A Bioinformatics Pipeline for the Identification of CHO Cell Differential Gene Expression from RNA-Seq Data

Part of the Methods in Molecular Biology book series (MIMB, volume 1603)


In recent years, the publication of genome sequences for the Chinese hamster and Chinese hamster ovary (CHO) cell lines has facilitated study of these biopharmaceutical cell factories with unprecedented resolution. Our understanding of the CHO cell transcriptome, in particular, has rapidly advanced through the application of next-generation sequencing (NGS) technology to characterize RNA expression (RNA-Seq). In this chapter, we present a computational pipeline for the analysis of CHO cell RNA-Seq data from the Illumina platform to identify differentially expressed genes. The example data and bioinformatics workflow required to run this analysis are freely available at

Key words

Transcriptomics RNA-Seq Differential gene expression Chinese hamster ovary cells Biopharmaceutical manufacture Systems biotechnology 



The authors gratefully acknowledge funding from Science Foundation Ireland (grant refs: 13/SIRG/2084 and 13/IA/1963) and the eCHO systems Marie Curie ITN programme (grant ref.: 642663).


  1. 1.
    Brinkrolf K, Rupp O, Laux H, Kollin F et al (2013) Chinese hamster genome sequenced from sorted chromosomes. Nat Biotechnol 31:694–695CrossRefPubMedGoogle Scholar
  2. 2.
    Lewis NE, Liu X, Li Y, Nagarajan H et al (2013) Genomic landscapes of Chinese hamster ovary cell lines as revealed by the Cricetulus griseus draft genome. Nat Biotechnol 31:759–765CrossRefPubMedGoogle Scholar
  3. 3.
    Xu X, Nagarajan H, Lewis NE, Pan S et al (2011) The genomic sequence of the Chinese hamster ovary (CHO)-K1 cell line. Nat Biotechnol 29:735–741CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Kaas CS, Kristensen C, Betenbaugh MJ, Andersen MR (2015) Sequencing the CHO DCB11 genome reveals regional variations in genomic stability and haploidy. BMC Genomics 16:160CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Meleady P, Hoffrogge R, Henry M, Rupp O et al (2012) Utilization and evaluation of CHO-specific sequence databases for mass spectrometry based proteomics. Biotechnol Bioeng 109:1386–1394CrossRefPubMedGoogle Scholar
  6. 6.
    Ronda C, Pedersen LE, Hansen HG, Kallehauge TB et al (2014) Accelerating genome editing in CHO cells using CRISPR Cas9 and CRISPy, a web-based target finding tool. Biotechnol Bioeng 111:1604–1616CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
  8. 8.
    Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357–360CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    DeLuca DS, Levin JZ, Sivachenko A, Fennell T et al (2012) RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28:1530–1532CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Anders S, Pyl PT, Huber W (2015) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics (Oxford, England) 31:166–169CrossRefGoogle Scholar
  12. 12.
    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Li H, Handsaker B, Wysoker A, Fennell T et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
  15. 15.
    Hannedouche S, Beck V, Leighton-Davies J, Beibel M et al (2013) Identification of the C3a receptor (C3AR1) as the target of the VGF-derived peptide TLQP-21 in rodent cells. J Biol Chem 288:27434–27443CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Monger C, Kelly PS, Gallagher C, Clynes M et al (2015) Towards next generation CHO cell biology: bioinformatics methods for RNA-Seq-based expression profiling. Biotechnol J 10:950–966CrossRefPubMedGoogle Scholar
  17. 17.
    BBMap—Bushnell B.—

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.National Institute for Bioprocessing Research and TrainingCo. DublinIreland
  2. 2.National Institute for Cellular BiotechnologyDublin City UniversityDublin 9Ireland

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