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

Abstract

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.

Blood transcriptome changes after stroke in an African American population

“…Because we had a clinically homogeneous cohort of hemi‐paretic patients, we further focused our analysis on stroke patients with a confirmed MCA territory infarct. Analysis of gene expression was performed on read data (normalized to RPKM values18, 20). We define a gene as consisting of multiple exons that define differential splicing and isoforms of the gene.…”

“…Technical replicates are typically not required for next‐generation sequencing (NGS) experiments 18. To control for potential technical issues, we prepared samples in batches of eight enabling color balancing of the barcoded primers and sequencing at ~50–100 million reads/sample.…”

A eole for the MS analysis of nucleic acids in the post-genomics age

“…Analogous to approaches employed in quantitative proteomics, 18 O end-labeling by nuclease digestion can be employed to evaluate RNA production and observe possible variations in the amount of posttranscriptionally modified nucleotides [83]. The development of these types of approaches will be expected to benefit the growing field of experimental RNomics by complementing current technologies based on microarray techniques and cDNA-based "deep sequencing," which can provide comprehensive views of entire cellular transcriptomes [84,85]. In fact, the dependence of established technologies on amplification/sequencing and electrophoretic analysis makes them ill-suited for the detection of possible post-transcriptional modifications, which is likely the cause of our insufficient understanding of the extent and biological significance of ncRNA modifications.…”

Improved moderation for gene-wise variance estimation in RNA-Seq via the exploitation of external information

“…For ease of presentation we will assume that all effects that are generally normalised for or modelled, such as library sizes and GC content, remain constant across samples. The technical variability for a gene count in RNA-Seq can be modelled quite reliably as Poisson [10,11]. This is attractive in situations of low replication as one parameter can be estimated to describe both the mean and variance of a gene.…”