Skip to main content
SpringerLink
Log in
Book cover

Avian and Reptilian Developmental Biology pp 87–100Cite as

Systems Biology Analyses in Chicken: Workflow for Transcriptome and ChIP-Seq Analyses Using the Chicken Skin Paradigm

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1650))

Abstract

With advances in molecular biology, various biological phenomena can now be explored at higher resolution using mRNA sequencing (RNA-Seq) and chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq), two powerful high-throughput next-generation sequencing (NGS) technologies. While methods are used widely in mouse, human, etc., less information is available in other animals, such as the chicken. Here we assemble a workflow of the RNA-Seq and ChIP-Seq analyses for the chicken studies using chicken skin appendage tissue as an example. We present guidelines for RNA-Seq quality control, alignment, quantification, normalization, and differentially expressed gene analysis. In the meantime, we outline a bioinformatics pipeline for ChIP-Seq quality control, alignment, peak calling, super-enhancer identification, and differential enrichment analysis.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Smith J, Burt DW (2015) The Avian RNAseq Consortium: a community effort to annotate the chicken genome. Cytogenet Genome Res 145(2):78–179. doi:10.1101/012559

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ng CS, Wu P, Fan WL, Yan J, Chen CK, Lai YT, Wu SM, Mao CT, Chen JJ, Lu MYJ, Ho MR, Widelitz RB, Chen CF, Chuong CM, Li WH (2014) Genomic organization, transcriptomic analysis, and functional characterization of avian alpha- and beta-keratins in diverse feather forms. Genome Biol Evol 6(9):2258–2273. doi:10.1093/gbe/evu181

    Article  PubMed  PubMed Central  Google Scholar 

  3. Strasser B, Mlitz V, Hermann M, Rice RH, Eigenheer RA, Alibardi L, Tschachler E, Eckhart L (2014) Evolutionary origin and diversification of epidermal barrier proteins in amniotes. Mol Biol Evol 31(12):3194–3205. doi:10.1093/molbev/msu251

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Williams AG, Thomas S, Wyman SK, Holloway AK (2014) RNA-seq data: challenges in and recommendations for experimental design and analysis. Curr Protoc Hum Genet 83:11 13 11–11 13 20. doi:10.1002/0471142905.hg1113s83

    PubMed  Google Scholar 

  5. Wang Y, Ghaffari N, Johnson CD, Braga-Neto UM, Wang H, Chen R, Zhou H (2011) Evaluation of the coverage and depth of transcriptome by RNA-Seq in chickens. BMC Bioinformatics 12(Suppl 10):S5

    Article  CAS  Google Scholar 

  6. Buisine N, Ruan XA, Bilesimo P, Grimaldi A, Alfama G, Ariyaratne P, Mulawadi F, Chen JQ, Sung WK, Liu ET, Demeneix BA, Ruan YJ, Sachs LM (2015) Xenopus tropicalis genome re-scaffolding and re-annotation reach the resolution required for in vivo ChIA-PET analysis. PLoS One 10(9):27. doi:10.1371/journal.pone.0137526

    Article  Google Scholar 

  7. Speir ML, Zweig AS, Rosenbloom KR, Raney BJ, Paten B, Nejad P, Lee BT, Learned K, Karolchik D, Hinrichs AS, Heitner S, Harte RA, Haeussler M, Guruvadoo L, Fujita PA, Eisenhart C, Diekhans M, Clawson H, Casper J, Barber GP, Haussler D, Kuhn RM, Kent WJ (2016) The UCSC Genome Browser database: 2016 update. Nucleic Acids Res 44(D1):D717–D725. doi:10.1093/nar/gkv1275

    Article  CAS  PubMed  Google Scholar 

  8. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21. doi:10.1093/bioinformatics/bts635

    Article  CAS  PubMed  Google Scholar 

  9. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9(4):357–U354. doi:10.1038/nmeth.1923

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Anders S, Pyl PT, Huber W (2014) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. doi:10.1093/bioinformatics/btu638

  11. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140. doi:10.1093/bioinformatics/btp616

    Article  CAS  PubMed  Google Scholar 

  12. Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11(3):R25. doi:10.1186/gb-2010-11-3-r25

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nussbaum C, Myers RM, Brown M, Li W, Liu XS (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9(9):R137. doi:10.1186/gb-2008-9-9-r137

    Article  PubMed  PubMed Central  Google Scholar 

  14. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26(6):841–842. doi:10.1093/bioinformatics/btq033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ramirez F, Dundar F, Diehl S, Gruning BA, Manke T (2014) deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res 42(W1):W187–W191. doi:10.1093/nar/gku365

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP (2011) Integrative genomics viewer. Nat Biotechnol 29(1):24–26. doi:10.1038/nbt.1754

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Whyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH, Rahl PB, Lee TI, Young RA (2013) Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153(2):307–319. doi:10.1016/j.cell.2013.03.035

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Williams CR, Baccarella A, Parrish JZ, Kim CC (2016) Trimming of sequence reads alters RNA-Seq gene expression estimates. BMC Bioinformatics 17(1):103. doi:10.1186/s12859-016-0956-2

    Article  PubMed  PubMed Central  Google Scholar 

  19. Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J, Guernec G, Jagla B, Jouneau L, Laloe D, Le Gall C, Schaeffer B, Le Crom S, Guedj M, Jaffrezic F, French StatOmique C (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14(6):671–683. doi:10.1093/bib/bbs046

    Article  CAS  PubMed  Google Scholar 

  20. Kramer A, Green J, Pollard J, Tugendreich S (2014) Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30(4):523–530. doi:10.1093/bioinformatics/btt703

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work is supported by a grant from Integrative Stem Cell Center, China Medical University Hospital, China Medical University, and by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under Award Numbers AR 47364 and AR 60306 of NIH in USA. We thank the University of Southern California’s Norris Medical Library Bioinformatics Service for assisting with sequencing data analysis. Some bioinformatics software and computing resources used in the analysis are funded by the USC Office of Research and the Norris Medical Library. The exemplary NGS data in this chapter were generated by Dr. Ping Wu and Dr. Yan Jie.

Author information

Authors and Affiliations

  1. Integrative Stem Cell Center, China Medical University Hospital, China Medical University, Taichung, 40402, Taiwan

    Yung-Chih Lai & Cheng-Ming Chuong

  2. Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA

    Yung-Chih Lai, Randall B. Widelitz & Cheng-Ming Chuong

  3. Research Center for Developmental Biology and Regenerative Medicine, National Taiwan University, Taipei, 10617, Taiwan

    Yung-Chih Lai & Cheng-Ming Chuong

Authors
  1. Yung-Chih Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Randall B. Widelitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cheng-Ming Chuong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yung-Chih Lai .

Editor information

Editors and Affiliations

  1. IRCMS, Kumamoto University, Kumamoto, Kumamoto, Japan

    Guojun Sheng

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Lai, YC., Widelitz, R.B., Chuong, CM. (2017). Systems Biology Analyses in Chicken: Workflow for Transcriptome and ChIP-Seq Analyses Using the Chicken Skin Paradigm. In: Sheng, G. (eds) Avian and Reptilian Developmental Biology. Methods in Molecular Biology, vol 1650. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7216-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7216-6_5

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7215-9

  • Online ISBN: 978-1-4939-7216-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation