Brown University Center for Computational Molecular Biology

Events

CCMB Seminar Series 2004-2005

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Center for Computational Molecular Biology Seminar Series

Biological and Bioinformatic Approaches to Identifying Regulatory Elements in mRNA

Scott A.Tenenbaum
Ge*NY*Sis Center for Excellence in Cancer Genomics
University at Albany-SUNY

Abstract:
We have used methods for purifying endogenously formed mRNP complexes and identifying their associated mRNA targets using microarray technologies (ribonomic profiling). This has enabled the genomic-scale identification of many mRNA targets of RNA-binding proteins (RBPs) and has provided new insights into the principles governing post-transcriptional gene regulation. Using Affymetrix tiling arrays for human chromosomes 21-22, we have extended our findings and determined the associations of both coding and noncoding RNAs for several RBPs including HuR, IMP, La and PABP. Tiling arrays are designed to exhaustively span a designated genetic sequence in a high-density manner to include all coding and noncoding regions exclusive of highly repeated regions. This allows for the exhaustive and unbiased analysis of mRNP associated RNA, annotated and un-annotated, as well as the identification of alternative spliced products and there association with RBPs. The tiling array platform used in this study interrogates on average, every 35 bases of the approximately 35 million base pairs of chr 21-22 (Kapranov et al., 2002). Previously, using tiling arrays, it was unexpectedly observed that a great deal more genomic sequence was transcribed into RNA than can currently be accounted for using our present annotation. Limited analysis of these novel transcripts revealed that they possess little protein coding potential and frequently occupy an antisense orientation relative to well-characterized coding transcripts. By combining ribonomic profiling with tiling arrays, our studies indicate that in addition to targeting predicted mRNAs, many of the noncoding RNAs expressed from the genome also appear to be associated with RBPs in a specific and selective manner. In addition to having significant uniqueness in exonic, intronic and novel RNA specificity, we also observe potentially meaningful overlaps in the RNA subset affinities of the RBPs that we targeted. The UTRs of many mRNAs contain sequence and structural motifs that are used to regulate the stability, localization and translatability of mRNA. Unfortunately, the consensus sequences for these motifs frequently have significant variability and are only loosely characterized making the use of simple alignment tools inadequate for the discovery of new RNA regulatory motifs. Additionally, many software tools utilize adaptive techniques requiring training. We have generated a collection of positive control Training UTR datasets called “the UAlbany TUTR collection” which is meant to be used as blind training/test sets that contain a previously characterized RNA motif conforming to a defined consensus. The basic training sets have been generated with associated indexes and "answer sets" produced to identify where the previously characterized RNA motif (e.g. the IRE, ARE, SECIS, etc.) resides in each sequence. The UAlbany TUTR collection is meant to be a shared resource and has been made available to a number of researchers for software testing. The strengths and weaknesses of different algorithms to successfully identify different consensus motifs will be presented. Additionally, we are presently developing customized tiling array based ribonomic profiling technology which enables the genomic-scale foot-printing of RBP binding sites. Examples of this technology will also be discussed.

Monday, May 9, 2005
4:00 pm
70 Ship Street, Room 107

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Gene Regulation and Probabilistic Graphical Models

Nir Friedman
Hebrew University, Israel
Currently visiting at the Bauer Center for Genomics Research and the Division of Engineering and Applied Science at Harvard University

Abstract:
High-throughput genome-wide molecular assays have become central to molecular biology. These assays probe cellular networks from different perspectives and provide rich and diverse data, posing the challenge of developing methodologies for extracting meaningful biological insights. The challenge for computational biology is to provide methodologies for transforming high-throughput heterogeneous datasets into biological insights about the underlying mechanisms. Integration of data from assays that examine cellular systems from different viewpoints can lead to a more coherent reconstruction, reduce the effects of noise, and provide new knowledge about the relevant biological entities and processes. One class of approaches to answer this challenge builds on probabilistic graphical models. Such models provide a concise representation of complex cellular networks models by composing simpler sub-models. Procedures based on well understood principles for inferring such models from data facilitate a model—based methodology for analysis and discovery. In this talk I will attempt to discuss few recent projects that use the language of probabilistic graphical models to model and understand gene regulation and function from genomics datasets.

Wednesday, April 27, 2005
4:00 pm
McMillan Hall, Room 115

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