Comparing segmentation methods for genome annotation based on RNA-seq data

DOI10.1007/s13253-013-0159-5zbMath1303.62064OpenAlexW2030883275MaRDI QIDQ486163

Alice Cleynen, Sandrine Dudoit, Stephane Robin

Publication date: 14 January 2015

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-013-0159-5

zbMATH Keywords

confidence intervalscount datanegative binomial distributionsegmentationRNA-seqchange-point detectiongenome annotation

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Related Items

Nonparametric multiple change-point estimation for analyzing large Hi-C data matrices, New efficient algorithms for multiple change-point detection with reproducing kernels, A breakpoint detection in the mean model with heterogeneous variance on fixed time intervals, Consistency of a range of penalised cost approaches for detecting multiple changepoints

Uses Software

• R
• Bioconductor
• CRAN
• edgeR
• changepoint
• EDAseq
• SRA

Cites Work

• Unnamed Item
• Segmentation of the mean of heteroscedastic data via cross-validation
• Exact posterior distributions and model selection criteria for multiple change-point detection problems
• Fast estimation of posterior probabilities in change-point analysis through a constrained hidden Markov model
• Partition Regression
• A Bayesian Analysis for Change Point Problems
• A Cluster Analysis Method for Grouping Means in the Analysis of Variance
• Segmentation of the Poisson and negative binomial rate models: a penalized estimator
• Denoising array-based comparative genomic hybridization data using wavelets