Furthermore, conflicting genotypes between two algorithms showed a systematic bias in that one caller almost exclusively assigned heterozygotes, while the other one almost exclusively assigned homozygotes. Of all conflicting genotype calls, CLC was only correct in 17% of the cases. Genotype validations revealed that the Unified Genotyper of the Genome Analysis Toolkit and SAMtools performed significantly better than a caller from CLC Genomics Workbench (CLC). We obtained substantially different SNP datasets depending on the SNP caller. SNPs and genotypes were called using three different algorithms. We sequenced ~1% of the orangutan genome with 41-fold median coverage in 31 wild-born individuals from two populations. Our modifications facilitate generation of single-sample libraries, enabling individual genotype assignments instead of pooled-sample analysis. We present an improved RRL (iRRL) protocol that maximizes the generation of homologous DNA sequences, thus achieving improved genotyping-by-sequencing efficiency. 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. In the laboratory, current protocols require improvements with regards to sequencing homologous fragments to reduce the number of missing genotypes. Yet, generating such datasets remains challenging due to laboratory and bioinformatical issues. 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. investigating only parts of the genome, is reduced-representation library (RRL) sequencing. One approach to reduce genome complexity, i.e. High-throughput sequencing has opened up exciting possibilities in population and conservation genetics by enabling the assessment of genetic variation at genome-wide scales.
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