Integrated Chip-Seq and RNA-Seq Data Analysis Coupled with Bioinformatics Approaches to Investigate Regulatory Landscape of Transcription Modulators in Breast Cancer Cells

  • Jairo Ramos
  • Quentin Felty
  • Deodutta RoyEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2102)


The objective of this chapter is to describe step-by-step bioinformatics and functional genomics solutions for analyzing ChIP-Seq and RNA-Seq data for understanding the regulatory mechanisms of chromatin modifiers and transcription factors that can drive pathogenesis of chronic complex human diseases, such as cancer. Here we have used two transcription regulatory proteins: nuclear respiratory factor 1 (NRF1) and inhibitor of differentiation protein 3 (ID3) for ChIP-Seq and RNA-Seq data as examples for discussing the importance of selecting the appropriate computational analysis methods, software, and parameters for the processing of raw data as well as their integrative regulatory landscape analysis to obtain accurate and reliable results. Both ChIP-Seq and RNA-Seq analytic methodologies are used as instructional examples to identify NRF1 or ID3 binding to the promoters and enhancers in the genome and their effects on the activity as well as to discover target genes that can drive breast cancer.

Key words

Integrated ChIP-Seq and RNA-Seq NRF1 ID3 Transcription factor Bioinformatics 


  1. 1.
    Das JK, Doke M, Deoraj A, Felty Q, Roy D (2018) Exosomal ID3 is pro-metastatic through guiding NRF1-induced breast cancer stem cells across the blood-brain-barrier [abstract 1128]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14–18; Chicago, IL. AACR, Philadelphia (PA); Cancer Res 2018;78(13 Suppl): Abstract nr 1128Google Scholar
  2. 2.
    Bhawe K, Roy D (2018 Oct) Interplay between NRF1, E2F4 and MYC transcription factors regulating common target genes contributes to cancer development and progression. Cell Oncol (Dordr) 41(5):465–484. Scholar
  3. 3.
    Das JK, Felty Q, Poppiti R, Jackson RM, Roy D (2018) Nuclear respiratory factor 1 acting as an oncoprotein drives estrogen-induced breast carcinogenesis. Cell 7(12):E234. Scholar
  4. 4.
    Preciados M, Yoo C, Roy D (2016) Estrogenic endocrine disrupting chemicals influencing NRF1 regulated gene networks in the development of complex human brain diseases. Int J Mol Sci 17(12):E2086PubMedCrossRefGoogle Scholar
  5. 5.
    Wang S, Sun H, Ma J, Zang C, Wang C, Wang J, Liu XS (2013) Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat Protoc 8(12):2502–2515PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Dai Q (2007) Chromatin regulators and transcriptional control of drosophila (Unpublished Doctoral dissertation). Wenner-Grens institut för experimentell biologi, Stockholm University, Stockholm, SwedenGoogle Scholar
  7. 7.
    Bailey TL, Machanick P (2012) Inferring direct DNA binding from ChIP-seq. Nucleic Acids Res 40(17):1–10CrossRefGoogle Scholar
  8. 8.
    Furey TS (2012) ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet 13(12):840–852PubMedPubMedCentralCrossRefGoogle Scholar
  9. 9.
    Finotello F, Di Camillo B (2015) Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis. Brief Funct Genomics 14(2):130–142PubMedCrossRefGoogle Scholar
  10. 10.
    Feng J, Liu T, Qin B, Zhang Y, Liu XS (2012) Identifying ChIP-seq enrichment using MACS. Nat Protoc 7(9):1728–1740PubMedCrossRefGoogle Scholar
  11. 11.
    Park PJ (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 10(10):669–680PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Domcke S, Bardet AF, Adrian Ginno P, Hartl D, Burger L, Schubeler D (2015) Competition between DNA methylation and transcription factors determines binding of NRF1. Nature 528(7583):575–579PubMedCrossRefGoogle Scholar
  13. 13.
    Hon GC, Hawkins RD, Caballero OL, Lo C, Lister R, Pelizzola M et al (2012) Global DNA hypomethylation coupled to repressive chromatin domain formation and gene silencing in breast cancer. Genome Res 22(2):246–258PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, Chen Y (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res 22(9):1813–1831PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Rozowsky J, Euskirchen G, Auerbach RK, Zhang ZD, Gibson T, Bjornson R et al (2009) PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nat Biotechnol 27(1):66–75PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Andrews S (2010). FastQC: a quality control tool for high throughput sequence data.
  17. 17.
    Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15):2114–2120PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Langmead B, Salzberg SL (2012) Fast gapped-read alignment with bowtie 2. Nat Methods 9(4):357–359PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    McLean CY, Bristor D, Hiller M, Clarke SL, Schaar BT, Lowe CB et al (2010) GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28(5):495–501PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14(4):1–13CrossRefGoogle Scholar
  21. 21.
    Anders S, Pyl PT, Huber W (2015) HTSeq—A python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169PubMedCrossRefGoogle Scholar
  22. 22.
    Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Mortazavi A (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17(13):1–9Google Scholar
  23. 23.
    Pachter L (2011) Models for transcript quantification from RNA-seq. arXiv 1104(3889):1–28Google Scholar
  24. 24.
    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(550):1–21Google Scholar
  25. 25.
    Tang Q, Chen Y, Meyer C, Geistlinger T, Lupien M, Wang Q et al (2011) A comprehensive view of nuclear receptor cancer cistromes. Cancer Res 71(22):6940–6947PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Caldon CE, Sergio CM, Kang J, Muthukaruppan A, Boersma MN, Stone A, Gee JM (2012) Cyclin E2 overexpression is associated with endocrine resistance but not insensitivity to CDK2 inhibition in human breast cancer cells. Mol Cancer Ther 11(7):1488–1499PubMedCrossRefGoogle Scholar
  27. 27.
    Lehtinen L, Vainio P, Wikman H, Reemts J, Hilvo M, Issa R et al (2012) 15-hydroxyprostaglandin dehydrogenase associates with poor prognosis in breast cancer, induces epithelial-mesenchymal transition, and promotes cell migration in cultured breast cancer cells. J Pathol 226(4):674–686PubMedCrossRefGoogle Scholar
  28. 28.
    Pond AC, Bin X, Batts T, Roarty K, Hilsenbeck S, Rosen JM (2013) Fibroblast growth factor receptor signaling is essential for normal mammary gland development and stem cell function. Stem Cells 31(1):178–189PubMedCrossRefGoogle Scholar
  29. 29.
    Xian W, Pappas L, Pandya D, Selfors LM, Derksen PW, de Bruin M, Brugge JS (2009) Fibroblast growth factor receptor 1-transformed mammary epithelial cells are dependent on RSK activity for growth and survival. Cancer Res 69(6):2244–2251PubMedCrossRefGoogle Scholar
  30. 30.
    Zubor P, Hatok J, Moricova P, Kapustova I, Kajo K, Mendelova A et al (2015) Gene expression profiling of histologically normal breast tissue in females with human epidermal growth factor receptor 2positive breast cancer. Mol Med Rep 11(2):1421–1427PubMedCrossRefGoogle Scholar
  31. 31.
    Sathyanarayana UG, Padar A, Huang CX, Suzuki M, Shigematsu H, Bekele BN, Gazdar AF (2003) Aberrant promoter methylation and silencing of laminin-5-encoding genes in breast carcinoma. Clin Cancer Res 9(17):6389–6394PubMedGoogle Scholar
  32. 32.
    Bourguignon LY, Spevak CC, Wong G, Xia W, Gilad E (2009) Hyaluronan-CD44 interaction with protein kinase C(epsilon) promotes oncogenic signaling by the stem cell marker nanog and the production of microRNA-21, leading to down-regulation of the tumor suppressor protein PDCD4, anti-apoptosis, and chemotherapy resistance in breast tumor cells. J Biol Chem 284(39):26533–26546PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Zhang J, Wang C, Chen X, Takada M, Fan C, Zheng X et al (2015) EglN2 associates with the NRF1-PGC1alpha complex and controls mitochondrial function in breast cancer. EMBO J 34(23):2953–2970PubMedPubMedCentralCrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of Environmental Health SciencesFlorida International UniversityMiamiUSA

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