Dear Dr. Gerstein,
Thank you for your abstract. Co you please provide me with an abstract title and if there are more authors then just you, please send me their names and institutes.
Thank you and have a good day!
Program Implementation Associate
From: Mark Gerstein [mailto:email@example.com]
Sent: Saturday, November 30, 2013 11:45 AM
Subject: Abstract for Talk at Keystone Big Data Symposium (i0keybdata)
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 genomics.
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 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.