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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

Comparison of the performance ofde novoSNP calling approaches based on the real sequencing datasets ofArabidopsis thaliana(A) andGasterosteus aculeatus(B). FPR/FNR, false positive or negative rate.
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Figure 5: Comparison of the performance ofde novoSNP calling approaches based on the real sequencing datasets ofArabidopsis thaliana(A) andGasterosteus aculeatus(B). FPR/FNR, false positive or negative rate.

Mentions: As expected, iML still generated lower FPRs than ML with approximately 17% FPR reduction at different sequencing depths for A. thaliana (Figure 5A), and 4% (50 bp) ~7% (30 bp) FPR reduction at different read lengths for G. aculeatus (Figure 5B). The performance of iML was less pronounced on the G. aculeatus dataset because this dataset contained much less repetitive restriction sites than the A. thaliana dataset (Figure 4). In comparison with the simulation analysis, iML coupled with the mixed normal model is relatively less efficient at distinguishing composite clusters from unique ones on the real sequencing data, as reflected by the observation of substantially high FPR and FNR remained in real datasets even at the deep sequencing coverages (Figure 5A). Nevertheless, iML still outperformed the original ML algorithm in terms of genotyping accuracy on the real sequencing datasets, and therefore represents the most promising algorithm currently available for accurate de novo SNP genotyping in diploid genomes with high repeat contents.


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)

Comparison of the performance ofde novoSNP calling approaches based on the real sequencing datasets ofArabidopsis thaliana(A) andGasterosteus aculeatus(B). FPR/FNR, false positive or negative rate.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Comparison of the performance ofde novoSNP calling approaches based on the real sequencing datasets ofArabidopsis thaliana(A) andGasterosteus aculeatus(B). FPR/FNR, false positive or negative rate.
Mentions: As expected, iML still generated lower FPRs than ML with approximately 17% FPR reduction at different sequencing depths for A. thaliana (Figure 5A), and 4% (50 bp) ~7% (30 bp) FPR reduction at different read lengths for G. aculeatus (Figure 5B). The performance of iML was less pronounced on the G. aculeatus dataset because this dataset contained much less repetitive restriction sites than the A. thaliana dataset (Figure 4). In comparison with the simulation analysis, iML coupled with the mixed normal model is relatively less efficient at distinguishing composite clusters from unique ones on the real sequencing data, as reflected by the observation of substantially high FPR and FNR remained in real datasets even at the deep sequencing coverages (Figure 5A). Nevertheless, iML still outperformed the original ML algorithm in terms of genotyping accuracy on the real sequencing datasets, and therefore represents the most promising algorithm currently available for accurate de novo SNP genotyping in diploid genomes with high repeat contents.

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