Biomedical subjects

Peter Tonellato

Publications and source records attributed to Peter Tonellato.

3 recordsLinked to original sources

PubMed2005

Analysis of concordance of different haplotype block partitioning algorithms.

BACKGROUND: Different classes of haplotype block algorithms exist and the ideal dataset to assess their performance would be to comprehensively re-sequence a large genomic region in a large population. Such data sets are expensive to collect. Alternatively, we performed coalescent simulations to generate haplotypes with a high marker density and compared block partitioning results from diversity based, LD based, and information theoretic algorithms under different values of SNP density and allele frequency. RESULTS: We simulated 1000 haplotypes using the standard coalescent for three world populations--European, African American, and East Asian--and applied three classes of block partitioning algorithms--diversity based, LD based, and information theoretic. We assessed algorithm differences in number, size, and coverage of blocks inferred under different conditions of SNP density, allele frequency, and sample size. Each algorithm inferred blocks differing in number, size, and coverage under different density and allele frequency conditions. Different partitions had few if any matching block boundaries. However they still overlapped and a high percentage of total chromosomal region was common to all methods. This percentage was generally higher with a higher density of SNPs and when rarer markers were included. CONCLUSION: A gold standard definition of a haplotype block is difficult to achieve, but collecting haplotypes covered with a high density of SNPs, partitioning them with a variety of block algorithms, and identifying regions common to all methods may be the best way to identify genomic regions that harbor SNP variants that cause disease.

Algorithms↗

PubMed2005

The Rat Genome Database (RGD): developments towards a phenome database.

The Rat Genome Database (RGD) (http://rgd.mcw.edu) aims to meet the needs of its community by providing genetic and genomic infrastructure while also annotating the strengths of rat research: biochemistry, nutrition, pharmacology and physiology. Here, we report on RGD's development towards creating a phenome database. Recent developments can be categorized into three groups. (i) Improved data collection and integration to match increased volume and biological scope of research. (ii) Knowledge representation augmented by the implementation of a new ontology and annotation system. (iii) The addition of quantitative trait loci data, from rat, mouse and human to our advanced comparative genomics tools, as well as the creation of new, and enhancement of existing, tools to enable users to efficiently browse and survey research data. The emphasis is on helping researchers find genes responsible for disease through the use of rat models. These improvements, combined with the genomic sequence of the rat, have led to a successful year at RGD with over two million page accesses that represent an over 4-fold increase in a year. Future plans call for increased annotation of biological information on the rat elucidated through its use as a model for human pathobiology. The continued development of toolsets will facilitate integration of these data into the context of rat genomic sequence, as well as allow comparisons of biological and genomic data with the human genomic sequence and of an increasing number of organisms.

Animals↗

PubMed2004

Cross-species global and subset gene expression profiling identifies genes involved in prostate cancer response to selenium.

BACKGROUND: Gene expression technologies have the ability to generate vast amounts of data, yet there often resides only limited resources for subsequent validation studies. This necessitates the ability to perform sorting and prioritization of the output data. Previously described methodologies have used functional pathways or transcriptional regulatory grouping to sort genes for further study. In this paper we demonstrate a comparative genomics based method to leverage data from animal models to prioritize genes for validation. This approach allows one to develop a disease-based focus for the prioritization of gene data, a process that is essential for systems that lack significant functional pathway data yet have defined animal models. This method is made possible through the use of highly controlled spotted cDNA slide production and the use of comparative bioinformatics databases without the use of cross-species slide hybridizations. RESULTS: Using gene expression profiling we have demonstrated a similar whole transcriptome gene expression patterns in prostate cancer cells from human and rat prostate cancer cell lines both at baseline expression levels and after treatment with physiologic concentrations of the proposed chemopreventive agent Selenium. Using both the human PC3 and rat PAII prostate cancer cell lines have gone on to identify a subset of one hundred and fifty-four genes that demonstrate a similar level of differential expression to Selenium treatment in both species. Further analysis and data mining for two genes, the Insulin like Growth Factor Binding protein 3, and Retinoic X Receptor alpha, demonstrates an association with prostate cancer, functional pathway links, and protein-protein interactions that make these genes prime candidates for explaining the mechanism of Selenium's chemopreventive effect in prostate cancer. These genes are subsequently validated by western blots showing Selenium based induction and using tissue microarrays to demonstrate a significant association between downregulated protein expression and tumorigenesis, a process that is the reverse of what is seen in the presence of Selenium. CONCLUSIONS: Thus the outlined process demonstrates similar baseline and selenium induced gene expression profiles between rat and human prostate cancers, and provides a method for identifying testable functional pathways for the action of Selenium's chemopreventive properties in prostate cancer.

Adenocarcinoma↗