Computational Biology and Bioinformatics Discussion Group

Organizer: Olga Troyanskaya

Abstracts

Dana Pe'er, Columbia University:
"Genetic Variation and Regulatory Networks: Mechanisms and Complexity"

Sequence polymorphisms affect gene expression by perturbing the complex network of regulatory interactions. Standard methods attempt to associate each gene expression phenotype with genetic polymorphisms. We suggest a novel computational method, called Geronemo, which aims to understand the mechanism by which genetic changes perturb gene regulation. By exploiting the modularity of biological systems, Geronemo reveals regulatory relationships that are indiscernible when genes are considered in isolation, allowing the recovery of intricate combinatorial regulation.
We applied Geronemo to a set of yeast recombinants generated by a cross between laboratory (BY) and wild (RM) strains of S. cerevisiae (Brem & Kruglyak, 2005), resulting in multiple novel hypotheses about genetic perturbations in the yeast regulatory network, including in transcriptional regulation, signal transduction, and chromatin modification. In this talk we will present 3 key findings:

1. A significant part of the observed expression change arises from individual genetic variation of a small number of chromatin modifying factors.
2. Our method uncovered a novel mRNA degradation mechanism that couples P-bodies and the Puf family of mRNA factors, which we validated experimentally.
3. Based on individual gene expression in rich media, we predicts sensitivity of individuals to the drug Rapamycin, which we also validated experimentally.

Joint work with Suin Lee, Aimee Dudley, George Church and Daphne Koller.

Xiaole Shirley Liu, Harvard University:
"Analysis of ChIP-chip on Genome Tiling Arrays for the Study of Nuclear Hormone Receptor Cistrome"
Chromatin Immunoprecipitation coupled with DNA microarray analysis (ChIP-chip) has quickly evolved as a popular technique to study the in vivo targets of transcription factors (TF) and other DNA-binding proteins at the genome level. We developed a series of algorithms for the analysis of ChIP-chip on genome tiling microarrays, including a Microarray Blob Remover (MBR) to filter probes in blob defects on the array, an algorithm for eXtreme fast MApping of Nucleotide (XMAN) probes to the genome, a Model-based Analysis of Tiling arrays (MAT) that models probe baseline behavior from probe sequence and genome copy numbers, and a web Cis-Element Annotation System (CEAS) for a comprehensive annotation of TF-bound ChIP-regions in the genome. We applied these algorithms to the ChIP-chip data of Estrogen Receptor (ER) and Androgen Receptor (AR) on Affymetrix whole human genome tiling arrays to identify their cistrome, the set of cis-acting targets of a trans-acting factor on a genome scale. These efforts identified thousands of novel binding sites, most of which are far from the promoters of known genes. A screen for enriched motifs within those binding sites revealed both the typical and non-typical AR or ER responsive elements (ARE or ERE) and several co-factor motifs, including the Forkhead, Oct and C/EBP, all confirmed with experimental validation. Specific targeted silencing of these various cofactors differentially affects hormone-induced gene expression and cell cycle progression.

Richard Bonneau, New York University:
"Learning of regulatory modules and predictive models of global transcriptional dynamics with application to the extreemophile Halobacterium NRC-1"

Our system for network inference and modeling consists of three major components: cMonkey (a method for learning co-regulated biclusters and pathways), the Inferelator (regulatory network inference) and the Gaggle (a system for visualizing and managing the results of the analysis as well as the input data). These three components have been described individually