RNA-Seq-quantitative measurement of expression through massively parallel RNA-sequencing.

By B. Wilhelm, J. Landry. Methods, 2009

533 citations recorded by the source corpus.

How researchers see this paper

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

A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae (2012, background): “Other advantages of RNA-seq compared with microarrays are its high resolution, better dynamic range of detection and lower technical variation (3).”
Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data (2013, background): “In the recent years, RNA-Seq has become a very competitive alternative to the microarrays (see for instance [17, 18, 25, 33, 34]).”
Next-generation transcriptome assembly (2011, background): “Recently, however, whole-transcriptome sequencing using next-generation sequencing (NGS) technologies, or RNA sequencing (RNA-seq), has started to reveal the complex landscape and dynamics of the transcriptome from yeast to human at an unprecedented level of sensitivity and accurac”
Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community (2019, background): “They are potentially obtainable via transcriptomics [146] and meta-genomics [147].”
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.”