SNPest: a probabilistic graphical model for estimating genotypes

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• 作者：Stinus Lindgreen (11) (12) (13)
  Anders Krogh (11) (12)
  Jakob Skou Pedersen (11) (14)
  
  11. Section for Computational and RNA Biology ; Department of Biology ; University of Copenhagen ; Ole Maaloes Vej ; 2200 ; Copenhagen ; Denmark
  12. Center of Excellence for GeoGenetics ; Natural History Museum of Denmark and Department of Biology ; University of Copenhagen ; Oester Voldgade 5-7 ; 1350 ; Copenhagen K ; Denmark
  13. Biomolecular Interaction Centre ; School of Biological Sciences ; University of Canterbury ; Private Bag 4800 ; 8041 ; Christchurch ; New Zealand
  14. Department of Molecular Medicine ; Aarhus University Hospital ; Skejby ; Brendstrupgaardsvej 100 ; DK-8200 ; Aarhus N ; Denmark
  
• 关键词：Next ; generation sequencing ; SNP ; Genotyping ; Illumina ; Ancient DNA
• 刊名：BMC Research Notes
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：7
• 期：1
• 全文大小：457 KB
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• 刊物主题：Biomedicine general; Medicine/Public Health, general; Life Sciences, general;
• 出版者：BioMed Central
• ISSN：1756-0500

文摘

Background As the use of next-generation sequencing technologies is becoming more widespread, the need for robust software to help with the analysis is growing as well. A key challenge when analyzing sequencing data is the prediction of genotypes from the reads, i.e. correct inference of the underlying DNA sequences that gave rise to the sequenced fragments. For diploid organisms, the genotyper should be able to predict both alleles in the individual. Variations between the individual and the population can then be analyzed by looking for SNPs (single nucleotide polymorphisms) in order to investigate diseases or phenotypic features. To perform robust and high confidence genotyping and SNP calling, methods are needed that take the technology specific limitations into account and can model different sources of error. As an example, ancient DNA poses special challenges as the data is often shallow and subject to errors induced by post mortem damage. Findings We present a novel approach to the genotyping problem where a probabilistic framework describing the process from sampling to sequencing is implemented as a graphical model. This makes it possible to model technology specific errors and other sources of variation that can affect the result. The inferred genotype is given a posterior probability to signify the confidence in the result. SNPest has already been used to genotype large scale projects such as the first ancient human genome published in 2010. Conclusions We compare the performance of SNPest to a number of other widely used genotypers on both real and simulated data, covering both haploid and diploid genomes. We investigate the effects of read depth, of removing adapters before mapping and genotyping, of using different mapping tools, and of using the correct model in the genotyping process. We show that the performance of SNPest is comparable to existing methods, and we also illustrate cases where SNPest has an advantage over other methods, e.g. when dealing with simulated ancient DNA.