RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays.

By John C. Marioni, Christopher E. Mason, Shrikant M. Mane, Matthew Stephens, Yoav Gilad. Genome Research, 2008

2,942 citations recorded by the source corpus.

Ultra-high-throughput sequencing is emerging as an attractive alternative to microarrays for genotyping, analysis of methylation patterns, and identification of transcription factor binding sites. Here, we describe an application of the Illumina sequencing (formerly Solexa sequencing) platform to study mRNA expression levels. Our goals were to estimate technical variance associated with Illumina sequencing in this context and to compare its ability to identify differentially expressed genes with existing array technologies. To do so, we estimated gene expression differences between liver and kidney RNA samples using multiple sequencing replicates, and compared the sequencing data to results obtained from Affymetrix arrays using the same RNA samples. We find that the Illumina sequencing data are highly replicable, with relatively little technical variation, and thus, for many purposes, it may suffice to sequence each mRNA sample only once (i.e., using one lane). The information in a single lane of Illumina sequencing data appears comparable to that in a single array in enabling identification of differentially expressed genes, while allowing for additional analyses such as detection of low-expressed genes, alternative splice variants, and novel transcripts. Based on our observations, we propose an empirical protocol and a statistical framework for the analysis of gene expression using ultra-high-throughput sequencing technology.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1a3555826ef68df33933414fb8a0724ad715e97d

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Citations in the corpus

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation (2012, methodology): “The Poisson model implies that the mean equals the variance, a relationship that has been validated in one of the early RNA-Seq studies using the same initial source of RNA distributed across multiple lanes of an Illumina GA se-quencer (30).”
Studying and modelling dynamic biological processes using time-series gene expression data (2012, result): “Finally, there are indications that sequencing-based methods are more replicable and lead to more-accurate results compared with microarray-based methods 26 .”
TopHat: discovering splice junctions with RNA-Seq (2009, neutral): “Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.”
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package (2015, background): “It has been claimed that RNA-seq has a number of advantages over its predecessors (arrays), such as a wider dynamic range of measurements (5), the capacity to detect transcripts with low expression level (3) and the ability to identify differences in isoform or allele expression (6,7).”
Statistical inferences for isoform expression in RNA-Seq (2009, background): “…analysis in several different species such as yeast (Nagalakshmi et al. , 2008; Wilhelm et al. , 2008), Arabidopsis (Lister et al. , 2008), mouse (Cloonan et al. , 2008; Mortazavi et al. , 2008) and human (Wang et al. , 2008; Pan et al. , 2008; Marioni et al. , 2008; Sultan et al. , 2008).”
Applications of next-generation sequencing to phylogeography and phylogenetics. (2013, methodology): “Sequencing the transcriptome itself (RNA-seq) can be viewed as another method of genomic reduction where the remaining subset of gDNA is not random, as with restriction-digest, but contains the set of expressed genes (Marioni et al., 2008; Morin et al., 2008; Wang et al., 2009).”
RNA Sequencing and Analysis (2015, positive): “Early RNA-Seq studies suggested that the distribution of read counts across replicates fit a Poisson distribution, which formed the basis for modeling RNA-Seq count data (Marioni et al. 2008).”
RNA-Seq gene expression estimation with read mapping uncertainty (2009, methodology): “2 Simulation procedure We first derived simulation parameters from the mouse liver RNA-Seq data described in Mortazavi et al. (2008). Nucleotide fractions (ν) were computed from this data with the rescue method (so as to reduce the bias toward our method) and the mouse UCSC Genes (Hsu et al.”
The technology and biology of single-cell RNA sequencing. (2015, methodology): “Subsequently, RNA sequencing (Cloonan et al., 2008; Marioni et al., 2008; Mortazavi et al., 2008; Nagalakshmi et al., 2008; Wilhelm et al., 2008) was quickly adapted for analyzing single cells.”
Gene Mapping via Bulked Segregant RNA-Seq (BSR-Seq) (2012, methodology): “One of the most widely adopted adaptations of NGS technology is RNA-Seq [21–27], which enables the comparative quantification of gene expression in, for example, various genotypes.”
Systems genetics approaches to understand complex traits (2013, result): “Comparisons of RNA-seq results with expression array results indicate fairly high concordance for transcript abundance 25,26 .”
An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex (2014, background): “Transcript abundance is directly proportional to the number of sequencing reads that map to a specific transcript, resulting in lower falsenegative and false-positive discovery rates in addition to a greater linear range (Bainbridge et al., 2006; Cloonan et al., 2008; Marioni et al., 2008; Mortazavi”