* 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
Thursday, October 11, 2012
Abstract for Talk at Harvard (i0hsph)
Title : Human Genome Annotation
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.
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.
Thursday, October 4, 2012
2012 HUPO Abstract Entry Confirmation 294
Your abstract for the HUPO2012 conference was submitted on 5/3/2012.The log number for your abstract is 294. | ||||||||||||||||||||
| Analysis of Protein Networks | ||||||||||||||||||||
| Mark Gerstein | ||||||||||||||||||||
| 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).
| ||||||||||||||||||||
| ||||||||||||||||||||
Saturday, May 19, 2012
Re: Abstract for talk at CBCB Symposium at U Del ((* i0udcbcbrs *))
Many thanks Mark, We are looking forward to your inspiring talk :-)
Best, Cathy
On 5/19/2012 12:04 AM, Mark Gerstein wrote:
> TITLE:
>
> Molecular Networks: The Next-generation Annotation for Personal Genomes
>
> Mark Gerstein
>
> Yale University
>
> My talk will be concerned 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-dimension browser tracks. 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
>
> Comparing genomes to computer operating systems in terms of the
> topology and evolution of their regulatory control networks.
> KK Yan, G Fang, N Bhardwaj, RP Alexander, M Gerstein (2010). Proc Natl
> Acad Sci U S A 107:9186-91.
>
> Analysis of diverse regulatory networks in a hierarchical context
> shows consistent tendencies for collaboration in the middle levels.
> N Bhardwaj, KK Yan, MB Gerstein (2010). Proc Natl Acad Sci U S A 107:6841-6.
>
> Positive selection at the protein network periphery: evaluation in
> terms of structural constraints and cellular context.
> PM Kim, JO Korbel, MB Gerstein (2007). Proc Natl Acad Sci U S A 104:20274-9.
>
> The tYNA platform for comparative interactomics: a web tool for
> managing, comparing and mining multiple networks.
> KY Yip, H Yu, PM Kim, M Schultz, M Gerstein (2006). Bioinformatics 22:2968-70.
--
Cathy H. Wu, Ph.D.
Edward G. Jefferson Chair of Bioinformatics& Computational Biology
Department of Computer& Information Sciences
Department of Biological Sciences
Director, Center for Bioinformatics& Computational Biology (CBCB)
Director, Master's Programs in Bioinformatics& Computational Biology
Director, Protein Information Resource (PIR)
Delaware Biotechnology Institute
University of Delaware
15 Innovation Way, Suite 205
Newark, DE 19711-5449
Email: wuc@udel.edu
Phone: 302-831-8869; Fax: 302-831-4841
Adm Assistant: Susan Phipps
Email: phipps@dbi.udel.edu
Phone: 302-831-0161
CBCB home: http://bioinformatics.udel.edu
Master's programs: http://bioinformatics.udel.edu/Education
PIR Home: http://ProteinInformationResource.org
Best, Cathy
On 5/19/2012 12:04 AM, Mark Gerstein wrote:
> TITLE:
>
> Molecular Networks: The Next-generation Annotation for Personal Genomes
>
> Mark Gerstein
>
> Yale University
>
> My talk will be concerned 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-dimension browser tracks. 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
>
> Comparing genomes to computer operating systems in terms of the
> topology and evolution of their regulatory control networks.
> KK Yan, G Fang, N Bhardwaj, RP Alexander, M Gerstein (2010). Proc Natl
> Acad Sci U S A 107:9186-91.
>
> Analysis of diverse regulatory networks in a hierarchical context
> shows consistent tendencies for collaboration in the middle levels.
> N Bhardwaj, KK Yan, MB Gerstein (2010). Proc Natl Acad Sci U S A 107:6841-6.
>
> Positive selection at the protein network periphery: evaluation in
> terms of structural constraints and cellular context.
> PM Kim, JO Korbel, MB Gerstein (2007). Proc Natl Acad Sci U S A 104:20274-9.
>
> The tYNA platform for comparative interactomics: a web tool for
> managing, comparing and mining multiple networks.
> KY Yip, H Yu, PM Kim, M Schultz, M Gerstein (2006). Bioinformatics 22:2968-70.
--
Cathy H. Wu, Ph.D.
Edward G. Jefferson Chair of Bioinformatics& Computational Biology
Department of Computer& Information Sciences
Department of Biological Sciences
Director, Center for Bioinformatics& Computational Biology (CBCB)
Director, Master's Programs in Bioinformatics& Computational Biology
Director, Protein Information Resource (PIR)
Delaware Biotechnology Institute
University of Delaware
15 Innovation Way, Suite 205
Newark, DE 19711-5449
Email: wuc@udel.edu
Phone: 302-831-8869; Fax: 302-831-4841
Adm Assistant: Susan Phipps
Email: phipps@dbi.udel.edu
Phone: 302-831-0161
CBCB home: http://bioinformatics.udel.edu
Master's programs: http://bioinformatics.udel.edu/Education
PIR Home: http://ProteinInformationResource.org
Friday, May 18, 2012
Abstract for talk at CBCB Symposium at U Del ((* i0udcbcbrs *))
TITLE:
Molecular Networks: The Next-generation Annotation for Personal Genomes
Mark Gerstein
Yale University
My talk will be concerned 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-dimension browser tracks. 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
Comparing genomes to computer operating systems in terms of the
topology and evolution of their regulatory control networks.
KK Yan, G Fang, N Bhardwaj, RP Alexander, M Gerstein (2010). Proc Natl
Acad Sci U S A 107:9186-91.
Analysis of diverse regulatory networks in a hierarchical context
shows consistent tendencies for collaboration in the middle levels.
N Bhardwaj, KK Yan, MB Gerstein (2010). Proc Natl Acad Sci U S A 107:6841-6.
Positive selection at the protein network periphery: evaluation in
terms of structural constraints and cellular context.
PM Kim, JO Korbel, MB Gerstein (2007). Proc Natl Acad Sci U S A 104:20274-9.
The tYNA platform for comparative interactomics: a web tool for
managing, comparing and mining multiple networks.
KY Yip, H Yu, PM Kim, M Schultz, M Gerstein (2006). Bioinformatics 22:2968-70.
Molecular Networks: The Next-generation Annotation for Personal Genomes
Mark Gerstein
Yale University
My talk will be concerned 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-dimension browser tracks. 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
Comparing genomes to computer operating systems in terms of the
topology and evolution of their regulatory control networks.
KK Yan, G Fang, N Bhardwaj, RP Alexander, M Gerstein (2010). Proc Natl
Acad Sci U S A 107:9186-91.
Analysis of diverse regulatory networks in a hierarchical context
shows consistent tendencies for collaboration in the middle levels.
N Bhardwaj, KK Yan, MB Gerstein (2010). Proc Natl Acad Sci U S A 107:6841-6.
Positive selection at the protein network periphery: evaluation in
terms of structural constraints and cellular context.
PM Kim, JO Korbel, MB Gerstein (2007). Proc Natl Acad Sci U S A 104:20274-9.
The tYNA platform for comparative interactomics: a web tool for
managing, comparing and mining multiple networks.
KY Yip, H Yu, PM Kim, M Schultz, M Gerstein (2006). Bioinformatics 22:2968-70.
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