Detection of DNA copy number alterations using penalized least squares regression.

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TítuloDetection of DNA copy number alterations using penalized least squares regression.
Publication TypeJournal Article
Year of Publication2005
AutoresHuang, T, Wu, B, Lizardi, P, Zhao, H
JournalBioinformatics
Volume21
Issue20
Pagination3811-7
Date Published2005 Oct 15
ISSN1367-4803
Palabras claveAlgorithms, Base Sequence, Chromosome Mapping, Gene Dosage, Genetic Variation, Least-Squares Analysis, Models, Genetic, Models, Statistical, Molecular Sequence Data, Regression Analysis, Sequence Alignment, Sequence Analysis, DNA
Abstract

MOTIVATION: Genomic DNA copy number alterations are characteristic of many human diseases including cancer. Various techniques and platforms have been proposed to allow researchers to partition the whole genome into segments where copy numbers change between contiguous segments, and subsequently to quantify DNA copy number alterations. In this paper, we incorporate the spatial dependence of DNA copy number data into a regression model and formalize the detection of DNA copy number alterations as a penalized least squares regression problem. In addition, we use a stationary bootstrap approach to estimate the statistical significance and false discovery rate.

RESULTS: The proposed method is studied by simulations and illustrated by an application to an extensively analyzed dataset in the literature. The results show that the proposed method can correctly detect the numbers and locations of the true breakpoints while appropriately controlling the false positives.

AVAILABILITY: http://bioinformatics.med.yale.edu/DNACopyNumber

CONTACT: hongyu.zhao@yale.edu

SUPPLEMENTARY INFORMATION: http://bioinformatics.med.yale.edu/DNACopyNumber.

DOI10.1093/bioinformatics/bti646
Alternate JournalBioinformatics
PubMed ID16131523
Grant ListCA99135 / CA / NCI NIH HHS / United States
GM59507 / GM / NIGMS NIH HHS / United States