Bioinformatics data analysis of next-generation sequencing data from heterogeneous tumor samples

Sean R. Landman, Tae Hyun Hwang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Tumor heterogeneity is a major challenge when it comes to treating cancer and also complicates research aimed at determining genetic sources for tumorigenesis. Leveraging high-throughput sequencing technology has been an effective approach for advancing our understanding of genetic diseases, and this type of data can also be used to better understand and make inferences about tumor heterogeneity. Here we describe the basics of genomics data analysis, as well as analysis pipelines for investigating tumor heterogeneity with next-generation sequencing data.

Original languageEnglish (US)
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages185-192
Number of pages8
Volume1633
DOIs
Publication statusPublished - 2017

Publication series

NameMethods in Molecular Biology
Volume1633
ISSN (Print)1064-3745

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Keywords

  • Bioinformatics
  • Genomics
  • Next-generation sequencing
  • Structural variation
  • Subclone
  • Subpopulation
  • Tumor heterogeneity

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

Cite this

Landman, S. R., & Hwang, T. H. (2017). Bioinformatics data analysis of next-generation sequencing data from heterogeneous tumor samples. In Methods in Molecular Biology (Vol. 1633, pp. 185-192). (Methods in Molecular Biology; Vol. 1633). Humana Press Inc.. https://doi.org/10.1007/978-1-4939-7142-8_12