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临床与实验生物学研究中的转录组学:紧跟测序技术发展

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq.

作者信息

Stokes Tanner, Cen Haoning Howard, Kapranov Philipp, Gallagher Iain J, Pitsillides Andrew A, Volmar Claude-Henry, Kraus William E, Johnson James D, Phillips Stuart M, Wahlestedt Claes, Timmons James A

机构信息

Faculty of Science McMaster University Hamilton L8S 4L8 Canada.

Life Sciences Institute University of British Columbia Vancouver V6T 1Z3 Canada.

出版信息

Adv Genet (Hoboken). 2023 Jan 17;4(2):2200024. doi: 10.1002/ggn2.202200024. eCollection 2023 Jun.

DOI:10.1002/ggn2.202200024
PMID:37288167
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10242409/
Abstract

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

摘要

对人类基因组进行测序推动了转化医学的发展,促进了全转录组范围的分子诊断、通路生物学研究和药物重新定位。最初,微阵列用于研究整体转录组;但现在短读长RNA测序(RNA-seq)占主导地位。作为一种更优越的技术,RNA-seq使新转录本的发现成为常规操作,然而实际上大多数RNA-seq分析都是基于已知转录组进行建模的。随着微阵列设计和应用的分析策略日益成熟,RNA-seq方法的局限性逐渐显现。本文对这两种技术进行了公平比较,突出了现代微阵列相对于RNA-seq的优势。微阵列方案能更准确地量化跨组织重复样本中组成性表达的蛋白质编码基因,并且在研究低表达基因时更可靠。微阵列显示长链非编码RNA(lncRNA)的表达既不稀疏,也不低于蛋白质编码基因。RNA-seq对组成性表达基因的覆盖不均一,这削弱了通路分析的有效性和可重复性。本文讨论了导致这些现象的因素,其中许多因素与长读长或单细胞测序相关。正如本文所提议的,需要重新认识整体转录组学方法,包括更广泛地使用现代高密度阵列数据——以迫切修订现有的解剖学RNA参考图谱,并有助于更准确地研究lncRNA。

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The specious art of single-cell genomics.单细胞基因组学的似是而非的艺术。
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