Advertisement

Identification of Genomic Alterations Through Multilevel DNA Structural Analysis

  • Ryan K. Shultzaberger
  • John Dresios
Part of the Methods in Molecular Biology book series (MIMB, volume 1896)

Abstract

Current methods to identify genomic alterations using whole-genome sequencing (WGS) data are often limited to single nucleotide polymorphisms and insertions and deletions that are less than 10 bp in length. These limitations are largely due to challenges in accurately mapping short sequencing reads that significantly diverge from the reference genome. Newer sequencing-based methods have been developed to define and characterize larger DNA structural elements. This is achieved by enriching for and sequencing regions of the genome that contain a specific element, followed by identifying genomic regions with high densities of mapped short reads that designate the location of these elements. This process essentially aggregates short read data into larger structural units for further characterization. Here, we describe protocols for identifying various types of genomic alterations using differential analysis of these structural units. We focus on changes in DNA accessibility, protein–DNA interactions, and chromosomal contacts as measured by ATAC-Seq, ChIP-Seq, and Hi-C respectively. As many protocols have been published describing the generation and processing of these data, we focus on simple methods that can be used to identify mutations in these data, and can be executed by someone with limited computational expertise.

Key words

Mutation detection DNA organization Chromatin DNA accessibility DNA–protein interactions ChIP-Seq ATAC-Seq Hi-C 

References

  1. 1.
    Alkan C, Coe BP, Eichler EE (2011) Genome structural variation discovery and genotyping. Nat Rev Genet 12(5):363CrossRefGoogle Scholar
  2. 2.
    Kornberg RD (1977) Structure of chromatin. Annu Rev Biochem 46(1):931–954CrossRefGoogle Scholar
  3. 3.
    Bouwman BA, de Laat W (2015) Getting the genome in shape: the formation of loops, domains and compartments. Genome Biol 16(1):154CrossRefGoogle Scholar
  4. 4.
    Chen H, Chen J, Muir LA, Ronquist S, Meixner W, Ljungman M, Ried T, Smale S, Rajapakse I (2015) Functional organization of the human 4D Nucleome. Proc Natl Acad Sci U S A 112(26):8002–8007CrossRefGoogle Scholar
  5. 5.
    Huang J, Marco E, Pinello L, Yuan GC (2015) Predicting chromatin organization using histone marks. Genome Biol 16(1):162CrossRefGoogle Scholar
  6. 6.
    Cavalli G, Misteli T (2013) Functional implications of genome topology. Nat Struct Mol Biol 20(3):290CrossRefGoogle Scholar
  7. 7.
    Kaufmann S, Fuchs C, Gonik M, Khrameeva EE, Mironov AA, Frishman D (2015) Inter-chromosomal contact networks provide insights into mammalian chromatin organization. PLoS One 10(5):e0126125CrossRefGoogle Scholar
  8. 8.
    Lieberman-Aiden E, Van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A et al (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326(5950):289–293CrossRefGoogle Scholar
  9. 9.
    Johnson DS, Mortazavi A, Myers RM, Wold B (2007) Genome-wide mapping of in vivo protein-DNA interactions. Science 316(5830):1497–1502CrossRefGoogle Scholar
  10. 10.
    Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ (2013) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10(12):1213CrossRefGoogle Scholar
  11. 11.
    Raha D, Hong M, Snyder M (2010) ChIP-Seq: a method for global identification of regulatory elements in the genome. Curr Protoc Mol Biol 21:21–19Google Scholar
  12. 12.
    Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S et al (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res 22(9):1813–1831CrossRefGoogle Scholar
  13. 13.
    Buenrostro JD, Wu B, Chang HY, Greenleaf WJ (2015) ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr Protoc Mol Biol 109:21–29Google Scholar
  14. 14.
    Van Berkum NL, Lieberman-Aiden E, Williams L, Imakaev M, Gnirke A, Mirny LA, Dekker J, Lander ES (2010) Hi-C: a method to study the three-dimensional architecture of genomes. J Vis Exp 39Google Scholar
  15. 15.
    Durand NC, Shamim MS, Machol I, Rao SS, Huntley MH, Lander ES, Aiden EL (2016) Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst 3(1):95–98CrossRefGoogle Scholar
  16. 16.
    Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP (2011) Integrative genomics viewer. Nat Biotechnol 29(1):24CrossRefGoogle Scholar
  17. 17.
    Durand NC, Robinson JT, Shamim MS, Machol I, Mesirov JP, Lander ES, Aiden EL (2016) Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst 3(1):99–101CrossRefGoogle Scholar
  18. 18.
    Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550CrossRefGoogle Scholar
  19. 19.
    Lun AT, Smyth GK (2015) diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data. BMC Bioinformatics 16(1):258CrossRefGoogle Scholar
  20. 20.
    Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9(9):R137CrossRefGoogle Scholar
  21. 21.
    Backman TW, Girke T (2016) systemPipeR: NGS workflow and report generation environment. BMC Bioinformatics 17(1):388CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Leidos Inc.San DiegoUSA

Personalised recommendations