ALG: Automated Genotype Calling of Luminex Assays

Single nucleotide polymorphisms (SNPs) are the most commonly used polymorphic markers in genetics studies. Among the different platforms for SNP genotyping, Luminex is one of the less exploited mainly due to the lack of a robust (semi-automated and replicable) freely available genotype calling softw...

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Veröffentlicht in:PloS one Jg. 6; H. 5; S. e19368
Hauptverfasser: Bourgey, Mathieu, Lariviere, Mathieu, Richer, Chantal, Sinnett, Daniel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 06.05.2011
Public Library of Science (PLoS)
Schlagworte:
ABC transporters
Algorithms
Alleles
Analysis
Automation
Biological Assay - methods
Biology
Biomarkers
Child, Preschool
Children
Chromosomes
Cluster analysis
Clustering
Computer programs
Computer Science
Confidence intervals
Epidemiology
Female
Genetic aspects
Genetics
Genomes
Genotype
Genotypes
Genotyping
Graphical user interface
Haplotypes
Hospitals
Humans
Informatics
Leukemia
Low level
Male
Mathematics
Minority & ethnic groups
Parents
Physics
Polymorphism
Polymorphism, Single Nucleotide - genetics
Reproducibility
Robustness (mathematics)
Single nucleotide polymorphisms
Single-nucleotide polymorphism
Software
Technology application
User interface
Canada
Montreal Quebec Canada
Quebec Canada
ISSN:1932-6203, 1932-6203
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Zusammenfassung:Single nucleotide polymorphisms (SNPs) are the most commonly used polymorphic markers in genetics studies. Among the different platforms for SNP genotyping, Luminex is one of the less exploited mainly due to the lack of a robust (semi-automated and replicable) freely available genotype calling software. Here we describe a clustering algorithm that provides automated SNP calls for Luminex genotyping assays. We genotyped 3 SNPs in a cohort of 330 childhood leukemia patients, 200 parents of patient and 325 healthy individuals and used the Automated Luminex Genotyping (ALG) algorithm for SNP calling. ALG genotypes were called twice to test for reproducibility and were compared to sequencing data to test for accuracy. Globally, this analysis demonstrates the accuracy (99.6%) of the method, its reproducibility (99.8%) and the low level of no genotyping calls (3.4%). The high efficiency of the method proves that ALG is a suitable alternative to the current commercial software. ALG is semi-automated, and provides numerical measures of confidence for each SNP called, as well as an effective graphical plot. Moreover ALG can be used either through a graphical user interface, requiring no specific informatics knowledge, or through command line with access to the open source code. The ALG software has been implemented in R and is freely available for non-commercial use either at http://alg.sourceforge.net or by request to mathieu.bourgey@umontreal.ca.
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Implemented software: MB. Tested software: ML CR. Conceived and designed the experiments: MB ML CR DS. Performed the experiments: CR. Analyzed the data: MB. Wrote the paper: MB DS.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0019368