RNA内参在转录组测序质量控制中的应用

张瑜; 王霞; 张永卓; 杨佳怡; 牛春艳; 赵洋; 董莲华

doi:10.3969/j.issn.1000-1158.2023.05.22

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计量学报 ›› 2023, Vol. 44 ›› Issue (5) : 818-825. DOI: 10.3969/j.issn.1000-1158.2023.05.22

电离辐射、标准物质与生物计量

RNA内参在转录组测序质量控制中的应用

张瑜,王霞,张永卓,杨佳怡,牛春艳,赵洋,董莲华

作者信息 +

Applications of RNA Spike-in Quality Control for RNA Sequencing

ZHANG Yu,WANG Xia,ZHANG Yong-zhuo,YANG Jia-yi,NIU Chun-yan,ZHAO Yang,DONG Lian-hua

Author information +

文章历史 +

摘要

真核生物转录组多样且复杂,通过转录组测序可以获得基因表达的整体信息,发现与疾病相关的标志物,为复杂疾病的诊疗提供支持。转录组测序包括样品制备、建库、测序和数据分析等步骤,每个步骤中存在的误差都会影响转录组测序结果的准确性。利用RNA内参(RNA spike-in)对转录组测序进行质控,可以发现并减少这些误差,提升测序数据结果的可靠性。因此,对RNA spike-in进行介绍,并对其在转录组数据标准化、基因表达量的校正、转录本多样性的识别及量化中的应用进行了重点综述,可为有效利用RNA spike-in提升转录组测序的质量提供参考。

Abstract

Eukaryotic transcriptome is diverse and complex. RNA sequencing (RNA-seq) can be used to provide a global profile of the transcriptome, find biomarkers associated with dieases and support the diagnosis and treatment of complex diseases. The process of RNA-seq includes sample preparation, library preparation, sequencing and analysis. The technical errors that are introduced during all the workflow influence the accuracy of RNA-seq. These errors can be understood and mitigated through the use of RNA spike-in, which can determine quality control for RNA-seq and improve the reliability of RNA-seq data. Therefore, RNA spike-in and its applications are introduced, including normalization between samples, gene expression measurements, detection and quantification of diverse transcripts, which provides powerful reference for improving the quality of RNA-seq by using RNA spike-in effectively.

导出引用

张瑜,王霞,张永卓,杨佳怡,牛春艳,赵洋,董莲华. RNA内参在转录组测序质量控制中的应用[J]. 计量学报. 2023, 44(5): 818-825 https://doi.org/10.3969/j.issn.1000-1158.2023.05.22

ZHANG Yu,WANG Xia,ZHANG Yong-zhuo,YANG Jia-yi,NIU Chun-yan,ZHAO Yang,DONG Lian-hua. Applications of RNA Spike-in Quality Control for RNA Sequencing[J]. Acta Metrologica Sinica. 2023, 44(5): 818-825 https://doi.org/10.3969/j.issn.1000-1158.2023.05.22

中图分类号： TB99

参考文献

［1］Voelkerding K V, Dames S A, Durtschi J D. Next-generation sequencing: from basic research to diagnostics ［J］. Clin Chem, 2009, 55(4): 641-658.
［2］Serratì S, Simona D S, Brunella P, et al. Next-generation sequencing: advances and applications in cancer diagnosis ［J］. Onco Targets Ther, 2016, 9: 7355-7365.
［3］Byron S A, Van Keuren-Jensen K R, Engelthaler D M, et al. Translating RNA sequencing into clinical diagnostics: opportunities and challenges ［J］. Nat Rev Genet, 2016, 17(5): 257-271.
［4］Lockwood W W, Wilson I M, Coe B P, et al. Divergent genomic and epigenomic landscapes of lung cancer subtypes underscore the selection of different oncogenic pathways during tumor development ［J］. PloS One, 2012, 7(5): e37775.
［5］Zhang A, Wang C, Wang S J, et al. Visualization-aided classification ensembles discriminate lung adenocarcinoma and squamous cell carcinoma samples using their gene expression profiles ［J］. PloS One, 2014, 9(10): e110052.
［6］Price N D, Trent J, EI-Naggar A K, et al. Highly accurate two-gene classifier for differentiating gastrointestinal stromal tumors and leiomyosarcomas ［J］. Proc Natl Acad Sci U S A, 2007, 104(9): 3414-3419.
［7］Yang Z, Zhuang B, Yan Y, et al. Identification of gene markers in the development of smoking-induced lung cancer ［J］. Gene, 2016, 576(1): 451-457.
［8］Faquin, W C. Can a gene-expression classifier with high negative predictive value solve the indeterminate thyroid fine-needle aspiration dilemma? ［J］. Cancer Cytopathol, 2013, 121(3): 116-119.
［9］Tomei S, Marchetti I, Zavaglia K, et al. A molecular computational model improves the preoperative diagnosis of thyroid nodules ［J］. BMC Cancer, 2012, 12(1): 1-10.
［10］Panebianco F, Mazzanti C, Tomei S, et al. The combination of four molecular markers improves thyroid cancer cytologic diagnosis and patient management ［J］. BMC Cancer, 2015, 15(1): 1-11.
［11］Morrissy A S, Morin R D, Delaney A, et al. Next-generation tag sequencing for cancer gene expression profiling ［J］. Genome Res, 2009, 19(10): 1825-1835.
［12］van Dijk E L, Auger H, Jaszczyszyn Y, et al. Ten years of next-generation sequencing technology ［J］. Trends Genet, 2014, 30(9): 418-426.
［13］van Dijk E L, Jaszczyszyn Y, Thermes C. Thermes, Library preparation methods for next-generation sequencing: tone down the bias ［J］. Exp Cell Res, 2014, 322(1): 12-20.
［14］Shi L, Reid L H, Jones W D,et al. The MicroArray Quality Control (MAQC) project shows inter and intra platform reproducibility of gene expression measurements ［J］. Nat Biotechnol, 2006, 24(9): 1151-1161.
［15］Bunk D M. Reference materials and reference measurement procedures: an overview from a national metrology institute ［J］. Clin Biochem Rev, 2007, 28(4): 131-137.
［16］Jiang L, Schlesinger F, Davis C A, et al. Synthetic spike-in standards for RNA-seq experiments ［J］. Genome Res, 2011, 21(9): 1543-1551.
［17］Munro S A. Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures ［J］. Nat Commun, 2014, 5(1): 1-10.
［18］Sprang M, Andrade-Navarro M A, Fontaine J F. Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality ［J］. BMC Bioinformatics, 2022, 23(6): 1-15.
［19］Mane S P, Evans C, Cooper K L, et al. Transcriptome sequencing of the Microarray Quality Control (MAQC) RNA reference samples using next generation sequencing ［J］. BMC Genomics, 2009, 10(1): 1-12.
［20］Sims D J, Harrington R D, Polley E C, et al. Plasmid-based materials as multiplex quality controls and calibrators for clinical next-generation sequencing assays ［J］. J Mol Diagn, 2016, 18(3): 336-349.
［21］Pine P S, Munro S A, Parsons J R, et al. Evaluation of the External RNA Controls Consortium (ERCC) reference material using a modified Latin square design ［J］. BMC Biotechnol, 2016, 16(1): 1-15.
［22］External RNA Controls Consortium. Proposed methods for testing and selecting the ERCC external RNA controls ［J］. BMC Genomics, 2005, 6: 150.
［23］Baker S C, Bauer S R, Beyer R P, et al. The External RNA Controls Consortium: a progress report ［J］. Nat Methods, 2005, 2(10): 731-734.
［24］Paul L, Kubala P, Horner G, et al. SIRVs: spike-in RNA variants as external isoform controls in RNA-sequencing ［J］. bioRxiv, 2016, https://doi.org/10.1101/080747.
［25］Hardwick S A, Chen W Y, Wong T, et al. Spliced synthetic genes as internal controls in RNA sequencing experiments ［J］. Nat Methods, 2016, 13(9): 792-798.
［26］Risso D, Ngai J, Speed T P, et al. Normalization of RNA-seq data using factor analysis of control genes or samples ［J］. Nat Biotechnol, 2014, 32(9): 896-902.
［27］Locati M D, Terpstra I, de Leeuw W C, et al. Improving small RNA-seq by using a synthetic spike-in set for size-range quality control together with a set for data normalization ［J］. Nucleic Acids Res, 2015, 43(14): 86-89.
［28］Conesa A, Madrigal P, Tarazona S, et al. A survey of best practices for RNA-seq data analysis ［J］. Genome Biol, 2016, 17(1): 1-19.
［29］Bullard J H, Purdom E, Hansen K D, et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments ［J］. BMC Bioinformatics, 2010, 11(1): 1-13.
［30］Robinson M D, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data ［J］. Genome Biol, 2010, 11(3): 1-9.
［31］Hu Z, Chen K, Xia Z, et al. Nucleosome loss leads to global transcriptional up-regulation and genomic instability during yeast aging ［J］. Genes Dev, 2014, 28(4): 396-408.
［32］Lovén J, Orlando D A, Sigova A A, et al. Revisiting global gene expression analysis ［J］. Cell, 2012, 151(3): 476-482.
［33］Lovén J, Hoke H A, Lin C Y, et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers ［J］. Cell, 2013. 153(2): 320-334.
［34］Andrews T S, Hemberg M. Identifying cell populations with scRNASeq ［J］. Mol Aspects Med, 2018, 59: 114-122.
［35］Vallejos C A, Risso D, Scialdone A, et al. Normalizing single-cell RNA sequencing data: challenges and opportunities ［J］. Nat Methods, 2017, 14(6): 565-571.
［36］Gierahn T, Wadsworth M H, Hughes T K, et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput ［J］. Nat Methods, 2017, 14: 395-398.
［37］Macosko E Z, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets ［J］. Cell, 2015, 161(5): 1202-1214.
［38］Mercer T R, Gerhardt D J, Dinger M E, et al. Targeted RNA sequencing reveals the deep complexity of the human transcriptome ［J］. Nat Biotechnol, 2012, 30(1): 99-107.
［39］Heyer E E, Deveson I W, Wooi D, et al. Diagnosis of fusion genes using targeted RNA sequencing ［J］. Nat Commun, 2019, 10(1): 1-12.
［40］Lever J, Krzywinski M, Altman N, et al. Points of significance: model selection and overfitting ［J］. Nat Methods, 2016, 13(9): 703-705.
［41］Altman N, Krzywinski M. Points of Significance: Simple linear regression ［J］. Nat Methods, 2015, 12(11): 999-1000.
［42］Deveson I W, Chen W Y, Wong T, et al. Representing genetic variation with synthetic DNA standards ［J］. Nat Methods, 2016, 13(9): 784-791.
［43］Tong L, Yang C, Wu P Y, et al. Evaluating the impact of sequencing error correction for RNA-seq data with ERCC RNA spike-in controls. IEEE EMBS Int Conf Biomed Health Inform, 2016: 74-77.
［44］Cibulskis K, Lawrence M S, Carter S L, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples ［J］. Nat Biotechnol, 2013, 31(3): 213-219.
［45］SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium ［J］. Nat Biotechnol, 2014, 32(9): 903-914.
［46］Li S, Tighe S W, Nicolet C M, et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study ［J］. Nat Biotechnol, 2014, 32(9): 915-925.
［47］Frankiw L, Baltimore D, Li G. Alternative mRNA splicing in cancer immunotherapy ［J］. Nat Rev Immunol, 2019, 19(11): 675-687.
［48］Stransky N, Cerami E, Schalm S, et al. The landscape of kinase fusions in cancer ［J］. Nature Commun, 2014, 5(1): 1-10.
［49］Tembe W D, Pond S J K, Legendre C, et al. Open-access synthetic spike-in mRNA-seq data for cancer gene fusions ［J］. BMC Genomics, 2014, 15(1): 1-9.