* 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
Monday, March 25, 2013
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).
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.
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.
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
Methods
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
the Population
Methods
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
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