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

1 Scopus citations

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
DOIs
StatePublished - 2017

Publication series

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

Keywords

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

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

Fingerprint

Dive into the research topics of 'Bioinformatics data analysis of next-generation sequencing data from heterogeneous tumor samples'. Together they form a unique fingerprint.

Cite this