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A Bioinformatic Pipeline to Identify Biomarkers for Metastasis Formation from RNA Sequencing Data

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Metastasis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2294))

Abstract

Deep molecular characterization of tumors is a prerequisite for precision oncology and personalized anticancer treatment. Analyzing the tumor transcriptome by RNA sequencing (RNAseq) allows the quantification of individual isoforms and also the detection of sequence alteration in the expressed genes. This chapter describes an analysis pipeline that focuses both on accurate quantification of transcripts and on the occurrence of cancer-associated mutations. Another section introduces the analysis of differentially expressed genes for biomarker evaluation on the example of comparing metastasized versus non-metastasized colorectal tumors.

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References

  1. Ramaswamy S, Ross KN, Lander ES et al (2003) A molecular signature of metastasis in primary solid tumors. Nat Genet 33:49–54

    Article  CAS  Google Scholar 

  2. Byron SA, Van Keuren-Jensen KR, Engelthaler DM et al (2016) Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet 17:257–271

    Article  CAS  Google Scholar 

  3. Bild AH, Yao G, Chang JT et al (2006) Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439:353–357

    Article  CAS  Google Scholar 

  4. Robinson DR, Wu Y-M, Lonigro RJ et al (2017) Integrative clinical genomics of metastatic cancer. Nature 548:297–303

    Article  CAS  Google Scholar 

  5. Choi J, Park S, Yoon Y et al (2017) Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers. Bioinformatics 33:3619–3626

    Article  CAS  Google Scholar 

  6. Yang L, Lee M-S, Lu H et al (2016) Analyzing somatic genome rearrangements in human cancers by using whole-exome sequencing. Am J Hum Genet 98:843–856

    Article  CAS  Google Scholar 

  7. Hutchins G, Southward K, Handley K et al (2011) Value of mismatch repair, KRAS, and BRAF mutations in predicting recurrence and benefits from chemotherapy in colorectal cancer. J Clin Oncol 29:1261–1270

    Article  Google Scholar 

  8. Conesa A, Madrigal P, Tarazona S et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13

    Article  Google Scholar 

  9. Baruzzo G, Hayer KE, Kim EJ et al (2017) Simulation-based comprehensive benchmarking of RNA-seq aligners. Nat Methods 14:135–139

    Article  CAS  Google Scholar 

  10. Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21

    Article  CAS  Google Scholar 

  11. Zhang C, Zhang B, Lin L-L et al (2017) Evaluation and comparison of computational tools for RNA-seq isoform quantification. BMC Genomics 18:583

    Article  Google Scholar 

  12. Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323

    Article  CAS  Google Scholar 

  13. Piskol R, Ramaswami G, Li JB (2013) Reliable identification of genomic variants from RNA-seq data. Am J Hum Genet 93:641–651

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  15. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550

    Article  Google Scholar 

  16. Trivedi UH, Cézard T, Bridgett S et al (2014) Quality control of next-generation sequencing data without a reference. Front Genet 5:111

    Article  Google Scholar 

  17. Chen S, Zhou Y, Chen Y et al (2018) Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884–i890

    Article  Google Scholar 

  18. Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079

    Article  Google Scholar 

  19. R Core Team (2019) R: A Language and Environment for Statistical Computing

    Google Scholar 

  20. Morgan M (2019) BiocManager: access the bioconductor project package repository R package version 1.30.10. https://CRAN.R-project.org/package=BiocManager

  21. McLaren W, Gil L, Hunt SE et al (2016) The Ensembl variant effect predictor. Genome Biol 17:122

    Article  Google Scholar 

  22. Adzhubei I, Jordan DM, Sunyaev SR (2013) Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. Chapter 7:Unit7.20

    Google Scholar 

  23. Vaser R, Adusumalli S, Leng SN et al (2016) SIFT missense predictions for genomes. Nat Protoc 11:1–9

    Article  CAS  Google Scholar 

  24. Stephens M, Carbonetto P, Gerard D, et al (2020) ashr: Methods for Adaptive Shrinkage, using Empirical Bayes R package version 2.2-47. https://CRAN.R-project.org/package=ashr

  25. Durinck S, Moreau Y, Kasprzyk A et al (2005) BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21:3439–3440

    Article  CAS  Google Scholar 

  26. Durinck S, Spellman PT, Birney E et al (2009) Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc 4:1184–1191

    Article  CAS  Google Scholar 

  27. Wickham H (2019) stringr: Simple, Consistent Wrappers for Common String Operations R package version 1.4.0. https://CRAN.R-project.org/package=stringr

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Correspondence to Mathias Dahlmann .

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Dahlmann, M., Stein, U.S. (2021). A Bioinformatic Pipeline to Identify Biomarkers for Metastasis Formation from RNA Sequencing Data. In: Stein, U.S. (eds) Metastasis. Methods in Molecular Biology, vol 2294. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1350-4_16

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  • DOI: https://doi.org/10.1007/978-1-0716-1350-4_16

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1349-8

  • Online ISBN: 978-1-0716-1350-4

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