Gene Myers , Ph.D.
"Whole Genome Sequencing, Comparative Genomics, and Systems Biology"

The whole-genome shotgun sequencing method with paired end-reads has proven rapid and economical, producing high-quality reconstructions of Drosophila (2000), Human (2001) and Mouse (2001), in quick succession. We discuss the overall algorithmic strategy, the results one can expect by comparing the whole genome assembly of Drosophila against the recently finished sequence, and advances such as high-density solid state sequencing and single molecule detection systems.

We anticipate having the euchromatic portions of the genomes of twelve species of Drosophila in the next year. We discuss the current state of the art in comparative gene finding, cis-control module finding, and possible improvements. The hope of these approaches is that we will be able to accurately identify the “parts lists” of the D. melanogaster genome, a basic prerequisite for systems biology.

We conclude with a segment on the possibility of a program of high-throughput in-situ image analysis in Drosophila embryos. We describe what information we might collect and what we might be able to infer form it. It is our contention that this may be the best way to understand development from a systems perspective.

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Ron Shamir, Ph.D.
"Computational Dissection of Regulatory Networks Using Diverse High-throughput Data"

The maturation of high-throughput technologies and the availability of whole genome sequences make it possible to apply holistic computational approaches to the study of biological systems. The use of high-throughput technologies requires the development of advanced computational methods and tools that would enable the elicitation of significant biological knowledge from the vast amounts of data generated by these methods. Our group has been developing a battery of such methodologies and incorporated some of them in several tools:

• CLICK (CLuster Identification via Connectivity Kernels): a clustering algorithm that combines graph-theoretic approaches and statistical considerations to yield solutions that balance intra-cluster homogeneity and inter-cluster separation.
• PRIMA (PRomoter Integration in Microarray Analysis): a promoter sequence analysis tool that aims at the identification of transcription factors whose binding sites are significantly over-represented in promoters of co-expressed genes. Using microarrays to compare the transcriptional response in wild-type and Atm-deficient mice, we used CLICK and PRIMA to identify, on a genomic scale, a DNA damage transcriptional response that is dependent on the ATM protein kinase, and dissected this response network into two major arms that are mediated by the p53 and NF-_B transcriptional regulators.
• SAMBA (Statistical-Algorithmic Method for Bicluster Analysis): a method for finding subsets of genes that manifest a significant co-expression within particular subsets of the conditions. The method is graph-theoretic and based on a statistical model of the data generation. We demonstrated the utility of SAMBA in mining biological knowledge out of large and highly heterogeneous genome-wide yeast datasets. These included gene expression profiles, and data on protein-protein interactions, phenotypes and transcription factor binding locations. Our approach analyzes such heterogeneous data set in an inherently integrative manner. SAMBA dissected the yeast system into modules, each comprising a set of genes that share common features over diverse data sources. Using these modules, we were able to predict the function of over 800 unknown genes, and validated some predictions experimentally. We were also able to obtain broad perspectives on the interaction of transcription factors and modules, and on the hierarchical organization of modules in yeast.
• EXPANDER (EXPression ANalyzer and DisplayER): an integrative platform for the analysis of gene expression data, providing multiple analysis algorithms including CLICK, PRIMA and SAMBA, along with a variety of data normalization and visualization utilities.
• SHARP (SHowcase for ATM Related Pathways): an interactive software environment that displays graphically biological interaction networks, allows dynamic layout and navigation through these networks, and the superposition of DNA microarray data on interaction maps.
• Binding Site Evolution: a novel genome-wide analysis method for detecting binding sites in aligned promoters of related species, that is based primarily on identifying selection forces and not mere conservation. We demonstrate the method to the data of several yeast species, and the analysis reveals novel fascinating details on the evolution of transcription factor binding sites.
• MetaReg: a methodology for the representation and analysis of heterogeneous biological networks. The network elements include mRNAs, proteins and metabolites, and cycles are allowed. We developed methods for the comparison of the model prediction to actual measurements, and for the generation of hypotheses where discrepancy between predictions and observations is large. We demonstrate the approach on the lysine biosynthesis pathway in yeast.

Our (more mature) tools are available at http://www.cs.tau.ac.il/~rshamir

Joint work, in parts, with Amos Tanay[2], Irit Gat-Viks[1], Ran Elkon1, Roded Sharan1[4], Chaim Linhart1, Adi Maron1, Nir Orlev[2], Giora Sternberg[2], Martin Kupiec[3], Sharon Rashi-Elkeles[2], Yosef Shiloh[2]

[1] School of Compter Science, Sackler Faculty of Exact Sciences
[2] Department of Human Genetics, Sackler School of Medicine
[3] George S. Wise Faculty of Life Sciences, Tel Aviv University, Israel
[4] Current address: International Computer Science Institute, Berkeley, CA

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