Thursday, October 11, 2012
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).
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
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.
Thursday, October 4, 2012
Your abstract for the HUPO2012 conference was submitted on 5/3/2012.The log number for your abstract is 294.
Analysis of Protein Networks
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).