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Journal ArticleDOI

RNA sequencing: the teenage years

TLDR
Advances in RNA-sequencing technologies and methods over the past decade are discussed and adaptations that are enabling a fuller understanding of RNA biology are outlined, from when and where an RNA is expressed to the structures it adopts.
Abstract
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.

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Citations
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Journal ArticleDOI

mRNAs, proteins and the emerging principles of gene expression control.

TL;DR: How large-scale comparative studies are characterizing the degree to which mRNA and protein levels correlate is discussed, and how transcriptomics and proteomics provide useful non-redundant readouts of gene expression is described.
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RNA sequencing: new technologies and applications in cancer research

TL;DR: The latest studies on RNA sequencing technology and their applications in cancer are summarized, and future challenges and opportunities forRNA sequencing technology in cancer applications are discussed.
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Noncoding RNAs in Extracellular Fluids as Cancer Biomarkers: The New Frontier of Liquid Biopsies.

TL;DR: An overview of the most common noncoding RNA species detectable in extracellular fluids and an update concerning the situation of the research on these molecules as cancer biomarkers are provided.
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Spatial transcriptomics of dorsal root ganglia identifies molecular signatures of human nociceptors

TL;DR: In this article , the authors used spatial transcriptomics to molecularly characterize transcriptomes of single dorsal root ganglia (DRG) neurons from eight organ donors, identifying 12 clusters of human sensory neurons, 5 of which are C nociceptors, as well as 1 C low-threshold mechanoreceptors (LTMRs), 1 Aβ nocicusor, 2 Aδ,2 Aβ, and 1 proprioceptor subtypes.
References
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Journal ArticleDOI

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
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STAR: ultrafast universal RNA-seq aligner

TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
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limma powers differential expression analyses for RNA-sequencing and microarray studies

TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Journal ArticleDOI

HTSeq—a Python framework to work with high-throughput sequencing data

TL;DR: This work presents HTSeq, a Python library to facilitate the rapid development of custom scripts for high-throughput sequencing data analysis, and presents htseq-count, a tool developed with HTSequ that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes.
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