Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms

• Author(s): Greminger, Maja P ; Stölting, Kai N ; Nater, Alexander ; Goossens, Benoit ; Arora, Natasha ; Bruggmann, Rémy ; Patrignani, Andrea ; Nussberger, Beatrice ; Sharma, Reeta ; Kraus, Robert H S ; Ambu, Laurentius N ; Singleton, Ian ; Chikhi, Lounes ; van Schaik, Carel P ; Krützen, Michael
• Date: 2014
• Persistent ID: http://hdl.handle.net/10400.7/347
• Origin: ARCA - Access to Research and Communication Annals
• Subject(s): Next-generation sequencing; Single-nucleotide polymorphisms; Reduced-representation libraries; Bioinformatics; GATK; SAMtools; CLC genomics workbench; Great apes
• Document Type: Journal article
• Language: English
• Contributor(s): ARCA

Description

High-throughput sequencing has opened up exciting possibilities in population and conservation genetics by enabling the assessment of genetic variation at genome-wide scales. One approach to reduce genome complexity, i.e. investigating only parts of the genome, is reduced-representation library (RRL) sequencing. Like similar approaches, RRL sequencing reduces ascertainment bias due to simultaneous discovery and genotyping of single-nucleotide polymorphisms (SNPs) and does not require reference genomes. Yet, generating such datasets remains challenging due to laboratory and bioinformatical issues. In the laboratory, current protocols require improvements with regards to sequencing homologous fragments to reduce the number of missing genotypes. From the bioinformatical perspective, the reliance of most studies on a single SNP caller disregards the possibility that different algorithms may produce disparate SNP datasets.