Comprehensive comparative analysis of strand-specific RNA sequencing methods

By J. Levin, Moran Yassour, Xian Adiconis, Chad Nusbaum, Dawn A Thompson, Nir Friedman, Andreas Gnirke, Aviv Regev. Nature Methods, 2010

781 citations recorded by the source corpus.

Strand-specific, massively parallel cDNA sequencing (RNA-seq) is a powerful tool for transcript discovery, genome annotation and expression profiling. There are multiple published methods for strand-specific RNA-seq, but no consensus exists as to how to choose between them. Here we developed a comprehensive computational pipeline to compare library quality metrics from any RNA-seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library-construction protocols, including both published and our own methods. We found marked differences in strand specificity, library complexity, evenness and continuity of coverage, agreement with known annotations and accuracy for expression profiling. Weighing each method's performance and ease, we identified the dUTP second-strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the current availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in other organisms.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c0c7b42e450ac9288471101dd5523e5495ac8425

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis (2013, methodology): “…methods have revolutionized studies of the transcriptome by enabling a wide range of novel applications, including detection of alternative splicing isoforms [3, 4], genome-guided [5, 6] or denovo assembly of transcripts [7–9], transcript fusion detection [10] or strand-specific expression [11].”
A survey of best practices for RNA-seq data analysis (2016, methodology): “Several strand-specific protocols [2], such as the widely used dUTP method, extend the original protocol by incorporating UTP nucleotides during the second cDNA synthesis step, prior to adapter ligation followed by digestion of the strand containing dUTP [3].”
A protocol for RNA methylation differential analysis with MeRIP-Seq data and exomePeak R/Bioconductor package. (2014, background): “Noted that, different from MeRIP-Seq, the current standard RIP-Seq protocol takes advantages of strand-specific sequencing technique [17], so the reads from a transcript are mapped to ‘+’ or ‘−’ strand only, and the bimodal pattern is not observable [18].”
Single-cell RNA-seq: advances and future challenges (2014, positive): “For bulk RNAseq experiments, fragmentation of the RNA (120) prior to adapter ligation can rescue information about strand orientation.”
Developmental regulation of human cortex transcription and its clinical relevance at base resolution (2014, positive): “We performed RNA sequencing (RNA-seq) on 36 DLPFC samples from across the lifespan, and implemented the derfinder method to identify “differentially expressed regions” (DERs).”
Computational methods for transcriptome annotation and quantification using RNA-seq (2011, background): “For example, although the variability of the sequencing process is low compared to microarray hybridization 19 , measurements can vary substantially because of differences in library construction protocols 78 and most importantly because of intrinsic variability in biological samples.”
Systematic identification of long noncoding RNAs expressed during zebrafish embryogenesis. (2012, methodology): “Polyadenylated RNA was purified from approximately 1000 embryos per time-point and converted into cDNA libraries for strand-specific, paired-end 76 bp sequencing on Illumina’s HiSeq platform (see Methods; Parkhomchuk et al. 2009; Levin et al. 2010).”
Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells (2011, methodology): “Leveraging the Nanostring nCounter technology for accurate multiplex measurement of RNA 27 and massively parallel sequencing 28 , we significantly reduce metabolic labeling time to directly measure RNA transcription rates at high temporal resolution for a selected set of signature genes, and at a…”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, methodology): “Patterns associated with coverage are also important [21, 24] and can be evaluated, for example, by considering the evenness of coverage as represented by the mean coefficient of variation across transcripts, 5 ′ /3 ′ coverage as assessed by calculating the average coverage at each percentile of…”