How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes?

被引:162
|
作者
Haas, Brian J. [1 ]
Chin, Melissa [1 ]
Nusbaum, Chad [1 ]
Birren, Bruce W. [1 ]
Livny, Jonathan [1 ,2 ]
机构
[1] Broad Inst MIT & Harvard, Genome Sequencing & Anal Program, Cambridge, MA 02142 USA
[2] Harvard Univ, Sch Med, Channing Lab, Brigham & Womens Hosp, Boston, MA 02115 USA
来源
BMC GENOMICS | 2012年 / 13卷
基金
美国国家卫生研究院;
关键词
FULLY AUTOMATED PROCESS; HIGH-THROUGHPUT; DIFFERENTIAL EXPRESSION; VIBRIO-CHOLERAE; CONSTRUCTION; ARCHITECTURE; DISCOVERY;
D O I
10.1186/1471-2164-13-734
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Background: High-throughput sequencing of cDNA libraries (RNA-Seq) has proven to be a highly effective approach for studying bacterial transcriptomes. A central challenge in designing RNA-Seq-based experiments is estimating a priori the number of reads per sample needed to detect and quantify thousands of individual transcripts with a large dynamic range of abundance. Results: We have conducted a systematic examination of how changes in the number of RNA-Seq reads per sample influences both profiling of a single bacterial transcriptome and the comparison of gene expression among samples. Our findings suggest that the number of reads typically produced in a single lane of the Illumina HiSeq sequencer far exceeds the number needed to saturate the annotated transcriptomes of diverse bacteria growing in monoculture. Moreover, as sequencing depth increases, so too does the detection of cDNAs that likely correspond to spurious transcripts or genomic DNA contamination. Finally, even when dozens of barcoded individual cDNA libraries are sequenced in a single lane, the vast majority of transcripts in each sample can be detected and numerous genes differentially expressed between samples can be identified. Conclusions: Our analysis provides a guide for the many researchers seeking to determine the appropriate sequencing depth for RNA-Seq-based studies of diverse bacterial species.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Deep Margins Melanoma: How Deep Is Deep Enough?
    Burger, Megan L.
    Haggerty, James M.
    Wang, Shengxuan
    Oxenberg, Jacqueline C.
    AMERICAN SURGEON, 2023, 89 (12) : 5297 - 5303
  • [42] Subcellular RNA-seq for the Analysis of the Dendritic and Somatic Transcriptomes of Single Neurons
    Perez, Julio D.
    Schuman, Erin M.
    BIO-PROTOCOL, 2022, 12 (01):
  • [43] A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data
    Srinivasan, Suhas
    Leshchyk, Anastasia
    Johnson, Nathan T.
    Korkin, Dmitry
    RNA, 2020, 26 (10) : 1303 - 1319
  • [44] Unraveling chloroplast transcriptomes with ChloroSeq, an organelle RNA-Seq bioinformatics pipeline
    Smith, David Roy
    Lima, Matheus Sanita
    BRIEFINGS IN BIOINFORMATICS, 2017, 18 (06) : 1012 - 1016
  • [45] Complete RNA-seq analysis of cancer transcriptomes from FFPE samples
    Shujun Luo
    Irina Khrebtukova
    Chuck Perou
    Gary P Schroth
    Genome Biology, 11 (Suppl 1)
  • [46] Complete RNA-seq analysis of cancer transcriptomes from FFPE samples
    Luo, Shujun
    Khrebtukova, Irina
    Perou, Chuck
    Schroth, Gary P.
    GENOME BIOLOGY, 2010, 11
  • [47] Computational analysis of bacterial RNA-Seq data
    McClure, Ryan
    Balasubramanian, Divya
    Sun, Yan
    Bobrovskyy, Maksym
    Sumby, Paul
    Genco, Caroline A.
    Vanderpool, Carin K.
    Tjaden, Brian
    NUCLEIC ACIDS RESEARCH, 2013, 41 (14)
  • [48] Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data
    Qin, Lu
    Zhang, Guangya
    Zhang, Shaoqiang
    Chen, Yong
    ADVANCED SCIENCE, 2024, 11 (29)
  • [49] Deep learning enables accurate alignment of single cell RNA-seq data
    Zhong, Yuanke
    Li, Jing
    Liu, Jie
    Zheng, Yan
    Shang, Xuequn
    Hu, Jialu
    2019 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2019, : 778 - 781
  • [50] Single-cell RNA-seq denoising using a deep count autoencoder
    Gökcen Eraslan
    Lukas M. Simon
    Maria Mircea
    Nikola S. Mueller
    Fabian J. Theis
    Nature Communications, 10