Skip to main content
SpringerLink
Log in

A Computational Protocol for Detecting Somatic Mutations by Integrating DNA and RNA Sequencing

  • Protocol
  • First Online:
Cancer Bioinformatics

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

  • 2700 Accesses

Abstract

Somatic mutation detection is a fundamental component of cancer genome research and of the molecular diagnosis of patients’ tumors. Traditionally, such efforts have focused on either DNA exome or whole genome sequencing; however, we recently have demonstrated that integrating multiple sequencing technologies provides increased statistical power to detect mutations, particularly in low-purity tumors upon the addition of RNA sequencing to DNA exome sequencing. The computational protocol described here enables an investigator to detect somatic mutations through integrating DNA and RNA sequencing from patient-matched tumor DNA, tumor RNA, and germline specimens via the open source software, UNCeqR.

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

Access this chapter

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

Institutional subscriptions

References

  1. Roychowdhury S, Chinnaiyan AM (2016) Translating cancer genomes and transcriptomes for precision oncology. CA Cancer J Clin 66:75–88

    Article  Google Scholar 

  2. Roychowdhury S, Iyer MK, Robinson DR, Lonigro RJ, Wu YM, Cao X, Kalyana-Sundaram S, Sam L, Balbin OA, Quist MJ et al (2011) Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci Transl Med 3:111ra121

    Article  Google Scholar 

  3. Clinical Lung Cancer Genome P, Network Genomic M (2013) A genomics-based classification of human lung tumors. Sci Transl Med 5:209ra153

    Google Scholar 

  4. Meyerson M, Gabriel S, Getz G (2010) Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 11:685–696

    Article  CAS  Google Scholar 

  5. Wilkerson MD, Cabanski CR, Sun W, Hoadley KA, Walter V, Mose LE, Troester MA, Hammerman PS, Parker JS, Perou CM, Hayes DN (2014) Integrated RNA and DNA sequencing improves mutation detection in low purity tumors. Nucleic Acids Res 42:e107

    Article  CAS  Google Scholar 

  6. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  8. Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, He X, Mieczkowski P, Grimm SA, Perou CM et al (2010) MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 38:e178

    Article  Google Scholar 

  9. Troester MA, Hoadley KA, D’Arcy M, Cherniack AD, Stewart C, Koboldt DC, Robertson AG, Mahurkar S, Shen H, Wilkerson MD et al (2016) DNA defects, epigenetics, and gene expression in cancer-adjacent breast: a study from The Cancer Genome Atlas. NPJ Breast Cancer 2:16007

    Article  Google Scholar 

  10. Mertins P, Mani DR, Ruggles KV, Gillette MA, Clauser KR, Wang P, Wang X, Qiao JW, Cao S, Petralia F et al (2016) Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534:55–62

    Article  CAS  Google Scholar 

  11. Liu W, Snell JM, Jeck WR, Hoadley KA, Wilkerson MD, Parker JS, Patel N, Mlombe YB, Mulima G, Liomba NG et al (2016) Subtyping sub-Saharan esophageal squamous cell carcinoma by comprehensive molecular analysis. JCI Insight 1(16):e88755

    Google Scholar 

  12. Fishbein L, Leshchiner I, Walter V, Danilova L, Robertson AG, Johnson AR, Lichtenberg TM, Murray BA, Ghayee HK, Else T et al (2017) Comprehensive molecular characterization of pheochromocytoma and paraganglioma. Cancer Cell 31:181–193

    Article  CAS  Google Scholar 

  13. Ciriello G, Gatza ML, Beck AH, Wilkerson MD, Rhie SK, Pastore A, Zhang H, McLellan M, Yau C, Kandoth C et al (2015) Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163:506–519

    Article  CAS  Google Scholar 

  14. The Cancer Genome Atlas Research Network (2014) Comprehensive molecular profiling of lung adenocarcinoma. Nature 511:543–550

    Article  Google Scholar 

  15. The Cancer Genome Atlas Research Network (2012) Comprehensive genomic characterization of squamous cell lung cancers. Nature 489:519–525

    Article  Google Scholar 

  16. The Cancer Genome Atlas Research Network (2015) Genomic classification of cutaneous melanoma. Cell 161:1681–1696

    Article  Google Scholar 

  17. The Cancer Genome Atlas Research Network (2015) Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517:576–582

    Article  Google Scholar 

  18. Brady SW, McQuerry JA, Qiao Y, Piccolo SR, Shrestha G, Jenkins DF, Layer RM, Pedersen BS, Miller RH, Esch A et al (2017) Combating subclonal evolution of resistant cancer phenotypes. Nat Commun 8:1231

    Article  Google Scholar 

  19. Field MG, Durante MA, Anbunathan H, Cai LZ, Decatur CL, Bowcock AM, Kurtenbach S, Harbour JW (2018) Punctuated evolution of canonical genomic aberrations in uveal melanoma. Nat Commun 9:116

    Article  Google Scholar 

  20. Yee T (2008) The {VGAM} package. R News 8:28–39

    Google Scholar 

  21. Gene Annotation File. https://api.gdc.cancer.gov/v0/data/a0bb9765-3f03-485b-839d-7dce4a9bcfeb

  22. Karolchik D, Barber GP, Casper J, Clawson H, Cline MS, Diekhans M, Dreszer TR, Fujita PA, Guruvadoo L, Haeussler M et al (2014) The UCSC genome browser database: 2014 update. Nucleic Acids Res 42:D764–D770

    Article  CAS  Google Scholar 

  23. Zook JM, Chapman B, Wang J, Mittelman D, Hofmann O, Hide W, Salit M (2014) Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat Biotechnol 32(3):246–251

    Article  CAS  Google Scholar 

  24. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29:308–311

    Article  CAS  Google Scholar 

  25. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB et al (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature 536:285–291

    Article  CAS  Google Scholar 

  26. Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, Ding M, Bamford S, Cole C, Ward S et al (2015) COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res 43:D805–D811

    Article  CAS  Google Scholar 

  27. Cabanski CR, Wilkerson MD, Soloway M, Parker JS, Liu J, Prins JF, Marron JS, Perou CM, Hayes DN (2013) BlackOPs: increasing confidence in variant detection through mappability filtering. Nucleic Acids Res 41:e178

    Article  CAS  Google Scholar 

  28. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31:213–219

    Article  CAS  Google Scholar 

  29. Pickrell JK, Gilad Y, Pritchard JK (2012) Comment on “Widespread RNA and DNA sequence differences in the human transcriptome”. Science 335:1302 author reply 1302

    Article  CAS  Google Scholar 

  30. Guo Y, Li J, Li CI, Long J, Samuels DC, Shyr Y (2012) The effect of strand bias in Illumina short-read sequencing data. BMC Genomics 13:666

    Article  CAS  Google Scholar 

  31. Jiang Y, Turinsky AL, Brudno M (2015) The missing indels: an estimate of indel variation in a human genome and analysis of factors that impede detection. Nucleic Acids Res 43:7217–7228

    Article  CAS  Google Scholar 

  32. Mose LE, Wilkerson MD, Hayes DN, Perou CM, Parker JS (2014) ABRA: improved coding indel detection via assembly-based realignment. Bioinformatics 30:2813–2815

    Article  CAS  Google Scholar 

  33. Stouffer SA, Suchman EA, DeVinney LC, Star SA, Williams RM (1949) Studies in social psychology in World War II. Princeton University Press, Princeton, NJ

    Google Scholar 

  34. Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38:e164

    Article  Google Scholar 

  35. Obenchain V, Lawrence M, Carey V, Gogarten S, Shannon P, Morgan M (2014) VariantAnnotation: a Bioconductor package for exploration and annotation of genetic variants. Bioinformatics 30:2076–2078

    Article  CAS  Google Scholar 

  36. Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, Carter SL, Stewart C, Mermel CH, Roberts SA et al (2013) Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499:214–218

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Collaborative Health Initiative Research Program, The American Genome Center, Department of Anatomy, Physiology and Genetics, Uniformed Services University, Bethesda, MD, USA

    Matthew D. Wilkerson

Authors
  1. Matthew D. Wilkerson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthew D. Wilkerson .

Editor information

Editors and Affiliations

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

    Alexander Krasnitz

Rights and permissions

Reprints and permissions

Copyright information

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

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Wilkerson, M.D. (2019). A Computational Protocol for Detecting Somatic Mutations by Integrating DNA and RNA Sequencing. 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_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8868-6_6

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8866-2

  • Online ISBN: 978-1-4939-8868-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation