Statistical Design and Analysis of RNA Sequencing Data

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The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing

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Analysis of Variance for Gene Expression Microarray Data

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A Global View of Gene Activity and Alternative Splicing by Deep Sequencing of the Human Transcriptome

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Small-sample estimation of negative binomial dispersion, with applications to SAGE data

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Fundamentals of experimental design for cDNA microarrays

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Stem cell transcriptome profiling via massive-scale mRNA sequencing

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Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations

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Extending the Linear Model with R

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Moderated statistical tests for assessing differences in tag abundance

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Personalized copy number and segmental duplication maps using next-generation sequencing

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Experimental design for gene expression microarrays

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Applications of New Sequencing Technologies for Transcriptome Analysis

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Statistical design and the analysis of gene expression microarray data

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A first course in design and analysis of experiments

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The design of experiments

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Dynamics of Gene Expression Revealed by Comparison of Serial Analysis of Gene Expression Transcript Profiles from Yeast Grown on Two Different Carbon Sources

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The beginning of the end for microarrays?

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Identification of genetic variants using bar-coded multiplexed sequencing

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Detection of single nucleotide variations in expressed exons of the human genome using RNA-Seq

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Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarrays

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POWER_SAGE: comparing statistical tests for SAGE experiments

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Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data

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Statistical evaluation of SAGE libraries: consequences for experimental design

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Transcript Profiling by 3′-Untranslated Region Sequencing Resolves Expression of Gene Families

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Probabilistic base calling of Solexa sequencing data

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Identifying differential expression in multiple SAGE libraries: an overdispersed log-linear model approach

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Genome sequencing: the third generation

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Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

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Detecting differentially expressed genes in multiple tag sampling experiments: comparative evaluation of statistical tests

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Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates

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Basic properties and information theory of Audic-Claverie statistic for analyzing cDNA arrays

Peter Tiňo, B. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-310 · Cited by 49

RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data

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Nonlinear Regression, Quasi Likelihood, and Overdispersion in Generalized Linear Models

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Modeling Sage data with a truncated gamma-Poisson model

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Using reads to annotate the genome: influence of length, background distribution, and sequence errors on prediction capacity

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Britain hits a hurdle in replacing key animal-pathogen facility

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De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

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Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195

A survey of best practices for RNA-seq data analysis

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rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data

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Normalization and microbial differential abundance strategies depend upon data characteristics

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ISSUE HIGHLIGHTS

(2010) · Genetics · doi:10.1093/genetics/185.2.np · Cited by 1

Design of RNA Sequencing Experiments

Nettleton, D. S. (2014) · Springer eBooks · doi:10.1007/978-3-319-07212-8_5 · Cited by 6

Design and validation issues in RNA-seq experiments

Fang, Z., & Cui, X. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr004 · Cited by 243

Biological Perspectives of RNA-Sequencing Experimental Design

Pasmanik‐Chor, M. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1103-6_17 · Cited by 2

Modeling and Analysis of Next-Generation Sequencing Data

Lin, S., Scholtens, D. M., & Datta, S. (2022) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315153728-5