Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers
Published 2013 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers
Authors
Keywords
-
Journal
BIOINFORMATICS
Volume 29, Issue 11, Pages 1390-1398
Publisher
Oxford University Press (OUP)
Online
2013-04-05
DOI
10.1093/bioinformatics/btt152
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- iBBiG: iterative binary bi-clustering of gene sets
- (2012) Daniel Gusenleitner et al. BIOINFORMATICS
- The sva package for removing batch effects and other unwanted variation in high-throughput experiments
- (2012) Jeffrey T. Leek et al. BIOINFORMATICS
- Molecular mechanism underlying the regulatory specificity of a Drosophila homeodomain protein that specifies myoblast identity
- (2012) B. W. Busser et al. DEVELOPMENT
- Genetic and Epigenetic Determinants of Neurogenesis and Myogenesis
- (2012) Abraham P. Fong et al. DEVELOPMENTAL CELL
- Using a structural and logics systems approach to infer bHLH–DNA binding specificity determinants
- (2011) Federico De Masi et al. NUCLEIC ACIDS RESEARCH
- Curated collection of yeast transcription factor DNA binding specificity data reveals novel structural and gene regulatory insights
- (2011) Raluca Gordân et al. GENOME BIOLOGY
- Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo
- (2010) Gong-Hong Wei et al. EMBO JOURNAL
- Deciphering a transcriptional regulatory code: modeling short-range repression in the Drosophila embryo
- (2010) Walid D Fakhouri et al. Molecular Systems Biology
- Tackling the widespread and critical impact of batch effects in high-throughput data
- (2010) Jeffrey T. Leek et al. NATURE REVIEWS GENETICS
- UniPROBE, update 2011: expanded content and search tools in the online database of protein-binding microarray data on protein-DNA interactions
- (2010) K. Robasky et al. NUCLEIC ACIDS RESEARCH
- Identification and Genome-Wide Prediction of DNA Binding Specificities for the ApiAP2 Family of Regulators from the Malaria Parasite
- (2010) Tracey L. Campbell et al. PLoS Pathogens
- A Multiparameter Network Reveals Extensive Divergence between C. elegans bHLH Transcription Factors
- (2009) Christian A. Grove et al. CELL
- High-resolution DNA-binding specificity analysis of yeast transcription factors
- (2009) C. Zhu et al. GENOME RESEARCH
- Distinguishing direct versus indirect transcription factor-DNA interactions
- (2009) R. Gordan et al. GENOME RESEARCH
- Universal protein-binding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors
- (2009) Michael F Berger et al. Nature Protocols
- Diversity and Complexity in DNA Recognition by Transcription Factors
- (2009) G. Badis et al. SCIENCE
- DNA Specificity Determinants Associate with Distinct Transcription Factor Functions
- (2009) Peter C. Hollenhorst et al. PLoS Genetics
- Variation in Homeodomain DNA Binding Revealed by High-Resolution Analysis of Sequence Preferences
- (2008) Michael F. Berger et al. CELL
- Analysis of Homeodomain Specificities Allows the Family-wide Prediction of Preferred Recognition Sites
- (2008) Marcus B. Noyes et al. CELL
- Design of Compact, Universal DNA Microarrays for Protein Binding Microarray Experiments
- (2008) Anthony A. Philippakis et al. JOURNAL OF COMPUTATIONAL BIOLOGY
- A Library of Yeast Transcription Factor Motifs Reveals a Widespread Function for Rsc3 in Targeting Nucleosome Exclusion at Promoters
- (2008) Gwenael Badis et al. MOLECULAR CELL
- A general framework for multiple testing dependence
- (2008) J. T. Leek et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now