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RNA sequencing

RNA sequencing

RNA sequencing techniques are used to determine the sequence of nucleotide bases, adenine (A), cytosine (C), guanine (G) and uracil (U) in RNA molecules. Uses the capabilities of high-throughput sequencing methods to provide insight into the transcriptome of a cell.

Usually RNA is first converted into cDNA (complementary DNA) with a reverse transcriptase enzyme, and then a second strand synthesis reaction is performed so DNA sequencing techniques can be applied to double stranded DNA copies of RNA transcripts. Since this method can lose information from the 5' and 3' ends of the transcript, other methods have been developed that omit the second strand synthesis reaction and ligate adapters to the cDNA for sequencing reactions. Sequencing adapters are consistent sequences that, when applied to flank the cDNA fragments, produce a cDNA library.

RNA sequencing is replacing gene expression arrays to analyze the spectrum and abundance of transcripts in a given cell or tissue type at a given time. The technique called RNA-Seq, also known as whole transcriptome shotgun sequencing, generates cDNA and uses it in next-generation sequencing.

UK-based Oxford Nanopore Technologies devised a system to directly sequence RNA with a device called the MinION, in which electrical current is applied across a nanoscale molecular pore and current fluctuations detect the RNA sequence as the RNA molecule snakes through the pore. This RNA sequencing device was used by NASA on the International Space Station because NASA is interested in using it to identify onboard microorganisms and to monitor changes in human health or microbiomes; NASA is also interested in the possibility of detecting life based on DNA or RNA elsewhere in the universe.

RNA-Seq delivers an unbiased and unprecedented high-resolution view of the global transcriptional landscape, which allows an affordable and accurate approach for gene expression quantification and differential gene expression analysis between multiple groups of samples. RNA-Seq can identify novel and previously unexpected transcripts without the need for a reference genome, allowing de novo assembly of new transcriptome that is not previously studied before. It also enables the discovery of novel gene structures, alternatively spliced isoforms, gene fusions, SNPs/InDel, and allele-specific expression (ASE).RNA-Seq is a sensitive tool for gene expression profiling. Compared to microarray, RNA-Seq offers a digital read that is more accurate for all gene expression.


Over the past decade, RNA sequencing (RNA-seq) has become an indispensable tool for transcriptome-wide analysis of differential gene expression and differential splicing of mRNAs. However, as next-generation sequencing technologies have developed, so too has RNA-seq. Now, RNA-seq methods are available for studying many different aspects of RNA biology, including single-cell gene expression, translation (the translatome) and RNA structure (the structurome). Exciting new applications are being explored, such as spatial transcriptomics (spatialomics). Together with new long-read and direct RNA-seq technologies and better computational tools for data analysis, innovations in RNA-seq are contributing to a fuller understanding of RNA biology, from questions such as when and where transcription occurs to the folding and intermolecular interactions that govern RNA function.


Further Resources


A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications

Ashraful Haque, Jessica Engel, Sarah A. Teichmann, Tapio Lönnberg


August 18, 2017

RNA Sequencing Part I: Introduction to Illumina's RNA library preparation workflows


November 6, 2021

RNA sequencing: the teenage years - Nature Reviews Genetics


July 24, 2019


Mark Terry
May 14, 2021
Every week there are numerous scientific studies published. Here's a look at some of the more interesting ones.
Ricard Argelaguet
May 3, 2021
Nature Biotechnology
The development of single-cell multimodal assays provides a powerful tool for investigating multiple dimensions of cellular heterogeneity, enabling new insights into development, tissue homeostasis and disease. A key challenge in the analysis of single-cell multimodal data is to devise appropriate strategies for tying together data across different modalities. The term 'data integration' has been used to describe this task, encompassing a broad collection of approaches ranging from batch correction of individual omics datasets to association of chromatin accessibility and genetic variation with transcription. Although existing integration strategies exploit similar mathematical ideas, they typically have distinct goals and rely on different principles and assumptions. Consequently, new definitions and concepts are needed to contextualize existing methods and to enable development of new methods. As the number of single-cell experiments with multiple data modalities increases, Argelaguet and colleagues review the concepts and challenges of data integration.
Science X staff
May 3, 2021
Researchers at the University of Haifa, the Weizmann Institute and the Center for Genomic Regulation (CRG) have built the first atlas of all of the different types of cells in Stylophora pistillata, a reef-building stony coral native to the Indo-Pacific oceans. Published today in the journal Cell, the study is the first to detect the presence of specialized immune cells in corals.
Akira Cortal
April 29, 2021
Nature Biotechnology
Because of the stochasticity associated with high-throughput single-cell sequencing, current methods for exploring cell-type diversity rely on clustering-based computational approaches in which heterogeneity is characterized at cell subpopulation rather than at full single-cell resolution. Here we present Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. We applied Cell-ID to data from multiple human and mouse samples, including blood cells, pancreatic islets and airway, intestinal and olfactory epithelium, as well as to comprehensive mouse cell atlas datasets. We demonstrate that Cell-ID signatures are reproducible across different donors, tissues of origin, species and single-cell omics technologies, and can be used for automatic cell-type annotation and cell matching across datasets. Cell-ID improves biological interpretation at individual cell level, enabling discovery of previously uncharacterized rare cell types or cell states. Cell-ID is distributed as an open-source R software package. Cell-ID facilitates the analysis of cell-type heterogeneity and cell identity across multiple samples at the single-cell level.
Jérémie Breda
April 29, 2021
Nature Biotechnology
Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods, there is still little agreement on how to best normalize such data. Starting from the basic requirements that inferred expression states should correct for both biological and measurement sampling noise and that changes in expression should be measured in terms of fold changes, we here derive a Bayesian normalization procedure called Sanity (SAmpling-Noise-corrected Inference of Transcription activitY) from first principles. Sanity estimates expression values and associated error bars directly from raw unique molecular identifier (UMI) counts without any tunable parameters. Using simulated and real scRNA-seq datasets, we show that Sanity outperforms other normalization methods on downstream tasks, such as finding nearest-neighbor cells and clustering cells into subtypes. Moreover, we show that by systematically overestimating the expression variability of genes with low expression and by introducing spurious correlations through mapping the data to a lower-dimensional representation, other methods yield severely distorted pictures of the data. A Bayesian procedure overcomes challenges in single-cell RNA-seq data normalization.


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