Comparison of RNA-Sequencing Methods for Degraded RNA

被引:1
|
作者
Ura, Hiroki [1 ,2 ]
Niida, Yo [1 ,2 ]
机构
[1] Kanazawa Med Univ Hosp, Ctr Clin Genom, 1-1 Daigaku, Uchinada, Ishikawa 9200293, Japan
[2] Kanazawa Med Univ, Med Res Inst, Dept Adv Med, Div Genom Med, 1-1 Daigaku, Uchinada, Kahoku 9200293, Japan
基金
日本学术振兴会;
关键词
transcriptome; RNA-Seq; degraded RNA; gene expression; TRANSCRIPTOME; SEQ;
D O I
10.3390/ijms25116143
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
RNA sequencing (RNA-Seq) is a powerful technique and is increasingly being used in clinical research and drug development. Currently, several RNA-Seq methods have been developed. However, the relative advantage of each method for degraded RNA and low-input RNA, such as RNA samples collected in the field of clinical setting, has remained unknown. The Standard method of RNA-Seq captures mRNA by poly(A) capturing using Oligo dT beads, which is not suitable for degraded RNA. Here, we used three commercially available RNA-Seq library preparation kits (SMART-Seq, xGen Broad-range, and RamDA-Seq) using random primer instead of Oligo dT beads. To evaluate the performance of these methods, we compared the correlation, the number of detected expressing genes, and the expression levels with the Standard RNA-Seq method. Although the performance of RamDA-Seq was similar to that of Standard RNA-Seq, the performance for low-input RNA and degraded RNA has decreased. The performance of SMART-Seq was better than xGen and RamDA-Seq in low-input RNA and degraded RNA. Furthermore, the depletion of ribosomal RNA (rRNA) improved the performance of SMART-Seq and xGen due to increased expression levels. SMART-Seq with rRNA depletion has relative advantages for RNA-Seq using low-input and degraded RNA.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Bias detection and correction in RNA-Sequencing data
    Zheng, Wei
    Chung, Lisa M.
    Zhao, Hongyu
    BMC BIOINFORMATICS, 2011, 12
  • [2] Transcriptomics and single-cell RNA-sequencing
    Chambers, Daniel C.
    Carew, Alan M.
    Lukowski, Samuel W.
    Powell, Joseph E.
    RESPIROLOGY, 2019, 24 (01) : 29 - 36
  • [3] A comparison between ribo-minus RNA-sequencing and polyA-selected RNA-sequencing
    Cui, Peng
    Lin, Qiang
    Ding, Feng
    Xin, Chengqi
    Gong, Wei
    Zhang, Lingfang
    Geng, Jianing
    Zhang, Bing
    Yu, Xiaomin
    Yang, Jin
    Hu, Songnian
    Yu, Jun
    GENOMICS, 2010, 96 (05) : 259 - 265
  • [4] An Introduction to the Analysis of Single-Cell RNA-Sequencing Data
    AlJanahi, Aisha A.
    Danielsen, Mark
    Dunbar, Cynthia E.
    MOLECULAR THERAPY-METHODS & CLINICAL DEVELOPMENT, 2018, 10 : 189 - 196
  • [5] Comparative analysis of RNA sequencing methods for degraded or low-input samples
    Adiconis, Xian
    Borges-Rivera, Diego
    Satija, Rahul
    DeLuca, David S.
    Busby, Michele A.
    Berlin, Aaron M.
    Sivachenko, Andrey
    Thompson, Dawn Anne
    Wysoker, Alec
    Fennell, Timothy
    Gnirke, Andreas
    Pochet, Nathalie
    Regev, Aviv
    Levin, Joshua Z.
    NATURE METHODS, 2013, 10 (07) : 623 - +
  • [6] A comparison of survival analysis methods for cancer gene expression RNA-Sequencing data
    Raman, Pichai
    Zimmerman, Samuel
    Rathi, Komal S.
    de Torrente, Laurence
    Sarmady, Mahdi
    Wu, Chao
    Leipzig, Jeremy
    Taylor, Deanne M.
    Tozeren, Aydin
    Mar, Jessica C.
    CANCER GENETICS, 2019, 235 : 1 - 12
  • [7] Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data
    Zhang, Lihua
    Zhang, Shihua
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2020, 17 (02) : 376 - 389
  • [8] Normalization of RNA-Sequencing Data from Samples with Varying mRNA Levels
    Aanes, Havard
    Winata, Cecilia
    Moen, Lars F.
    Ostrup, Olga
    Mathavan, Sinnakaruppan
    Collas, Philippe
    Rognes, Torbjorn
    Alestrom, Peter
    PLOS ONE, 2014, 9 (02):
  • [9] Single-cell RNA-sequencing: The future of genome biology is now
    Picelli, Simone
    RNA BIOLOGY, 2017, 14 (05) : 637 - 650
  • [10] A systematic evaluation of single-cell RNA-sequencing imputation methods
    Hou, Wenpin
    Ji, Zhicheng
    Ji, Hongkai
    Hicks, Stephanie C.
    GENOME BIOLOGY, 2020, 21 (01)