Biomedical subjects

Sündüz Keleş

Publications and source records attributed to Sündüz Keleş.

4 recordsLinked to original sources

PubMed2006

Novel TRF1/BRF target genes revealed by genome-wide analysis of Drosophila Pol III transcription.

Metazoans have evolved multiple paralogues of the TATA binding protein (TBP), adding another tunable level of gene control at core promoters. While TBP-related factor 1 (TRF1) shares extensive homology with TBP and can direct both Pol II and Pol III transcription in vitro, TRF1 target sites in vivo have remained elusive. Here, we report the genome-wide identification of TRF1-binding sites using high-resolution genome tiling microarrays. We found 354 TRF1-binding sites genome-wide with approximately 78% of these sites displaying colocalization with BRF. Strikingly, the majority of TRF1 target genes are Pol III-dependent small noncoding RNAs such as tRNAs and small nonmessenger RNAs. We provide direct evidence that the TRF1/BRF complex is functionally required for the activity of two novel TRF1 targets (7SL RNA and small nucleolar RNAs). Our studies suggest that unlike most other eukaryotic organisms that rely on TBP for Pol III transcription, in Drosophila and possibly other insects the alternative TRF1/BRF complex appears responsible for the initiation of all known classes of Pol III transcription.

Animals↗

PubMed2006

Multiple testing methods for ChIP-Chip high density oligonucleotide array data.

Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription factors along human chromosomes 21 and 22 using ChIP-Chip experiments. ChIP-Chip experiments are a new approach to the genomewide identification of transcription factor binding sites and consist of chromatin (Ch) immunoprecipitation (IP) of transcription factor-bound genomic DNA followed by high density oligonucleotide hybridization (Chip) of the IP-enriched DNA. We investigate the ChIP-Chip data structure and propose methods for inferring the location of transcription factor binding sites from these data. The proposed methods involve testing for each probe whether it is part of a bound sequence using a scan statistic that takes into account the spatial structure of the data. Different multiple testing procedures are considered for controlling the familywise error rate and false discovery rate. A nested-Bonferroni adjustment, which is more powerful than the traditional Bonferroni adjustment when the test statistics are dependent, is discussed. Simulation studies show that taking into account the spatial structure of the data substantially improves the sensitivity of the multiple testing procedures. Application of the proposed methods to ChIP-Chip data for transcription factor p53 identified many potential target binding regions along human chromosomes 21 and 22. Among these identified regions, 18% fall within a 3 kb vicinity of the 5'UTR of a known gene or CpG island and 31% fall between the codon start site and the codon end site of a known gene but not inside an exon. More than half of these potential target sequences contain the p53 consensus binding site or very close matches to it. Moreover, these target segments include the 13 experimentally verified p53 binding regions of Cawley et al. (2004), as well as 49 additional regions that show higher hybridization signal than these 13 experimentally verified regions.

5' Untranslated Regions↗

PubMed2002

Residual-based tree-structured survival analysis.

Extensions of various non-parametric regression techniques (for example, additive models, trees, MARS) have been devised for right censored survival data. These approaches directly handle the difficulties posed by censoring. However, it is possible to bypass these difficulties by utilizing standard non-parametric regression procedures applied with (say) martingale residuals as outcome. Analytic correspondences between the direct and residual-based approaches have been established for additive models while more qualitative comparisons have been provided for MARS. Here we develop such correspondences for tree-structured regression. In particular, we provide an analytic relationship between logrank and martingale residual sum-of-squares split functions that explains the widely observed similarity of the resultant trees. Further investigation is provided by simulation and an illustrative example using time to AIDS with data deriving from a Western Australian HIV cohort study.

Biomarkers↗

PubMed2002

Identification of regulatory elements using a feature selection method.

MOTIVATION: Many methods have been described to identify regulatory motifs in the transcription control regions of genes that exhibit similar patterns of gene expression across a variety of experimental conditions. Here we focus on a single experimental condition, and utilize gene expression data to identify sequence motifs associated with genes that are activated under this experimental condition. We use a linear model with two-way interactions to model gene expression as a function of sequence features (words) present in presumptive transcription control regions. The most relevant features are selected by a feature selection method called stepwise selection with monte carlo cross validation. We apply this method to a publicly available dataset of the yeast Saccharomyces cerevisiae, focussing on the 800 basepairs immediately upstream of each gene's translation start site (the upstream control region (UCR)). RESULTS: We successfully identify regulatory motifs that are known to be active under the experimental conditions analyzed, and find additional significant sequences that may represent novel regulatory motifs. We also discuss a complementary method that utilizes gene expression data from a single microarray experiment and allows averaging over variety of experimental conditions as an alternative to motif finding methods that act on clusters of co-expressed genes. AVAILABILITY: The software is available upon request from the first author or may be downloaded from http://www.stat.berkeley.edu/~sunduz. CONTACT: keles@stat.berkeley.edu

Amino Acid Motifs↗