Biomedical subjects

Patricio Yankilevich

Publications and source records attributed to Patricio Yankilevich.

3 recordsLinked to original sources

PubMed2007

IDconverter and IDClight: conversion and annotation of gene and protein IDs.

BACKGROUND: Researchers involved in the annotation of large numbers of gene, clone or protein identifiers are usually required to perform a one-by-one conversion for each identifier. When the field of research is one such as microarray experiments, this number may be around 30,000. RESULTS: To help researchers map accession numbers and identifiers among clones, genes, proteins and chromosomal positions, we have designed and developed IDconverter and IDClight. They are two user-friendly, freely available web server applications that also provide additional functional information by mapping the identifiers on to pathways, Gene Ontology terms, and literature references. Both tools are high-throughput oriented and include identifiers for the most common genomic databases. These tools have been compared to other similar tools, showing that they are among the fastest and the most up-to-date. CONCLUSION: These tools provide a fast and intuitive way of enriching the information coming out of high-throughput experiments like microarrays. They can be valuable both to wet-lab researchers and to bioinformaticians.

Algorithms↗

PubMed2005

Evaluating HapMap SNP data transferability in a large-scale genotyping project involving 175 cancer-associated genes.

One of the many potential uses of the HapMap project is its application to the investigation of complex disease aetiology among a wide range of populations. This study aims to assess the transferability of HapMap SNP data to the Spanish population in the context of cancer research. We have carried out a genotyping study in Spanish subjects involving 175 candidate cancer genes using an indirect gene-based approach and compared results with those for HapMap CEU subjects. Allele frequencies were very consistent between the two samples, with a high positive correlation (R) of 0.91 (P<<1x10(-6)). Linkage disequilibrium patterns and block structures across each gene were also very similar, with disequilibrium coefficient (r (2)) highly correlated (R=0.95, P<<1x10(-6)). We found that of the 21 genes that contained at least one block larger than 60 kb, nine (ATM, ATR, BRCA1, ERCC6, FANCC, RAD17, RAD50, RAD54B and XRCC4) belonged to the GO category "DNA repair". Haplotype frequencies per gene were also highly correlated (mean R=0.93), as was haplotype diversity (R=0.91, P<<1x10(-6)). "Yin yang" haplotypes were observed for 43% of the genes analysed and 18% of those were identical to the ancestral haplotype (identified in Chimpazee). Finally, the portability of tagSNPs identified in the HapMap CEU data using pairwise r (2) thresholds of 0.8 and 0.5 was assessed by applying these to the Spanish and current HapMap data for 66 genes. In general, the HapMap tagSNPs performed very well. Our results show generally high concordance with HapMap data in allele frequencies and haplotype distributions and confirm the applicability of HapMap SNP data to the study of complex diseases among the Spanish population.

Biomarkers, Tumor↗

PubMed2005

GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data.

The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With >76,000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in CGH-arrays which fostered the development of the corresponding tool in GEPAS: InSilicoCGH. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.

Algorithms↗