How to cite the Bioconductor package consensusSeekeR

Samb, R., Khadraoui, K., Belleau, P., Deschênes, A., Lakhal-Chaieb, L., & Droit, A. (2015). Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling. Statistical Applications in Genetics and Molecular Biology, 14(6). https://doi.org/10.1515/sagmb-2014-0098

1.

Samb R, Khadraoui K, Belleau P, Deschênes A, Lakhal-Chaieb L, Droit A. Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling. Stat Appl Genet Mol Biol [Internet]. 2015 Jan 1;14(6). Available from: http://dx.doi.org/10.1515/sagmb-2014-0098

@ARTICLE{Samb2015-sb,
  title     = "Using informative {Multinomial-Dirichlet} prior in a t-mixture
               with reversible jump estimation of nucleosome positions for
               genome-wide profiling",
  author    = "Samb, Rawane and Khadraoui, Khader and Belleau, Pascal and
               Desch{\^e}nes, Astrid and Lakhal-Chaieb, Lajmi and Droit, Arnaud",
  abstract  = "AbstractGenome-wide mapping of nucleosomes has revealed a great
               deal about the relationships between chromatin structure and
               control of gene expression. Recent next generation CHIP-chip and
               CHIP-Seq technologies have accelerated our understanding of
               basic principles of chromatin organization. These technologies
               have taught us that nucleosomes play a crucial role in gene
               regulation by allowing physical access to transcription factors.
               Recent methods and experimental advancements allow the
               determination of nucleosome positions for a given genome area.
               However, most of these methods estimate the number of
               nucleosomes either by an EM algorithm using a BIC criterion or
               an effective heuristic strategy. Here, we introduce a Bayesian
               method for identifying nucleosome positions. The proposed model
               is based on a Multinomial-Dirichlet classification and a
               hierarchical mixture distributions. The number and the positions
               of nucleosomes are estimated using a reversible jump Markov
               chain Monte Carlo simulation technique. We compare the
               performance of our method on simulated data and MNase-Seq data
               from Saccharomyces cerevisiae against PING and NOrMAL methods.",
  journal   = "Stat. Appl. Genet. Mol. Biol.",
  publisher = "Walter de Gruyter GmbH",
  volume    =  14,
  number    =  6,
  month     =  jan,
  year      =  2015,
  url       = "http://dx.doi.org/10.1515/sagmb-2014-0098",
  issn      = "2194-6302, 1544-6115",
  doi       = "10.1515/sagmb-2014-0098"
}

TY  - JOUR
AU  - Samb, Rawane
AU  - Khadraoui, Khader
AU  - Belleau, Pascal
AU  - Deschênes, Astrid
AU  - Lakhal-Chaieb, Lajmi
AU  - Droit, Arnaud
TI  - Using informative Multinomial-Dirichlet prior in a t-mixture with
      reversible jump estimation of nucleosome positions for genome-wide
      profiling
T2  - Stat. Appl. Genet. Mol. Biol.
VL  - 14
IS  - 6
PY  - 2015
DA  - 2015/1/1
PB  - Walter de Gruyter GmbH
AB  - AbstractGenome-wide mapping of nucleosomes has revealed a great deal about
      the relationships between chromatin structure and control of gene
      expression. Recent next generation CHIP-chip and CHIP-Seq technologies
      have accelerated our understanding of basic principles of chromatin
      organization. These technologies have taught us that nucleosomes play a
      crucial role in gene regulation by allowing physical access to
      transcription factors. Recent methods and experimental advancements allow
      the determination of nucleosome positions for a given genome area.
      However, most of these methods estimate the number of nucleosomes either
      by an EM algorithm using a BIC criterion or an effective heuristic
      strategy. Here, we introduce a Bayesian method for identifying nucleosome
      positions. The proposed model is based on a Multinomial-Dirichlet
      classification and a hierarchical mixture distributions. The number and
      the positions of nucleosomes are estimated using a reversible jump Markov
      chain Monte Carlo simulation technique. We compare the performance of our
      method on simulated data and MNase-Seq data from Saccharomyces cerevisiae
      against PING and NOrMAL methods.
SN  - 2194-6302
DO  - 10.1515/sagmb-2014-0098
UR  - http://dx.doi.org/10.1515/sagmb-2014-0098
ER  -