The integrative analysis of DNA copy number levels and transcriptional profiles, in context of the physical location of genes in a genome, still represents a challenge in the bioinformatics arena. A computational framework based on locally adaptive statistical procedures (Locally Adaptive Statistical Procedure, LAP and Global Smoothing Copy Number, GLSCN) for the identification of imbalanced chromosomal regions in single samples is described. The application of LAP and GLSCN to the integrative analysis of clear cell renal carcinoma patients allowed identifying chromosomal regions that are directly involved in known chromosomal aberrations characteristic of tumors.
A computational procedure for the integrative analysis of genomic data at the single sample level / M. Zampieri, I. Cifola, D. Basso, R. Spinelli, L. Beltrame, C. Peano, C. Battaglia, S. Bicciato. ((Intervento presentato al 10. convegno Symposium on Computer Applications in Biotechnology tenutosi a Cancun (Mex) nel 2007.
A computational procedure for the integrative analysis of genomic data at the single sample level
I. CifolaSecondo
;L. Beltrame;C. BattagliaPenultimo
;
2007
Abstract
The integrative analysis of DNA copy number levels and transcriptional profiles, in context of the physical location of genes in a genome, still represents a challenge in the bioinformatics arena. A computational framework based on locally adaptive statistical procedures (Locally Adaptive Statistical Procedure, LAP and Global Smoothing Copy Number, GLSCN) for the identification of imbalanced chromosomal regions in single samples is described. The application of LAP and GLSCN to the integrative analysis of clear cell renal carcinoma patients allowed identifying chromosomal regions that are directly involved in known chromosomal aberrations characteristic of tumors.Pubblicazioni consigliate
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