Tuesday, June 3, 2008

Abstract for talk [I:BIOMARKER] at Biomarker Discovery Summit, 30 Sep and 1 Oct 2008


Using Networks to Integrate Omic and Semantic Data: Towards Understanding
Protein Function on a Genome-scale

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 (including those derived from using the semantic web
and text mining) and how they can be analyzed in terms of
various simple topological statistics. In particular, I will discuss: (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, PubNet and
tYNA; (6) Using known semantic web relationships as training sets to
improve biological query applications. And (7) using literature data to predict
protein interactions.


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.

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.

The role of disorder in interaction networks: a structural analysis.
PM Kim, A Sboner, Y Xia, M Gerstein (2008) Mol Syst Biol 4: 179.

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.

Leveraging the structure of the Semantic Web to enhance information retrieval
for proteomics.
A Smith, K Cheung, M Krauthammer, M Schultz, M Gerstein (2007) Bioinformatics
23: 3073-9.

LinkHub: a Semantic Web system that facilitates cross-database queries and
information retrieval in proteomics.
AK Smith, KH Cheung, KY Yip, M Schultz, MK Gerstein (2007) BMC Bioinformatics 8
Suppl 3: S5.

Data mining on the web.
A Smith, M Gerstein (2006) Science 314: 1682;

Sunday, June 1, 2008

abstract for talk at ISMB SIG on Genome-scale Pattern Analysis in the Post-ENCODE Era on 21-Jul-08 [I:ISMB08-SIG]

Title: Human Genome Annotation

A central problem for 21st century science will be the annotation and
understanding of the human genome. My talk will be concerned with
topics within this area, in particular annotating pseudogenes (protein
fossils), binding sites, CNVs, and novel transcribed regions in the
genome. Much of this work has been carried out in the framework of the
ENCODE and modENCODE projects.

In particular, I will discuss how we identify regulatory regions and
novel, non-genic transcribed regions in the genome based on processing
of tiling array and next-generation sequencing experiments. I will
further discuss how we cluster together groups of binding sites and
novel transcribed regions.

Next, I will discuss a comprehensive pseudogene identification
pipeline and storage database we have built. This has enabled us to
identify >10K pseudogenes in the human and mouse genomes and analyze
their distribution with respect to age, protein family, and
chromosomal location. I will try to inter-relate our studies on
pseudogenes with those on transcribed regions. At the end I will bring
these together, trying to assess the transcriptional activity of

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


The ambiguous boundary between genes and pseudogenes: the dead rise up, or do they?
D Zheng, MB Gerstein (2007) Trends Genet 23: 219-24.

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

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

Toward a universal microarray: prediction of gene expression through
nearest-neighbor probe sequence identification.
TE Royce, JS Rozowsky, MB Gerstein (2007) Nucleic Acids Res 35: e99.

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 (2007) Genome Res 17: 839-51.

Statistical analysis of the genomic distribution and correlation of regulatory
elements in the ENCODE regions.
ZD Zhang, A Paccanaro, Y Fu, S Weissman, Z Weng, J Chang, M Snyder, MB Gerstein
(2007) Genome Res 17: 787-97.

The DART classification of unannotated transcription within the ENCODE regions:
associating transcription with known and novel loci.
JS Rozowsky, D Newburger, F Sayward, J Wu, G Jordan, JO Korbel, U Nagalakshmi, J
Yang, D Zheng, R Guigo, TR Gingeras, S Weissman, P Miller, M Snyder, MB Gerstein
(2007) Genome Res 17: 732-45.

What is a gene, post-ENCODE? History and updated definition.
MB Gerstein, C Bruce, JS Rozowsky, D Zheng, J Du, JO Korbel, O Emanuelsson, ZD
Zhang, S Weissman, M Snyder (2007) Genome Res 17: 669-81.

Systematic prediction and validation of breakpoints associated with copy-number
variants in the human genome.
JO Korbel, AE Urban, F Grubert, J Du, TE Royce, P Starr, G Zhong, BS Emanuel, SM
Weissman, M Snyder, MB Gerstein (2007) Proc Natl Acad Sci U S A 104: 10110-5.