Monday, March 25, 2013

Abstract for talk i0cmg - Comp_Meth_Prioritizing_Var_Exome_Seq_Pgenes_trueLoF_nethubs--20130319-i0cmg

* Filtering out Artifacts Due to Pseudogenes
-Certain genes have lots of similar pseudogenes
which could confound variant calling

* Finding True LoF Mutations
-Not just stop codon finding: tricky if one takes into
account splicing, NMD, indels, &c

* Using High Network Connectivity
-More connected genes in many networks have a
greater chance of being disease causing

Abstract for Talk at Chicago (i0chi12)

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.

Abstract for Genome_Annotation_Compare_n_Func_Description_SVs_n_Nets--20130322-i0simons

1 ## Annotation via Large-scale Identification of Variable Blocks in
the Population

Read-depth: MSB+CNVnator
Breakpoints & Split Read: SRiC, AGE & BreakSeq

Applications : 1000G & Somatic Variation

2 ## A Networks View on Large-scale Organization of Genomic Elements

Understanding the human regulatory network as a hierarchy with
information flow bottlenecks
Understanding the impact of variation and constraint on the network
Particularly with network analogies