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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 Poisson model (purple) at different sequencing depths (10x, 20x, 30x and 40x) for the simulation datasets ofArabidopsis thaliana. The mixed Poisson model well fits the observed distribution especially at higher sequencing coverages. The depth threshold for genotyping is indicated by a dashed line.
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Figure 2: Observed distribution of cluster depth (black) and the fitted mixed Poisson model (purple) at different sequencing depths (10x, 20x, 30x and 40x) for the simulation datasets ofArabidopsis thaliana. The mixed Poisson model well fits the observed distribution especially at higher sequencing coverages. The depth threshold for genotyping is indicated by a dashed line.

Mentions: However, in the absence of a reference genome, reference sites have to be established first from a large number of short reads before calling SNPs, which is usually carried out through the read-clustering approach [4]. When short reads are assembled into clusters, reads derived from repetitive genomic regions are usually unavoidably clustered together (i.e., forming composite clusters) due to high sequence similarity. In such a scenario, false SNPs could arise and be miscalled from composite clusters (Figure 1). Theoretically, the distribution of read depth of composite clusters should show a repeating pattern occurring at multiples of the average sequencing depth (C) (as shown in Figure 2), which corresponds to the copy number variations of repetitive sites. Therefore, in the read-clustering approach, the read depth k for each cluster approximately follows the mixed Poisson distribution due to the existence of composite clusters:


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 Poisson model (purple) at different sequencing depths (10x, 20x, 30x and 40x) for the simulation datasets ofArabidopsis thaliana. The mixed Poisson model well fits the observed distribution especially at higher sequencing coverages. The depth threshold for 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 2: Observed distribution of cluster depth (black) and the fitted mixed Poisson model (purple) at different sequencing depths (10x, 20x, 30x and 40x) for the simulation datasets ofArabidopsis thaliana. The mixed Poisson model well fits the observed distribution especially at higher sequencing coverages. The depth threshold for genotyping is indicated by a dashed line.
Mentions: However, in the absence of a reference genome, reference sites have to be established first from a large number of short reads before calling SNPs, which is usually carried out through the read-clustering approach [4]. When short reads are assembled into clusters, reads derived from repetitive genomic regions are usually unavoidably clustered together (i.e., forming composite clusters) due to high sequence similarity. In such a scenario, false SNPs could arise and be miscalled from composite clusters (Figure 1). Theoretically, the distribution of read depth of composite clusters should show a repeating pattern occurring at multiples of the average sequencing depth (C) (as shown in Figure 2), which corresponds to the copy number variations of repetitive sites. Therefore, in the read-clustering approach, the read depth k for each cluster approximately follows the mixed Poisson distribution due to the existence of composite clusters:

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