Variant calling using NGS data in European aspen (Populus tremula)
2015 (English)In: Advances in the Understanding of Biological Sciences Using Next Generation Sequencing (NGS) Approaches / [ed] Sablok, G., Kumar, S., Ueno, S., Kuo, J., Varotto, C. (Eds.), Springer, 2015, 1, 43-61 p.Chapter in book (Refereed)
Analysis of next-generation sequencing (NGS) data is rapidly becoming an important source of information for genetics and genomics studies. The utility of such data does, however, rely crucially on the accuracy and quality of SNP and genotype calling. Identification of genetic variants (SNPs and short indels) from NGS data is an area of active research and many recent statistical methods have been developed to both improve and quantify the large uncertainty associated with genotype calling. The detection of genetic variants from NGS data is prone to errors, due to multiple factors such as base-calling, alignment errors and read coverage. Here we highlight some of the issues and review and exemplify some of the recent methods that have been developed for genotype calling. We also provide guidelines for their application to whole-genome re-sequencing data using a data set based on a number of European aspen (Populus tremula) individuals each sequenced to a depth of about 20x coverage per individual.
Place, publisher, year, edition, pages
Springer, 2015, 1. 43-61 p.
Next generation sequencing, variant calling, single nucleotide polymorphism (SNP), pre-processing, alignment, post-processing, filtering
IdentifiersURN: urn:nbn:se:umu:diva-102803DOI: 10.1007/978-3-319-17157-9_4ISBN: 978-3-319-17156-2OAI: oai:DiVA.org:umu-102803DiVA: diva2:809757