15. Identification of Nucleotide Variation in Genomes Using Next-Generation Sequencing

  1. Dr. Matthias Harbers2,3 and
  2. Prof. Dr. Günter Kahl4,5,6
  1. Hendrik-Jan Megens and
  2. Martien A. M. Groenen

Published Online: 23 JAN 2012

DOI: 10.1002/9783527644582.ch15

Tag-Based Next Generation Sequencing

Tag-Based Next Generation Sequencing

How to Cite

Megens, H.-J. and Groenen, M. A. M. (2011) Identification of Nucleotide Variation in Genomes Using Next-Generation Sequencing, in Tag-Based Next Generation Sequencing (eds M. Harbers and G. Kahl), Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany. doi: 10.1002/9783527644582.ch15

Editor Information

  1. 2

    4-2-6 Nishihara, Kashiwa-Shi, Chiba 277-0885, Japan

  2. 3

    DNAFORM Inc., Leading Venture Plaza 2, 75-1 Ono-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0046, Japan

  3. 4

    Mohrmühlgasse 3, 63500 Seligenstadt, Germany

  4. 5

    University of Frankfurt am Main Biocenter, Max-von-Lauestraße 9, 60439 Frankfurt am Main, Germany

  5. 6

    Frankfurt Biotechnology Innovation Center (FIZ), GenXPro Ltd, Altenhöferallee 3, 60438 Frankfurt am Main, Germany

Author Information

  1. Wageningen University, Animal Breeding and Genomics Center, Marijkeweg 40, 6709 PG Wageningen, The Netherlands

Publication History

  1. Published Online: 23 JAN 2012
  2. Published Print: 14 DEC 2011

ISBN Information

Print ISBN: 9783527328192

Online ISBN: 9783527644582



  • nucleotide variation;
  • next-generation sequencing;
  • SNP;
  • nucleotide variation assessment;
  • methods


Discovery of genome-wide variation has taken a huge leap forward with the introduction of next-generation sequencing (NGS) technology. Variant discovery requires sampling of a number of haplotypes. This can be either the two haplotypes of a diploid organism or multiple haplotypes in a population. Variant discovery can be done by sequencing pooled DNA and NGS makes it cost-effective to sample many haplotypes. In this chapter, we discuss various sequencing strategies for variation discovery, focusing mainly on single nucleotide polymorphisms, and to a lesser extent on short insertion/deletions (Indels). We discuss different options, such as specific library construction and the amount of sequencing required to meet a certain objective. While the benefits of NGS to their own research may be obvious to many researchers, the main obstacle for applying NGS is often practical – how to manipulate and analyze the amount of data from a typical NGS run. The methods therefore focus on practical considerations dealing with large NGS datasets, such as sequence processing and filtering, mapping to a reference genome, and variant calling. In addition, we focus on data standards and tools to manipulate and analyze data from such standardized datasets. By providing examples and links to easy-to-implement scripts and software, we hope to lower the threshold for biologists to further explore the wealth of information that can be obtained by these new molecular resources.