
To promote these analyses, We maintain a full mirror of the UCSC Genome Browser at http://genome-mirror.duhs.duke.edu. This not only provides direct access to the extensive database of annotations of the human and other genomes compiled at UCSC, but also allows us to develop new annotations related to ongoing research based on analyses performed within the lab.
Cancer Genomics
Cancer is a complex disease with many histological subtypes and probably thousands of molecular subtypes that differ substantially with respect to their onset, progression, and response to treatment. High-throughput microarray and sequence-based assays are now capable of assessing genome-wide changes and variation in gene expression, genome copy number, allele expression, and DNA methylation status throughout cancer initiation and progression. These experiments reveal different yet complementary information regarding the current state of a population of cancer cells. This ability to molecularly characterize cancer has already resulted in novel diagnostic tests and treatments. Current computational models designed to distinguish between phenotypically disparate samples are generally accurate but are difficult to interpret biologically and primarily rely on data from a single molecular assay. The careful and accurate integration of complementary data in biologically interpretable models will provide a more complete and interpretable portrait of cancer, for example providing new and stronger evidence of genetic changes associated with the root causes of observed differential gene expression.
The goal of our lab is to develop statistical methods and computational software that integrate high-dimensional heterogeneous but complementary data from cancer samples to identify fundamental genomic alterations with clinical and biological relevance. These methods are being developed in collaboration with Sayan Mukherjee and Phil Febbo.
POSTDOC OPPORTUNITY
We are currently recruiting a post-doctoral researcher to join our cancer genomics team. Excellent candidates should possess a strong background in computational biology, have strong programming skills, and the desire to fully understand biological questions being explored. Prior experience with machine learning techniques and microarray analysis desired.
Graduate Student Opportunities
Students in the lab generally are part of either the graduate program in Computational Biology & Bioinformatics or from the Department of Computer Science, but any with strong computational backgrounds and an interest in biology should feel free to contact Terry.