Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion

Proceedings of the National Academy of Sciences of the United States of America

Volumn 108, Issue 46, 2011, Pages

  Xi, Ruibin ^a   Hadjipanayis, Angela G ^b   Luquette, Lovelace J ^a   Kim, Tae Min ^a   Lee, Eunjung ^a,b   Zhang, Jianhua ^c   Johnson, Mark D ^b   Muzny, Donna M ^d   Wheeler, David A ^d   Gibbs, Richard A ^d   Kucherlapati, Raju ^b,e   Park, Peter J ^a,b,f  

^a Harvard Medical School Center for Biomedical Informatics   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c DANA FARBER CANCER INSTITUTE   (United States)

^d BAYLOR COLLEGE OF MEDICINE   (United States)

^e HARVARD MEDICAL SCHOOL   (United States)

^f BOSTON CHILDREN S HOSPITAL   (United States)

Author keywords

Genomic alterations; Model selection; Semiparametric model; Structural variation

Indexed keywords

DNA;

ALGORITHM; ARTICLE; BAYES THEOREM; CANCER GROWTH; COMPARATIVE GENOMIC HYBRIDIZATION; COPY NUMBER VARIATION; GENE SEQUENCE; GENE TECHNOLOGY; GENOME ANALYSIS; GENOMICS; GLIOBLASTOMA; HUMAN; MAJOR CLINICAL STUDY; PATHOGENESIS; POLYMERASE CHAIN REACTION; PRIORITY JOURNAL; TUMOR GENE;

ALGORITHMS; BAYES THEOREM; BRAIN NEOPLASMS; COMPARATIVE GENOMIC HYBRIDIZATION; COMPUTER SIMULATION; DNA COPY NUMBER VARIATIONS; FEMALE; GENE DOSAGE; GENOME; GENOME, HUMAN; GLIOBLASTOMA; HUMANS; MODELS, GENETIC; MODELS, STATISTICAL; SEQUENCE ANALYSIS, DNA;

EID: 81755172942     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1110574108     Document Type: Article

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