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@article{Young10,
author = {M. D. Young and M. J. Wakefield and G. K. Smyth and A. Oshlack},
journal = {Genome Biology},
title = {Gene ontology analysis for {RNA}-seq: accounting for selection bias},
number = {},
pages = {R14},
volume = {11},
year = {2010},
month = {Feb},
}

@article{Benjamini95,
author = {Y. Benjamini and Y. Hochberg}, 
journal = {Journal of the  Royal Statistical Society: Series B},
title = {Controlling the false discovery rate: a practical and powerful approach to multiple testing},
number = {},
pages = {289--300},
volume = {57},
year = {1995},
month = {},
date-added = {2009-05-19 23:56:23 +1000},
date-modified = {2009-07-03 11:54:22 +1000},
doi = {},
local-url = {f},
uri = {},
read = {Yes},
rating = {5}
}



@article{Robinson07,
author = {M. D. Robinson and G. K. Smyth}, 
journal = {Bioinformatics},
title = {Moderated statistical tests for assessing differences in tag abundance},
number = {21},
pages = {2881--2887},
volume = {23},
year = {2007},
month = {},
date-added = {2009-05-19 23:56:23 +1000},
date-modified = {2009-07-03 11:54:22 +1000},
doi = {},
local-url = {f},
uri = {},
read = {Yes},
rating = {5}
}

@article{Robinson08,
author = {M. D. Robinson and G. K. Smyth}, 
journal = {Biostatistics},
title = {Small-sample estimation of negative binomial dispersion, with applications to SAGE data},
number = {2},
pages = {321--332},
volume = {9},
year = {2008},
month = {},
date-added = {2009-05-19 23:56:23 +1000},
date-modified = {2009-07-03 11:54:22 +1000},
doi = {},
local-url = {f},
uri = {},
read = {Yes},
rating = {5}
}

@article{Robinson10,
author = {M. D. Robinson and D. J. McCarthy and G. K. Smyth}, 
journal = {Bioinformatics},
title = {edge{R}: a Bioconductor package for differential expression analysis of digital gene expression data},
abstract = {SUMMARY: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. AVAILABILITY: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).},
affiliation = {Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia. mrobinson@wehi.edu.au},
number = {1},
pages = {139--40},
volume = {26},
year = {2010},
month = {Jan},
language = {eng},
date-added = {2010-02-02 14:01:09 +1100},
date-modified = {2010-02-02 14:01:09 +1100},
doi = {10.1093/bioinformatics/btp616},
pii = {btp616},
pmid = {19910308},
uri = {papers://C20AA413-5252-4F2C-BFAB-68084A7E2228/Paper/p567},
rating = {0}
}


@article{Li:2008p113,
author = {Hairi Li and Michael T Lovci and Young-Soo Kwon and Michael G Rosenfeld and Xiang-Dong Fu and Gene W Yeo}, 
journal = {Proc Natl Acad Sci USA},
title = {Determination of tag density required for digital transcriptome analysis: application to an androgen-sensitive prostate cancer model},
abstract = {High-throughput sequencing has rapidly gained popularity for transcriptome analysis in mammalian cells because of its ability to generate digital and quantitative information on annotated genes and to detect transcripts and mRNA isoforms. Here, we described a double-random priming method for deep sequencing to profile double poly(A)-selected RNA from LNCaP cells before and after androgen stimulation. From approximately 20 million sequence tags, we uncovered 71% of annotated genes and identified hormone-regulated gene expression events that are highly correlated with quantitative real time PCR measurement. A fraction of the sequence tags were mapped to constitutive and alternative splicing events to detect known and new mRNA isoforms expressed in the cell. Finally, curve fitting was used to estimate the number of tags necessary to reach a "saturating" discovery rate among individual applications. This study provides a general guide for analysis of gene expression and alternative splicing by deep sequencing.},
affiliation = {Department of Cellular and Molecular Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0651, USA.},
number = {51},
pages = {20179--84},
volume = {105},
year = {2008},
month = {Dec},
language = {eng},
keywords = {Automation, Male, Alternative Splicing, Expressed Sequence Tags, Polymerase Chain Reaction, Cell Line: Tumor, Prostatic Neoplasms, Gene Expression Profiling, Animals, RNA: Messenger, Androgens}, 
date-added = {2009-11-26 12:49:28 +1100},
date-modified = {2009-11-26 12:49:28 +1100},
doi = {10.1073/pnas.0807121105},
pii = {0807121105},
pmid = {19088194},
uri = {papers://C20AA413-5252-4F2C-BFAB-68084A7E2228/Paper/p113},
read = {Yes},
rating = {0}
}

@article{Oshlack:2009p149,
author = {Alicia Oshlack and Matthew J Wakefield}, 
journal = {Biol Direct},
title = {Transcript length bias in {RNA}-seq data confounds systems biology},
abstract = {BACKGROUND: Several recent studies have demonstrated the effectiveness of deep sequencing for transcriptome analysis (RNA-seq) in mammals. As RNA-seq becomes more affordable, whole genome transcriptional profiling is likely to become the platform of choice for species with good genomic sequences. As yet, a rigorous analysis methodology has not been developed and we are still in the stages of exploring the features of the data. RESULTS: We investigated the effect of transcript length bias in RNA-seq data using three different published data sets. For standard analyses using aggregated tag counts for each gene, the ability to call differentially expressed genes between samples is strongly associated with the length of the transcript. CONCLUSION: Transcript length bias for calling differentially expressed genes is a general feature of current protocols for RNA-seq technology. This has implications for the ranking of differentially expressed genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses. REVIEWERS: This article was reviewed by Rohan Williams (nominated by Gavin Huttley), Nicole Cloonan (nominated by Mark Ragan) and James Bullard (nominated by Sandrine Dudoit).},
affiliation = {Walter and Eliza Hall Institute of Medical Research, Parkville, Vic, Australia. oshlack@wehi.edu.au},
pages = {14},
volume = {4},
year = {2009},
month = {Jan},
language = {eng},
keywords = {Transcription: Genetic, Base Sequence, Humans, Gene Expression Profiling, Oligonucleotide Array Sequence Analysis, Bias (Epidemiology), Systems Biology, RNA: Messenger}, 
date-added = {2009-11-26 13:00:07 +1100},
date-modified = {2009-11-26 13:00:07 +1100},
doi = {10.1186/1745-6150-4-14},
pii = {1745-6150-4-14},
pmid = {19371405},
uri = {papers://C20AA413-5252-4F2C-BFAB-68084A7E2228/Paper/p149},
read = {Yes},
rating = {0}
}

