Abstract for Talk at Harvard (i0hsph)

Title : Human Genome Annotation

My talk will be concerned with the analysis of networks and the use of
networks as a "next-generation annotation" for interpreting personal
genomes. I will initially describe current approaches to genome
annotation in terms of one-dimensional browser tracks. Here I will discuss
approaches for annotating pseudogenes and also
for developing predictive models for gene expression.
Then I will describe various aspects of networks. In particular, I will touch on
the following topics: (1) I will show how analyzing the structure of
the regulatory network indicates that it has a hierarchical layout
with the "middle-managers" acting as information-flow bottlenecks and
with more "influential" TFs on top. (2) I will show that most human
variation occurs at the periphery of the network. (3) I will compare
the topology and variation of the regulatory network to the call graph
of a computer operating system, showing that they have different
patterns of variation. (4) I will talk about web-based tools for the
analysis of networks (TopNet and tYNA).

http://networks.gersteinlab.org
http://tyna.gersteinlab.org

Architecture of the human regulatory network derived from ENCODE data.
Gerstein et al. Nature 489: 91

Classification of human genomic regions based on experimentally
determined binding sites of more than 100 transcription-related
factors.
KY Yip et al. (2012). Genome Biol 13: R48.

Understanding transcriptional regulation by integrative analysis of
transcription factor binding data.
C Cheng et al. (2012). Genome Res 22: 1658-67.

The GENCODE pseudogene resource.
B Pei et al. (2012). Genome Biol 13: R51.

Comparing genomes to computer operating systems in terms of the
topology and evolution of their regulatory control networks.
KK Yan et al. (2010). Proc Natl Acad Sci U S A 107:9186-91.

2012 HUPO Abstract Entry Confirmation 294

Your abstract for the HUPO2012 conference was submitted on 5/3/2012.The log number for your abstract is 294.

Analysis of Protein Networks

Mark Gerstein

Yale Comp. Bio., New Haven, CT

Abstract My talk will be concerned with understanding protein function on a genomic scale. My lab approaches this through the prediction and analysis of biological networks, focusing on protein-protein interaction and transcription-factor-target ones. I will describe how these networks can be determined through integration of many genomic features and how they can be analyzed in terms of various topological statistics. In particular, I will discuss a number of recent analyses: (1) Improving the prediction of molecular networks through systematic training-set expansion; (2) Showing how the analysis of biochemical pathways across environments potentially allows them to act as biosensors; (3) Analyzing the structure of the regulatory network indicates that it has a hierarchical layout with the "middle-managers" acting as information bottlenecks; (4) Integrating the protein-interaction network with molecular structures and motions; (5) Showing the some motions are conflicting with protein-protein interactions and (6) Creating practical web-based tools for the analysis of these networks (DynaSIN and tYNA).   networks.gersteinlab.org dynasin.molmovdb.org

Corresponding Author:   Mark Gerstein   Yale Comp. Bio.   MBB   PO Box 208114   New Haven, CT 06520-8114   United States   2034328189   Mark.gerstein@yale.edu