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
  • [31] Missing data and technical variability in single-cell RNA-sequencing experiments
    Hicks, Stephanie C.
    Townes, F. William
    Teng, Mingxiang
    Irizarry, Rafael A.
    BIOSTATISTICS, 2018, 19 (04) : 562 - 578
  • [32] Exploring the Genetic Resistance to Gastrointestinal Nematodes Infection in Goat Using RNA-Sequencing
    Bhuiyan, Ali Akbar
    Li, Jingjin
    Wu, Zhenyang
    Ni, Pan
    Adetula, Adeyinka Abiola
    Wang, Haiyan
    Zhang, Cheng
    Tang, Xiaohui
    Bhuyan, Anjuman Ara
    Zhao, Shuhong
    Du, Xiaoyong
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2017, 18 (04):
  • [33] Transcriptional landscape of Aspergillus nigerat breaking of conidial dormancy revealed by RNA-sequencing
    Michaela Novodvorska
    Kimran Hayer
    Steven T Pullan
    Raymond Wilson
    Martin J Blythe
    Hein Stam
    Malcolm Stratford
    David B Archer
    BMC Genomics, 14
  • [34] Metabolic changes of Issatchenkia orientalis under acetic acid stress by transcriptome profile using RNA-sequencing
    Li, Yueqi
    Li, Yingdi
    Li, Ruoyun
    Liu, Lianliang
    Miao, Yingjie
    Weng, Peifang
    Wu, Zufang
    INTERNATIONAL MICROBIOLOGY, 2022, 25 (03) : 417 - 426
  • [35] Combined statistics for differential expression analysis of RNA-sequencing data
    Fanidis, Dionysios
    Moulos, Panagiotis
    2019 IEEE 19TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE), 2019, : 170 - 173
  • [36] PASTA: splice junction identification from RNA-Sequencing data
    Tang, Shaojun
    Riva, Alberto
    BMC BIOINFORMATICS, 2013, 14
  • [37] Single-Cell RNA-Sequencing in Glioma
    Johnson, Eli
    Dickerson, Katherine L.
    Connolly, Ian D.
    Gephart, Melanie Hayden
    CURRENT ONCOLOGY REPORTS, 2018, 20 (05)
  • [38] Chromosomal imbalances detected via RNA-sequencing in 28 cancers
    Ozcan, Zuhal
    San Lucas, Francis A.
    Wong, Justin W.
    Chang, Kyle
    Stopsack, Konrad H.
    Fowler, Jerry
    Jakubek, Yasminka A.
    Scheet, Paul
    BIOINFORMATICS, 2022, 38 (06) : 1483 - 1490
  • [39] Comparison of Methods for Feature Selection in Clustering of High-Dimensional RNA-Sequencing Data to Identify Cancer Subtypes
    Kallberg, David
    Vidman, Linda
    Ryden, Patrik
    FRONTIERS IN GENETICS, 2021, 12
  • [40] Comparison of five different RNA sources to examine the lactating bovine mammary gland transcriptome using RNA-Sequencing
    Canovas, Angela
    Rincon, Gonzalo
    Bevilacqua, Claudia
    Islas-Trejo, Alma
    Brenaut, Pauline
    Hovey, Russell C.
    Boutinaud, Marion
    Morgenthaler, Caroline
    VanKlompenberg, Monica K.
    Martin, Patrice
    Medrano, Juan F.
    SCIENTIFIC REPORTS, 2014, 4