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Allele-Specific Expression Analysis in Cancer Using Next-Generation Sequencing Data

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Cancer Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1878))

Abstract

Allele-specific expression arises when transcriptional activity at the different alleles of a gene differs considerably. Although extensive research has been carried out to detect and characterize this phenomenon, the landscape of allele-specific expression in cancer is still poorly understood. In this chapter, we describe a fast and reliable analysis pipeline to study allele-specific expression in cancer using next-generation sequencing data. The pipeline provides a gene-level analysis approach that exploits paired germline DNA and tumor RNA sequencing data and benefits from parallel computation resources when available.

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Notes

  1. 1.

    http://www.bioinformatics.babraham.ac.uk/projects/fastqc/.

  2. 2.

    http://picard.sourceforge.net.

  3. 3.

    http://www.ncbi.nlm.nih.gov/SNP/.

  4. 4.

    https://genome.ucsc.edu/.

  5. 5.

    Only dbSNP 144 common SNPs represented in the 1000 Genome Project genotype data and with global MAF greater or equal to 1% were considered.

References

  1. Lo HS, Wang Z, Hu Y, Yang HH, Gere S, Buetow KH et al (2003) Allelic variation in gene expression is common in the human genome. Genome Res 13(8):1855–1862

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Gimelbrant A, Hutchinson JN, Thompson BR, Chess A (2007) Widespread monoallelic expression on human autosomes. Science 318(5853):1136–1140

    Article  CAS  PubMed  Google Scholar 

  3. Walker EJ, Zhang C, Castelo-Branco P, Hawkins C, Wilson W, Zhukova N et al (2012) Monoallelic expression determines oncogenic progression and outcome in benign and malignant brain tumors. Cancer Res 72(3):636–644

    Article  CAS  PubMed  Google Scholar 

  4. Lalonde E, Ha KCH, Wang Z, Bemmo A, Kleinman CL, Kwan T et al (2011) RNA sequencing reveals the role of splicing polymorphisms in regulating human gene expression. Genome Res 21(4):545–554

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Meyer KB, Maia A-T, O’Reilly M, Teschendorff AE, Chin S-F, Caldas C et al (2008) Allele-specific up-regulation of FGFR2 increases susceptibility to breast cancer. PLoS Biol 6(5):e108

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wei Q-X, Claus R, Hielscher T, Mertens D, Raval A, Oakes CC et al (2013) Germline allele-specific expression of DAPK1 in chronic lymphocytic leukemia. PLoS One 8(1):e55261

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ferguson-Smith AC, Surani MA (2001) Imprinting and the epigenetic asymmetry between parental genomes. Science 293(5532):1086–1089

    Article  CAS  PubMed  Google Scholar 

  8. Knight JC (2004) Allele-specific gene expression uncovered. Trends Genet 20(3):113–116

    Article  CAS  PubMed  Google Scholar 

  9. Pastinen T (2010) Genome-wide allele-specific analysis: insights into regulatory variation. Nat Rev Genet 11(8):533–538

    Article  CAS  PubMed  Google Scholar 

  10. Prandi D, Baca SC, Romanel A, Barbieri CE, Mosquera J-M, Fontugne J et al (2014) Unraveling the clonal hierarchy of somatic genomic aberrations. Genome Biol 15(8):439

    Article  PubMed  PubMed Central  Google Scholar 

  11. Nik-Zainal S, Van Loo P, Wedge DC, Alexandrov LB, Greenman CD, Lau KW et al (2012) Breast Cancer Working Group of the International Cancer Genome Consortium. The life history of 21 breast cancers. Cell 149(5):994–1007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gajecka M (2016) Unrevealed mosaicism in the next-generation sequencing era. Mol Gen Genomics 291:513–530

    Article  CAS  Google Scholar 

  13. Degner JF, Marioni JC, Pai AA, Pickrell JK, Nkadori E, Gilad Y et al (2009) Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics 25(24):3207–3212

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E et al (2010) Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464(7289):768–772

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lee MP (2012) Allele-specific gene expression and epigenetic modifications and their application to understanding inheritance and cancer. Biochim Biophys Acta 1819(7):739–742

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tuch BB, Laborde RR, Xu X, Gu J, Chung CB, Monighetti CK et al (2010) Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations. PLoS One 5(2):e9317

    Article  PubMed  PubMed Central  Google Scholar 

  17. Ha G, Roth A, Lai D, Bashashati A, Ding J, Goya R et al (2012) Integrative analysis of genome-wide loss of heterozygosity and monoallelic expression at nucleotide resolution reveals disrupted pathways in triple-negative breast cancer. Genome Res 22(10):1995–2007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Rozowsky J, Abyzov A, Wang J, Alves P, Raha D, Harmanci A et al (2011) AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol Syst Biol 7:522

    Article  PubMed  PubMed Central  Google Scholar 

  19. Mayba O, Gilbert HN, Liu J, Haverty PM, Jhunjhunwala S, Jiang Z et al (2014) MBASED: allele-specific expression detection in cancer tissues and cell lines. Genome Biol 15(8):405 http://genomebiology.com/2014/15/8/405

    Article  PubMed  PubMed Central  Google Scholar 

  20. Skelly DA, Johansson M, Madeoy J, Wakefield J, Akey JM (2011) A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data. Genome Res 21(10):1728–1737

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wei Y, Li X, Wang Q, Ji H (2012) iASeq: integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets. BMC Genomics 13:681

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Pandey RV, Franssen SU, Futschik A, Schlötterer C (2013) Allelic imbalance metre (Allim), a new tool for measuring allele-specific gene expression with RNA-seq data. Mol Ecol Resour 13(4):740–745

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Romanel A, Lago S, Prandi D, Sboner A, Demichelis F (2015) ASEQ: fast allele-specific studies from next-generation sequencing data. BMC Med Genet 8:9

    Google Scholar 

  24. Beltran H, Eng K, Mosquera JM, Sigaras A, Romanel A, Rennert H et al (2015) Whole-exome sequencing of metastatic cancer and biomarkers of treatment response. JAMA Oncol 1(4):466

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15):2114–2120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14):1754–1760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al (2009) 1000 genome project data processing subgroup. The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078–2079

    Article  PubMed  PubMed Central  Google Scholar 

  28. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A et al (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20(9):1297–1303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 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):R36

    Article  PubMed  PubMed Central  Google Scholar 

  30. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Van Baren MJ et al (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28(5):511–515

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Boeva V, Popova T, Bleakley K, Chiche P, Cappo J, Schleiermacher G et al (2012) Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics 28(3):423–425

    Article  CAS  PubMed  Google Scholar 

  32. Amarasinghe KC, Li J, Halgamuge SK (2013) CoNVEX: copy number variation estimation in exome sequencing data using HMM. BMC Bioinformatics 14(Suppl 2):S2

    Article  PubMed  PubMed Central  Google Scholar 

  33. Magi A, Tattini L, Cifola I, D’Aurizio R, Benelli M, Mangano E et al (2013) EXCAVATOR: detecting copy number variants from whole-exome sequencing data. Genome Biol 14(10):R120

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chiang DY, Getz G, Jaffe DB, O’Kelly MJT, Zhao X, Carter SL et al (2009) High-resolution mapping of copy-number alterations with massively parallel sequencing. Nat Methods 6(1):99–103

    Article  CAS  PubMed  Google Scholar 

  35. Xi R, Hadjipanayis AG, Luquette LJ, Kim T-M, Lee E, Zhang J et al (2011) Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion. Proc Natl Acad Sci U S A 108(46):E1128–E1136

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Olshen AB, Venkatraman ES, Lucito R, Wigler M (2004) Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5(4):557–572

    Article  PubMed  Google Scholar 

  37. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26(6):841–842

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Su X, Zhang L, Zhang J, Meric-Bernstam F, Weinstein JN (2012) PurityEst: estimating purity of human tumor samples using next-generation sequencing data. Bioinformatics 28(17):2265–2266

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W et al (2013) Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 4:2612 http://www.nature.com/doifinder/10.1038/ncomms3612

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Centre for Integrative Biology (CIBIO), University of Trento, Trento, Italy

    Alessandro Romanel

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  1. Alessandro Romanel
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Correspondence to Alessandro Romanel .

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Editors and Affiliations

  1. Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    Alexander Krasnitz

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Romanel, A. (2019). Allele-Specific Expression Analysis in Cancer Using Next-Generation Sequencing Data. In: Krasnitz, A. (eds) Cancer Bioinformatics. Methods in Molecular Biology, vol 1878. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8868-6_7

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  • DOI: https://doi.org/10.1007/978-1-4939-8868-6_7

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8866-2

  • Online ISBN: 978-1-4939-8868-6

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