Abstract for talk [I] at Engingeering Cell Biology (ECB) meeting to be held at MIT on Aug. 5-8, 2007

TITLE:

Understanding Protein Function on a Genome-scale using Networks

Mark Gerstein

Yale University

My talk will be concerned with topics in proteomics, in particular
predicting protein function on a genomic scale. We approach 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 simple topological statistics. In particular, I will discuss a
number of specific analyses: (1) Integrating gene expression data with
the regulatory network illuminates transient hubs; (2) Integration of
the protein interaction network with 3D molecular structures reveals
different types of hubs, depending on the number of interfaces
involved in interactions (one or many); (3) Analysis of betweenness in
biological networks reveals that this quantity is more strongly
correlated with essentially than degree; (4) Analysis of structure of
the regulatory network shows that it has a hierarchiel layout with the
"middle-managers" acting as information bottlenecks. (5) Development
of a useful web-based tools for the analysis of networks, TopNet and
tYNA.

http://bioinfo.mbb.yale.edu
http://topnet.gersteinlab.org

TopNet: a tool for comparing biological sub-networks, correlating
protein properties with topological statistics. H Yu, X Zhu, D
Greenbaum, J Karro, M Gerstein (2004) Nucleic Acids Res 32: 328-37.

Genomic analysis of regulatory network dynamics reveals large
topological changes. NM Luscombe, MM Babu, H Yu, M Snyder, SA
Teichmann, M Gerstein (2004) Nature 431: 308-12.

Annotation transfer between genomes: protein-protein interologs and
protein-DNA regulogs. H Yu, NM Luscombe, HX Lu, X Zhu, Y Xia, JD Han,
N Bertin, S Chung, M Vidal, M Gerstein (2004) Genome Res 14: 1107-18.

Integrated prediction of the helical membrane protein interactome in
yeast. Y Xia, LJ Lu, M Gerstein (2006) J Mol Biol 357: 339-49.

Relating three-dimensional structures to protein networks provides
evolutionary insights. PM Kim, LJ Lu, Y Xia, MB Gerstein (2006)
Science 314: 1938-41.

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.

Positive Selection at the Protein Network Periphery: Evaluation in
Terms of Structural Constraints and Cellular Context. Philip M. Kim,
Jan O. Korbel and Mark B. Gerstein PNAS (in press)

The importance of bottlenecks in protein networks: correlation with
gene essentiality and expression dynamics. H Yu, PM Kim, E Sprecher,
V Trifonov, M Gerstein (2007) PLoS Comput Biol 3: e59.

Genomic analysis of the hierarchical structure of regulatory networks.
H Yu, M Gerstein (2006) Proc Natl Acad Sci U S A 103: 14724-31.

Abstract for talk [I] at MSCBB: 1st Annual Midwestern Computational Biology and Bioinformatics Symposium

TITLE:

Understanding Protein Function on a Genome-scale using Networks

Mark Gerstein

Yale University

My talk will be concerned with topics in proteomics, in particular
predicting protein function on a genomic scale. We approach 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 simple topological statistics. In particular, I will discuss a
number of specific analyses: (1) Integrating gene expression data with
the regulatory network illuminates transient hubs; (2) Integration of
the protein interaction network with 3D molecular structures reveals
different types of hubs, depending on the number of interfaces
involved in interactions (one or many); (3) Analysis of betweenness in
biological networks reveals that this quantity is more strongly
correlated with essentially than degree; (4) Analysis of structure of
the regulatory network shows that it has a hierarchiel layout with the
"middle-managers" acting as information bottlenecks. (5) Development
of a useful web-based tools for the analysis of networks, TopNet and
tYNA.

http://bioinfo.mbb.yale.edu
http://topnet.gersteinlab.org

TopNet: a tool for comparing biological sub-networks, correlating
protein properties with topological statistics. H Yu, X Zhu, D
Greenbaum, J Karro, M Gerstein (2004) Nucleic Acids Res 32: 328-37.

Genomic analysis of regulatory network dynamics reveals large
topological changes. NM Luscombe, MM Babu, H Yu, M Snyder, SA
Teichmann, M Gerstein (2004) Nature 431: 308-12.

Annotation transfer between genomes: protein-protein interologs and
protein-DNA regulogs. H Yu, NM Luscombe, HX Lu, X Zhu, Y Xia, JD Han,
N Bertin, S Chung, M Vidal, M Gerstein (2004) Genome Res 14: 1107-18.

Integrated prediction of the helical membrane protein interactome in
yeast. Y Xia, LJ Lu, M Gerstein (2006) J Mol Biol 357: 339-49.

Relating three-dimensional structures to protein networks provides
evolutionary insights. PM Kim, LJ Lu, Y Xia, MB Gerstein (2006)
Science 314: 1938-41.

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.

Positive Selection at the Protein Network Periphery: Evaluation in
Terms of Structural Constraints and Cellular Context. Philip M. Kim,
Jan O. Korbel and Mark B. Gerstein PNAS (in press)

The importance of bottlenecks in protein networks: correlation with
gene essentiality and expression dynamics. H Yu, PM Kim, E Sprecher,
V Trifonov, M Gerstein (2007) PLoS Comput Biol 3: e59.

Genomic analysis of the hierarchical structure of regulatory networks.
H Yu, M Gerstein (2006) Proc Natl Acad Sci U S A 103: 14724-31.

--
Mark.Gerstein@yale.edu * 203 432-6105 *

http://bioinfo.mbb.yale.edu

abstract for talk [I] at CPI-2007

Title: Human Genome Annotation, Focusing on Intergenic Regions

A central problem for 21st century science will be the analysis and
understanding of the human genome. My talk will be concerned with
topics within this area, in particular annotating pseudogenes (protein
fossils) in the genome. I will discuss a comprehensive pseudogene
identification pipeline and storage database we have built. This has
enabled use to identify >10K pseudogenes in the human and mouse
genomes and analyze their distribution with respect to age, protein
family, and chromosomal location. One interesting finding is the large
number of ribosomal pseudogenes in the human genome, with 80
functional ribosomal proteins giving rise to ~2,000 ribosomal protein
pseudogenes.

I will try to inter-relate our studies on pseudogenes with those on
tiling arrays, which enable one to comprehensively probe the activity
of intergenic regions. At the end I will bring these together, trying
to assess the transcriptional activity of pseudogenes.

Throughout I will try to introduce some of the computational
algorithms and approaches that are required for genome annotation and
tiling arrays -- i.e. the construction of annotation pipelines,
developing algorithms for optimal tiling, and refining approaches for
scoring microarrays.

http://bioinfo.mbb.yale.edu
http://pseudogene.org
http://tiling.gersteinlab.org

Comparative analysis of processed pseudogenes in the mouse and human
genomes.
Z Zhang, N Carriero, M Gerstein (2004) Trends Genet 20: 62-7.

Millions of years of evolution preserved: a comprehensive catalog of
the processed pseudogenes in the human genome.
Z Zhang, PM Harrison, Y Liu, M Gerstein (2003) Genome Res 13: 2541-58.

Patterns of nucleotide substitution, insertion and deletion in the
human genome inferred from pseudogenes. Z Zhang, M Gerstein (2003)
Nucleic Acids Res 31: 5338-48.

Integrated pseudogene annotation for human chromosome 22: evidence for
transcription.
D Zheng, Z Zhang, PM Harrison, J Karro, N Carriero, M Gerstein (2005)
J Mol Biol 349: 27-45.

P. Bertone, F. Schubert, V. Trifonov, J. Rozowsky, O. Emanuelsson,
J. Karro, M-Y Kao, M. Snyder, M. Gerstein. Design optimization methods
for genomic DNA tiling arrays. Genome Research (in press).

TE Royce, JS Rozowsky, P Bertone, M Samanta, V Stolc, S Weissman, M
Snyder, M Gerstein (2005). "Issues in the analysis of oligonucleotide
tiling microarrays for transcript mapping." Trends Genet 21: 466-75.

Pseudogenes in the ENCODE Regions: Consensus Annotation, Analysis of
Transcription and Evolution
D Zheng, A Frankish, R Baertsch, P Kapranov, A Reymond, SW Choo, Y Lu, F
Denoeud, SE Antonarakis, M Snyder, Y Ruan, CL Wei, TR Gingeras, R Guigo, J
Harrow, MB Gerstein (in press) Genome Research.

The ambiguous boundary between genes and pseudogenes: the dead rise up, or do they?
D Zheng, MB Gerstein (2007) Trends Genet

Pseudogene.org: a comprehensive database and comparison platform for pseudogene
annotation.
JE Karro, Y Yan, D Zheng, Z Zhang, N Carriero, P Cayting, P Harrrison, M
Gerstein (2007) Nucleic Acids Res 35: D55-60.

A computational approach for identifying pseudogenes in the ENCODE regions.
D Zheng, MB Gerstein (2006) Genome Biol 7 Suppl 1: S13.1-10.

The real life of pseudogenes.
M Gerstein, D Zheng (2006) Sci Am 295: 48-55.

PseudoPipe: an automated pseudogene identification pipeline.
Z Zhang, N Carriero, D Zheng, J Karro, PM Harrison, M Gerstein (2006)
Bioinformatics 22: 1437-9.

Abstract for talk [I] at NetSci2007

TITLE:

Understanding Protein Function on a Genome-scale using Networks

Mark Gerstein
Yale University

My talk will be concerned with topics in proteomics, in particular
predicting protein function on a genomic scale. We approach 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 simple topological statistics. In particular, I will discuss a
number of specific analyses: (1) Integrating gene expression data with
the regulatory network illuminates transient hubs; (2) Integration of
the protein interaction network with 3D molecular structures reveals
different types of hubs, depending on the number of interfaces
involved in interactions (one or many); (3) Analysis of betweenness in
biological networks reveals that this quantity is more strongly
correlated with essentially than degree; (4) Analysis of structure of
the regulatory network shows that it has a hierarchiel layout with the
"middle-managers" acting as information bottlenecks. (5) Development
of a useful web-based tools for the analysis of networks, TopNet and
tYNA.

http://bioinfo.mbb.yale.edu
http://topnet.gersteinlab.org

TopNet: a tool for comparing biological sub-networks, correlating protein
properties with topological statistics.
H Yu, X Zhu, D Greenbaum, J Karro, M Gerstein (2004) Nucleic Acids Res 32: 328-37.

Genomic analysis of regulatory network dynamics reveals large topological changes.
NM Luscombe, MM Babu, H Yu, M Snyder, SA Teichmann, M Gerstein (2004)
Nature 431: 308-12.

Annotation transfer between genomes: protein-protein interologs and protein-DNA
regulogs.
H Yu, NM Luscombe, HX Lu, X Zhu, Y Xia, JD Han, N Bertin, S Chung, M Vidal,
M Gerstein (2004) Genome Res 14: 1107-18.

Genomic analysis of the hierarchical structure of regulatory networks.
H Yu, M Gerstein (2006) Proc Natl Acad Sci U S A

Integrated prediction of the helical membrane protein interactome in yeast.
Y Xia, LJ Lu, M Gerstein (2006) J Mol Biol 357: 339-49.

Relating three-dimensional structures to protein networks provides evolutionary
insights.
PM Kim, LJ Lu, Y Xia, MB Gerstein (2006) Science 314: 1938-41.

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.

--
Mark.Gerstein@yale.edu * 203 432-6105 *

http://bioinfo.mbb.yale.edu

abstract for talk [I] at Cistrome2007

Title: Human Genome Annotation, Focussing on Intergenic Regions

A central problem for 21st century science will be the analysis and
understanding of the human genome. My talk will be concerned with
topics within this area, in particular annotating pseudogenes (protein
fossils) in the genome. I will discuss a comprehensive pseudogene
identification pipeline and storage database we have built. This has
enabled use to identify >10K pseudogenes in the human and mouse
genomes and analyze their distribution with respect to age, protein
family, and chromosomal location. One interesting finding is the large
number of ribosomal pseudogenes in the human genome, with 80
functional ribosomal proteins giving rise to ~2,000 ribosomal protein
pseudogenes.

I will try to inter-relate our studies on pseudogenes with those on
tiling arrays, which enable one to comprehensively probe the activity
of intergenic regions. At the end I will bring these together, trying
to assess the transcriptional activity of pseudogenes.

Throughout I will try to introduce some of the computational
algorithms and approaches that are required for genome annotation and
tiling arrays -- i.e. the construction of annotation pipelines,
developing algorithms for optimal tiling, and refining approaches for
scoring microarrays.

http://bioinfo.mbb.yale.edu
http://pseudogene.org
http://tiling.gersteinlab.org

Comparative analysis of processed pseudogenes in the mouse and human
genomes.
Z Zhang, N Carriero, M Gerstein (2004) Trends Genet 20: 62-7.

Millions of years of evolution preserved: a comprehensive catalog of
the processed pseudogenes in the human genome.
Z Zhang, PM Harrison, Y Liu, M Gerstein (2003) Genome Res 13: 2541-58.

Patterns of nucleotide substitution, insertion and deletion in the
human genome inferred from pseudogenes. Z Zhang, M Gerstein (2003)
Nucleic Acids Res 31: 5338-48.

Integrated pseudogene annotation for human chromosome 22: evidence for
transcription.
D Zheng, Z Zhang, PM Harrison, J Karro, N Carriero, M Gerstein (2005)
J Mol Biol 349: 27-45.

P. Bertone, F. Schubert, V. Trifonov, J. Rozowsky, O. Emanuelsson,
J. Karro, M-Y Kao, M. Snyder, M. Gerstein. Design optimization methods
for genomic DNA tiling arrays. Genome Research (in press).

TE Royce, JS Rozowsky, P Bertone, M Samanta, V Stolc, S Weissman, M
Snyder, M Gerstein (2005). "Issues in the analysis of oligonucleotide
tiling microarrays for transcript mapping." Trends Genet 21: 466-75.

Pseudogenes in the ENCODE Regions: Consensus Annotation, Analysis of
Transcription and Evolution
D Zheng, A Frankish, R Baertsch, P Kapranov, A Reymond, SW Choo, Y Lu, F
Denoeud, SE Antonarakis, M Snyder, Y Ruan, CL Wei, TR Gingeras, R Guigo, J
Harrow, MB Gerstein (in press) Genome Research.

The ambiguous boundary between genes and pseudogenes: the dead rise up, or do they?
D Zheng, MB Gerstein (2007) Trends Genet

Pseudogene.org: a comprehensive database and comparison platform for pseudogene
annotation.
JE Karro, Y Yan, D Zheng, Z Zhang, N Carriero, P Cayting, P Harrrison, M
Gerstein (2007) Nucleic Acids Res 35: D55-60.

A computational approach for identifying pseudogenes in the ENCODE regions.
D Zheng, MB Gerstein (2006) Genome Biol 7 Suppl 1: S13.1-10.

The real life of pseudogenes.
M Gerstein, D Zheng (2006) Sci Am 295: 48-55.

PseudoPipe: an automated pseudogene identification pipeline.
Z Zhang, N Carriero, D Zheng, J Karro, PM Harrison, M Gerstein (2006)
Bioinformatics 22: 1437-9.