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.

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).   networks.gersteinlab.org dynasin.molmovdb.org

Corresponding Author:   Mark Gerstein   Yale Comp. Bio.   MBB   PO Box 208114   New Haven, CT 06520-8114   United States   2034328189   Mark.gerstein@yale.edu

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

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.

Abstract for talk at CCBR at U Toronto ((* i0tor12 *))

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.

Abstract for talk at Next Generation Sequencing for Drug Developers ((* i0ngenddev *))

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.

abstract for talk at Harvard Systems Biology [i0hsb]

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.

abstract for talk at UCLA [i0ucla]

TITLE:

Analysis of Molecular Networks

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.

abstract for talk at CMU [i0cmu12]

TITLE:

Analysis of Molecular Networks

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.

abstract for talk at UMD Center for Bioinformatics and Computational Biology [i0cbcb]

TITLE:

Analysis of Molecular Networks

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.

abstract for talk at DOE Workshop ((* i0gtl2012 *))

Tools and Approaches for Integrating Multiple genetic and Cellular Networks

One important task of KBase is to provide a platform to help users
analyze gene biological function and inspire experiments for the
purpose of biofuel development. A gene's biological function is
essentially its relationships or interactions with other biological
objects within the cell and around the environment. It is necessary to
understand gene function on a genomic scale, and from the integration
of genetic and cellular networks. We approach this through the
prediction and analysis of biological networks, focusing on
protein-protein and transcription-factor-target interactions. We
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, we will report a number of
recent analyses: (1) Improving the prediction of molecular networks
through systematic training-set expansion; (2) Showing how the
analysis of pathways across environments potentially allow them to act
as biosensors; (3) Analyzing the structure of the regulatory network
which indicates that it has a hierarchical layout with the
"middle-managers" acting as information bottlenecks; (4) Showing these
middle managers tend be arranged in various "partnership" structures
giving the hierarchy a "democratic character" ; (5) Comparing the
topology and variation of the regulatory network to the call graph of
a computer operating system; (6) Developing a framework to integrate
together various kinds of  biological networks (e.g. relating to TFs
and miRNAs) into an integrated meta-network; (7) Integrating this
meta-network with actual molecular structures; and (8) Creating
practical web-based tools for the analysis of these networks (DynaSIN
and tYNA).

http://www.orau.gov/gtl2012

Re: abstract for talk at Berkeley Statistics and Genomics Seminar 16-Feb-2012 ((* i0sf12 *))

Thanks Mark. I look forward to seeing you on February 16th.

Best,
Sandrine

Sent from my iPhone

On Jan 29, 2012, at 21:03, Mark Gerstein <Mark.Gerstein@yale.edu> wrote:

> Approaches to Genome Annotation
>
> Mark Gerstein
>
> Yale U., New Haven, CT, USA
>
> A central problem for 21st century science is annotating the human
> genome and making this annotation useful for the interpretation of
> personal genomes. My talk will focus on annotating the bulk of the
> genome that does not code for canonical genes, concentrating on
> intergenic features such as TF binding sites, non-coding RNAs
> (ncRNAs), and pseudogenes (protein fossils). I will describe an
> overall framework for data integration that brings together different
> evidence to annotate features such as binding sites and ncRNAs. Much
> of this work has been carried out within the ENCODE and modENCODE
> projects, and I will describe my approach interchangeably both in
> human and various model organisms (e.g. worm). I will further explain
> how many different annotations can be inter-related to characterize
> the intergenic space, build regulatory networks, and construct
> predictive models of gene expression from chromatin features and the
> activity at binding sites.
>
> URLS:
>
> http://pseudogene.org
> http://GenomeTECH.Gersteinlab.org

abstract for talk at Berkeley Statistics and Genomics Seminar 16-Feb-2012 ((* i0sf12 *))

Approaches to Genome Annotation

Mark Gerstein

Yale U., New Haven, CT, USA

A central problem for 21st century science is annotating the human
genome and making this annotation useful for the interpretation of
personal genomes.  My talk will focus on annotating the bulk of the
genome that does not code for canonical genes, concentrating on
intergenic features such as TF binding sites, non-coding RNAs
(ncRNAs), and pseudogenes (protein fossils). I will describe an
overall framework for data integration that brings together different
evidence to annotate features such as binding sites and ncRNAs. Much
of this work has been carried out within the ENCODE and modENCODE
projects, and I will describe my approach interchangeably both in
human and various model organisms (e.g. worm). I will further explain
how many different annotations can be inter-related to characterize
the intergenic space, build regulatory networks, and construct
predictive models of gene expression from chromatin features and the
activity at binding sites.

URLS:

http://pseudogene.org
http://GenomeTECH.Gersteinlab.org