Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates
Published 2017 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Mixture models reveal multiple positional bias types in RNA-Seq data and lead to accurate transcript concentration estimates
Authors
Keywords
RNA sequencing, Microarrays, Probability distribution, Quality control, RNA hybridization, Statistical models, Next-generation sequencing, Transcriptome analysis
Journal
PLoS Computational Biology
Volume 13, Issue 5, Pages e1005515
Publisher
Public Library of Science (PLoS)
Online
2017-05-16
DOI
10.1371/journal.pcbi.1005515
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium
- (2014) NATURE BIOTECHNOLOGY
- DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions
- (2013) Günter Klambauer et al. NUCLEIC ACIDS RESEARCH
- PennSeq: accurate isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution
- (2013) Yu Hu et al. NUCLEIC ACIDS RESEARCH
- Human housekeeping genes, revisited
- (2013) Eli Eisenberg et al. TRENDS IN GENETICS
- Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads
- (2012) Wei Li et al. BIOINFORMATICS
- Modeling RNA degradation for RNA-Seq with applications
- (2012) L. Wan et al. BIOSTATISTICS
- A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data
- (2012) H. Wu et al. BIOSTATISTICS
- Streaming fragment assignment for real-time analysis of sequencing experiments
- (2012) Adam Roberts et al. NATURE METHODS
- Modelling and simulating generic RNA-Seq experiments with the flux simulator
- (2012) Thasso Griebel et al. NUCLEIC ACIDS RESEARCH
- Normalization, testing, and false discovery rate estimation for RNA-sequencing data
- (2011) J. Li et al. BIOSTATISTICS
- RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
- (2011) Bo Li et al. BMC BIOINFORMATICS
- Differential expression in RNA-seq: A matter of depth
- (2011) S. Tarazona et al. GENOME RESEARCH
- Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data
- (2011) Jun Li et al. STATISTICAL METHODS IN MEDICAL RESEARCH
- Improving RNA-Seq expression estimates by correcting for fragment bias
- (2011) Adam Roberts et al. GENOME BIOLOGY
- Using non-uniform read distribution models to improve isoform expression inference in RNA-Seq
- (2010) Zhengpeng Wu et al. BIOINFORMATICS
- Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
- (2010) James H Bullard et al. BMC BIOINFORMATICS
- baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
- (2010) Thomas J Hardcastle et al. BMC BIOINFORMATICS
- Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
- (2010) Cole Trapnell et al. NATURE BIOTECHNOLOGY
- Biases in Illumina transcriptome sequencing caused by random hexamer priming
- (2010) Kasper D. Hansen et al. NUCLEIC ACIDS RESEARCH
- Modeling non-uniformity in short-read rates in RNA-Seq data
- (2010) Jun Li et al. GENOME BIOLOGY
- Differential expression analysis for sequence count data
- (2010) Simon Anders et al. GENOME BIOLOGY
- RNA-Seq gene expression estimation with read mapping uncertainty
- (2009) Bo Li et al. BIOINFORMATICS
- DEGseq: an R package for identifying differentially expressed genes from RNA-seq data
- (2009) Likun Wang et al. BIOINFORMATICS
- TopHat: discovering splice junctions with RNA-Seq
- (2009) Cole Trapnell et al. BIOINFORMATICS
- The Sequence Alignment/Map format and SAMtools
- (2009) H. Li et al. BIOINFORMATICS
- edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
- (2009) M. D. Robinson et al. BIOINFORMATICS
- Mapping and quantifying mammalian transcriptomes by RNA-Seq
- (2008) Ali Mortazavi et al. NATURE METHODS
- RNA-Seq: a revolutionary tool for transcriptomics
- (2008) Zhong Wang et al. NATURE REVIEWS GENETICS
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started