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Aggregate and Heatmap Representations of Genome-Wide Localization Data Using VAP, a Versatile Aggregate Profiler

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1334))

Abstract

In the analysis of experimental data corresponding to the signal enrichment of chromatin features such as histone modifications throughout the genome, it is often useful to represent the signal over known regions of interest, such as genes, using aggregate or individual profiles. In the present chapter, we describe and explain the best practices on how to generate such profiles as well as other usages of the versatile aggregate profiler (VAP) tool (Coulombe et al., Nucleic Acids Res 42:W485–W493, 2014), with a particular focus on the new functionalities introduced in version 1.1.0 of VAP.

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References

  1. Coulombe C, Poitras C, Nordell-Markovits A et al (2014) VAP: a versatile aggregate profiler for efficient genome-wide data representation and discovery. Nucleic Acids Res 42:W485–W493

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  2. Zentner GE, Henikoff S (2014) High-resolution digital profiling of the epigenome. Nat Rev Genet 15:814–827

    Article  CAS  PubMed  Google Scholar 

  3. Kent WJ, Sugnet CW, Furey TS et al (2002) The human genome browser at UCSC. Genome Res 12:996–1006

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  4. Robinson J, Thorvaldsdóttir H, Winckler W et al (2011) Integrative genomics viewer. Nat Biotechnol 29:24–26

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Meyer CA, Liu XS (2014) Identifying and mitigating bias in next-generation sequencing methods for chromatin biology. Nat Rev Genet 15:709–721

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Nordell MA, Joly BC, Toupin D et al (2013) NGS++: a library for rapid prototyping of epigenomics software tools. Bioinformatics 29:1893–1894

    Article  Google Scholar 

  7. Pokholok D, Harbison C, Levine S et al (2005) Genome-wide map of nucleosome acetylation and methylation in yeast. Cell 122:517–527

    Article  CAS  PubMed  Google Scholar 

  8. Guillemette B, Bataille AR, Gévry N et al (2005) Variant histone H2A.Z is globally localized to the promoters of inactive yeast genes and regulates nucleosome positioning. PLoS Biol 3, e384

    Article  PubMed Central  PubMed  Google Scholar 

  9. Holstege FC, Jennings EG, Wyrick JJ et al (1998) Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95:717–728

    Article  CAS  PubMed  Google Scholar 

  10. The Encode Project Consortium, Dunham I, Kundaje A et al (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74

    Article  PubMed Central  Google Scholar 

  11. Hoffman MM, Ernst J, Wilder SP et al (2013) Integrative annotation of chromatin elements from ENCODE data. Nucleic Acids Res 41:827–841

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Bolger A, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  14. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Li H, Handsaker B, Wysoker A et al (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079

    Article  PubMed Central  PubMed  Google Scholar 

  16. Lassmann T, Hayashizaki Y, Daub CO (2011) SAMStat: monitoring biases in next generation sequencing data. Bioinformatics 27:130–131

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26:841–842

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Giardine B, Riemer C, Hardison RC et al (2005) Galaxy: a platform for interactive large-scale genome analysis. Genome Res 15:1451–1455

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Jeronimo C, Bataille AR, Robert F (2013) The writers, readers, and functions of the RNA polymerase II C-terminal domain code. Chem Rev 113:8491–8522

    Article  CAS  PubMed  Google Scholar 

  20. Bonhoure N, Bounova G, Bernasconi D et al (2014) Quantifying ChIP-seq data: a spiking method providing an internal reference for sample-to-sample normalization. Genome Res 24:1157–1168

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  21. Lovén J, Orlando D, Sigova A et al (2012) Revisiting global gene expression analysis. Cell 151:476–482

    Article  PubMed Central  PubMed  Google Scholar 

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Correspondence to Pierre-Étienne Jacques .

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Brunelle, M. et al. (2015). Aggregate and Heatmap Representations of Genome-Wide Localization Data Using VAP, a Versatile Aggregate Profiler. In: Leblanc, B., Rodrigue, S. (eds) DNA-Protein Interactions. Methods in Molecular Biology, vol 1334. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2877-4_18

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  • DOI: https://doi.org/10.1007/978-1-4939-2877-4_18

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2876-7

  • Online ISBN: 978-1-4939-2877-4

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