Using SAAS-CNV to Detect and Characterize Somatic Copy Number Alterations in Cancer Genomes from Next Generation Sequencing and SNP Array Data

Zhongyang Zhang; Ke Hao

doi:10.1007/978-1-4939-8666-8_2

Using SAAS-CNV to Detect and Characterize Somatic Copy Number Alterations in Cancer Genomes from Next Generation Sequencing and SNP Array Data

Methods Mol Biol. 2018:1833:29-47. doi: 10.1007/978-1-4939-8666-8_2.

Authors

Zhongyang Zhang¹, Ke Hao²

Affiliations

¹ Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
² Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. ke.hao@mssm.edu.

PMID: 30039361
DOI: 10.1007/978-1-4939-8666-8_2

Abstract

Somatic copy number alterations (SCNAs) are profound in cancer genomes at different stages: oncogenesis, progression, and metastasis. Accurate detection and characterization of SCNA landscape at genome-wide scale are of great importance. Next-generation sequencing and SNP array are current technology of choice for SCNA analysis. They are able to quantify SCNA with high resolution and meanwhile raise great challenges in data analysis. To this end, we have developed an R package saasCNV for SCNA analysis using (1) whole-genome sequencing (WGS), (2) whole-exome sequencing (WES) or (3) whole-genome SNP array data. In this chapter, we provide the features of the package and step-by-step instructions in detail.

Keywords: Cancer genome; Copy number variation; Next-generation sequencing; SAAS-CNV; SNP array; Segmentation; Somatic copy number alteration; Whole-exome sequencing; Whole-genome sequencing.

Publication types

Review

MeSH terms

Animals
Exome*
Gene Dosage*
Genome, Human*
High-Throughput Nucleotide Sequencing*
Humans
Neoplasms / genetics*
Software*