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AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays
Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-sig...
Autores principales: | , , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2006
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1635267/ https://www.ncbi.nlm.nih.gov/pubmed/16982644 http://dx.doi.org/10.1093/nar/gkl601 |
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author | Hu, Guohong Wang, Hui-Yun Greenawalt, Danielle M. Azaro, Marco A. Luo, Minjie Tereshchenko, Irina V. Cui, Xiangfeng Yang, Qifeng Gao, Richeng Shen, Li Li, Honghua |
author_facet | Hu, Guohong Wang, Hui-Yun Greenawalt, Danielle M. Azaro, Marco A. Luo, Minjie Tereshchenko, Irina V. Cui, Xiangfeng Yang, Qifeng Gao, Richeng Shen, Li Li, Honghua |
author_sort | Hu, Guohong |
collection | PubMed |
description | Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-signal filtering before genotype determination. Although many algorithms are sophisticated for these purposes, biases are still present. In the present paper, new algorithms for SNP microarray data analysis and the software, AccuTyping, developed based on these algorithms are described. The algorithms take advantage of a large number of SNPs included in each assay, and the fact that the top and bottom 20% of SNPs can be safely treated as homozygous after sorting based on their ratios between the signal intensities. These SNPs are then used as controls for color channel normalization and background subtraction. Genotype calls are made based on the logarithms of signal intensity ratios using two cutoff values, which were determined after training the program with a dataset of ∼160 000 genotypes and validated by non-microarray methods. AccuTyping was used to determine >300 000 genotypes of DNA and sperm samples. The accuracy was shown to be >99%. AccuTyping can be downloaded from . |
format | Text |
id | pubmed-1635267 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-16352672006-11-29 AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays Hu, Guohong Wang, Hui-Yun Greenawalt, Danielle M. Azaro, Marco A. Luo, Minjie Tereshchenko, Irina V. Cui, Xiangfeng Yang, Qifeng Gao, Richeng Shen, Li Li, Honghua Nucleic Acids Res Methods Online Microarray-based analysis of single nucleotide polymorphisms (SNPs) has many applications in large-scale genetic studies. To minimize the influence of experimental variation, microarray data usually need to be processed in different aspects including background subtraction, normalization and low-signal filtering before genotype determination. Although many algorithms are sophisticated for these purposes, biases are still present. In the present paper, new algorithms for SNP microarray data analysis and the software, AccuTyping, developed based on these algorithms are described. The algorithms take advantage of a large number of SNPs included in each assay, and the fact that the top and bottom 20% of SNPs can be safely treated as homozygous after sorting based on their ratios between the signal intensities. These SNPs are then used as controls for color channel normalization and background subtraction. Genotype calls are made based on the logarithms of signal intensity ratios using two cutoff values, which were determined after training the program with a dataset of ∼160 000 genotypes and validated by non-microarray methods. AccuTyping was used to determine >300 000 genotypes of DNA and sperm samples. The accuracy was shown to be >99%. AccuTyping can be downloaded from . Oxford University Press 2006-10 2006-09-18 /pmc/articles/PMC1635267/ /pubmed/16982644 http://dx.doi.org/10.1093/nar/gkl601 Text en © 2006 The Author(s) |
spellingShingle | Methods Online Hu, Guohong Wang, Hui-Yun Greenawalt, Danielle M. Azaro, Marco A. Luo, Minjie Tereshchenko, Irina V. Cui, Xiangfeng Yang, Qifeng Gao, Richeng Shen, Li Li, Honghua AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays |
title | AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays |
title_full | AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays |
title_fullStr | AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays |
title_full_unstemmed | AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays |
title_short | AccuTyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays |
title_sort | accutyping: new algorithms for automated analysis of data from high-throughput genotyping with oligonucleotide microarrays |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1635267/ https://www.ncbi.nlm.nih.gov/pubmed/16982644 http://dx.doi.org/10.1093/nar/gkl601 |
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