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Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions.

Dou J, Zhao X, Fu X, Jiao W, Wang N, Zhang L, Hu X, Wang S, Bao Z - Biol. Direct (2012)

Bottom Line: The iML algorithm incorporates the mixed Poisson/normal model to detect composite read clusters and can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions.The iML algorithm can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions, and thus outperforms the original ML algorithm by achieving much higher genotyping accuracy.Our algorithm is therefore very useful for accurate de novo SNP genotyping in the non-model organisms without a reference genome.

View Article: PubMed Central - HTML - PubMed

Affiliation: Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, 5 Yushan Road, Qingdao, 266003, China.

ABSTRACT

Background: Single nucleotide polymorphisms (SNPs) are the most abundant type of genetic variation in eukaryotic genomes and have recently become the marker of choice in a wide variety of ecological and evolutionary studies. The advent of next-generation sequencing (NGS) technologies has made it possible to efficiently genotype a large number of SNPs in the non-model organisms with no or limited genomic resources. Most NGS-based genotyping methods require a reference genome to perform accurate SNP calling. Little effort, however, has yet been devoted to developing or improving algorithms for accurate SNP calling in the absence of a reference genome.

Results: Here we describe an improved maximum likelihood (ML) algorithm called iML, which can achieve high genotyping accuracy for SNP calling in the non-model organisms without a reference genome. The iML algorithm incorporates the mixed Poisson/normal model to detect composite read clusters and can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions. Through analysis of simulation and real sequencing datasets, we demonstrate that in comparison with ML or a threshold approach, iML can remarkably improve the accuracy of de novo SNP genotyping and is especially powerful for the reference-free genotyping in diploid genomes with high repeat contents.

Conclusions: The iML algorithm can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions, and thus outperforms the original ML algorithm by achieving much higher genotyping accuracy. Our algorithm is therefore very useful for accurate de novo SNP genotyping in the non-model organisms without a reference genome.

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Related in: MedlinePlus

Observed distribution of cluster depth (black) and the fitted mixed normal model (purple) for the real sequencing datasets ofArabidopsis thalianaandGasterosteus aculeatus. The depth threshold for iML genotyping is indicated by a dashed line
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Figure 4: Observed distribution of cluster depth (black) and the fitted mixed normal model (purple) for the real sequencing datasets ofArabidopsis thalianaandGasterosteus aculeatus. The depth threshold for iML genotyping is indicated by a dashed line

Mentions: In data simulation, we assume that read depth for each restriction site follows the Poisson distribution, which is, however, may not be fully valid for real datasets due to a few practical reasons such as uneven cutting efficiency across restriction sites, amplification bias, and sequencing artifact/error. Before implementing the iML algorithm, we first performed a model fitness test for four distribution models (Poisson, mixed Poisson, normal, and mixed normal) on the two real datasets (Table 2). It turned out that the mixed normal model best fit the observed distribution of cluster depth in both datasets (Table 2, Figure 4), suggesting that unlike in the simulation analysis, the mixed Poisson model may not be the model of choice for real datasets in practice. Therefore, the mixed normal model was selected to implement the iML algorithm on the real sequencing datasets.


Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions.

Dou J, Zhao X, Fu X, Jiao W, Wang N, Zhang L, Hu X, Wang S, Bao Z - Biol. Direct (2012)

Observed distribution of cluster depth (black) and the fitted mixed normal model (purple) for the real sequencing datasets ofArabidopsis thalianaandGasterosteus aculeatus. The depth threshold for iML genotyping is indicated by a dashed line
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472322&req=5

Figure 4: Observed distribution of cluster depth (black) and the fitted mixed normal model (purple) for the real sequencing datasets ofArabidopsis thalianaandGasterosteus aculeatus. The depth threshold for iML genotyping is indicated by a dashed line
Mentions: In data simulation, we assume that read depth for each restriction site follows the Poisson distribution, which is, however, may not be fully valid for real datasets due to a few practical reasons such as uneven cutting efficiency across restriction sites, amplification bias, and sequencing artifact/error. Before implementing the iML algorithm, we first performed a model fitness test for four distribution models (Poisson, mixed Poisson, normal, and mixed normal) on the two real datasets (Table 2). It turned out that the mixed normal model best fit the observed distribution of cluster depth in both datasets (Table 2, Figure 4), suggesting that unlike in the simulation analysis, the mixed Poisson model may not be the model of choice for real datasets in practice. Therefore, the mixed normal model was selected to implement the iML algorithm on the real sequencing datasets.

Bottom Line: The iML algorithm incorporates the mixed Poisson/normal model to detect composite read clusters and can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions.The iML algorithm can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions, and thus outperforms the original ML algorithm by achieving much higher genotyping accuracy.Our algorithm is therefore very useful for accurate de novo SNP genotyping in the non-model organisms without a reference genome.

View Article: PubMed Central - HTML - PubMed

Affiliation: Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, 5 Yushan Road, Qingdao, 266003, China.

ABSTRACT

Background: Single nucleotide polymorphisms (SNPs) are the most abundant type of genetic variation in eukaryotic genomes and have recently become the marker of choice in a wide variety of ecological and evolutionary studies. The advent of next-generation sequencing (NGS) technologies has made it possible to efficiently genotype a large number of SNPs in the non-model organisms with no or limited genomic resources. Most NGS-based genotyping methods require a reference genome to perform accurate SNP calling. Little effort, however, has yet been devoted to developing or improving algorithms for accurate SNP calling in the absence of a reference genome.

Results: Here we describe an improved maximum likelihood (ML) algorithm called iML, which can achieve high genotyping accuracy for SNP calling in the non-model organisms without a reference genome. The iML algorithm incorporates the mixed Poisson/normal model to detect composite read clusters and can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions. Through analysis of simulation and real sequencing datasets, we demonstrate that in comparison with ML or a threshold approach, iML can remarkably improve the accuracy of de novo SNP genotyping and is especially powerful for the reference-free genotyping in diploid genomes with high repeat contents.

Conclusions: The iML algorithm can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions, and thus outperforms the original ML algorithm by achieving much higher genotyping accuracy. Our algorithm is therefore very useful for accurate de novo SNP genotyping in the non-model organisms without a reference genome.

Show MeSH
Related in: MedlinePlus