Copy number variation detection using next generation sequencing read counts

BMC Bioinformatics

Volumn 15, Issue 1, 2014, Pages

  Wang, Heng ^a   Nettleton, Dan ^b   Ying, Kai ^c  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b IOWA STATE UNIVERSITY   (United States)

^c NATIONAL HUMAN GENOME RESEARCH INSTITUTE   (United States)

Author keywords

Count data; Gamma Poisson mixture; Hidden Markov model; Plant genomics; Poisson mixture model

Indexed keywords

ALGORITHMS; DNA SEQUENCES; GENES; HIDDEN MARKOV MODELS; PROBABILITY DISTRIBUTIONS;

COPY NUMBER VARIATIONS; COUNT DATUM; EMISSION PROBABILITIES; EXPECTATION-MAXIMIZATION ALGORITHMS; NEXT-GENERATION SEQUENCING; PHENOTYPIC DIFFERENCES; PLANT GENOMICS; POISSON MIXTURES;

MIXTURES;

ZEA MAYS;

ALGORITHM; ARTICLE; COPY NUMBER VARIATION; DNA SEQUENCE; GENETICS; GENOMICS; HIGH THROUGHPUT SEQUENCING; HUMAN; MAIZE; METHODOLOGY; PLANT CHROMOSOME; PLANT GENOME; PROBABILITY;

ALGORITHMS; CHROMOSOMES, PLANT; DNA COPY NUMBER VARIATIONS; GENOME, PLANT; GENOMICS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MARKOV CHAINS; SEQUENCE ANALYSIS, DNA; ZEA MAYS;

EID: 84899479560     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-15-109     Document Type: Article

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