@inbook{59ce71363aca4d94a7776b9f158231ec,
title = "Bioinformatics data analysis of next-generation sequencing data from heterogeneous tumor samples",
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.",
keywords = "Bioinformatics, Genomics, Next-generation sequencing, Structural variation, Subclone, Subpopulation, Tumor heterogeneity",
author = "Landman, {Sean R.} and Hwang, {Tae Hyun}",
note = "Publisher Copyright: {\textcopyright} 2017, Springer Science+Business Media LLC.",
year = "2017",
doi = "10.1007/978-1-4939-7142-8_12",
language = "English (US)",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "185--192",
booktitle = "Methods in Molecular Biology",
}