My talk will discuss Human Genome Analysis from a data science perspective.
Plummeting sequencing costs have led to a great increase in the number
of personal genomes. Interpreting the large number of variants in
them, particularly in non-coding regions, is a central challenge for
One data science construct that is particularly useful for genome
interpretation is networks. 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.
Saturday, November 30, 2013
Abstract for Talk at Keystone Big Data Symposium (i0keybdata)
Posted by Gerstein Lab Abstracts at 10:45 AM
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