Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions.
Biol Direct
; 7: 17, 2012 Jun 08.
Article
in En
| MEDLINE
| ID: mdl-22682067
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Software
/
Repetitive Sequences, Nucleic Acid
/
Genome, Plant
/
Polymorphism, Single Nucleotide
/
Genotyping Techniques
Type of study:
Diagnostic_studies
/
Prognostic_studies
Language:
En
Journal:
Biol Direct
Year:
2012
Document type:
Article
Affiliation country:
China