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A base-calling algorithm for Tm-shifted melting curve SNP assay

BACKGROUND: Tm-shifted melting curve SNP assays are a class of homogeneous, low-cost genotyping assays. Alleles manifest themselves as signal peaks in the neighbourhood of theoretical allele-specific melting temperatures. Base calling for these assays has mostly relied on unsupervised algorithm or h...

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Autores principales: Liang, Kung-Hao, Fen, Jun-Jeng, Chang, Hsien-Hsun, Wang, Hsei-Wei, Hwang, Yuchi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143900/
https://www.ncbi.nlm.nih.gov/pubmed/21884624
http://dx.doi.org/10.1186/2043-9113-1-3
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author Liang, Kung-Hao
Fen, Jun-Jeng
Chang, Hsien-Hsun
Wang, Hsei-Wei
Hwang, Yuchi
author_facet Liang, Kung-Hao
Fen, Jun-Jeng
Chang, Hsien-Hsun
Wang, Hsei-Wei
Hwang, Yuchi
author_sort Liang, Kung-Hao
collection PubMed
description BACKGROUND: Tm-shifted melting curve SNP assays are a class of homogeneous, low-cost genotyping assays. Alleles manifest themselves as signal peaks in the neighbourhood of theoretical allele-specific melting temperatures. Base calling for these assays has mostly relied on unsupervised algorithm or human visual inspection to date. However, a practical clinical test needs to handle one or few individual samples at a time. This could pose a challenge for unsupervised algorithms which usually require a large number of samples to define alleles-representing signal clusters on the fly. METHODS: We presented a supervised base-calling algorithm and software for Tm-shifted melting curve SNP assays. The algorithm comprises a peak detection procedure and an ordinal regression model. The peak detection procedure is required for building models as well as handling new samples. Ordinal regression is proposed because signal intensities of alleles AA, AB, and BB usually follow an ordinal pattern with the heterozygous allele lie between two distinct homozygous alleles. Coefficients of the ordinal regression model are first trained and then used for base calling. RESULTS: A dataset of 12 SNPs of 44 unrelated persons was used for a demonstration purpose. The call rate is 99.6%. Among the base calls, 99.1% are identical to those made by the sequencing method. A small fraction of the melting curve signals (0.4%) is declared as "no call" for further human inspection. A software was implemented using the Java language, providing a graphical user interface for the visualization and handling of multiple melting curve signals. CONCLUSIONS: Tm-shifted melting curve SNP assays, together with the proposed base calling algorithm and software, provide a practical solution for genetic tests on a clinical setting. The software is available in http://www.bioinformatics.org/mcsnp/wiki/Main/HomePage
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spelling pubmed-31439002011-07-27 A base-calling algorithm for Tm-shifted melting curve SNP assay Liang, Kung-Hao Fen, Jun-Jeng Chang, Hsien-Hsun Wang, Hsei-Wei Hwang, Yuchi J Clin Bioinforma Research BACKGROUND: Tm-shifted melting curve SNP assays are a class of homogeneous, low-cost genotyping assays. Alleles manifest themselves as signal peaks in the neighbourhood of theoretical allele-specific melting temperatures. Base calling for these assays has mostly relied on unsupervised algorithm or human visual inspection to date. However, a practical clinical test needs to handle one or few individual samples at a time. This could pose a challenge for unsupervised algorithms which usually require a large number of samples to define alleles-representing signal clusters on the fly. METHODS: We presented a supervised base-calling algorithm and software for Tm-shifted melting curve SNP assays. The algorithm comprises a peak detection procedure and an ordinal regression model. The peak detection procedure is required for building models as well as handling new samples. Ordinal regression is proposed because signal intensities of alleles AA, AB, and BB usually follow an ordinal pattern with the heterozygous allele lie between two distinct homozygous alleles. Coefficients of the ordinal regression model are first trained and then used for base calling. RESULTS: A dataset of 12 SNPs of 44 unrelated persons was used for a demonstration purpose. The call rate is 99.6%. Among the base calls, 99.1% are identical to those made by the sequencing method. A small fraction of the melting curve signals (0.4%) is declared as "no call" for further human inspection. A software was implemented using the Java language, providing a graphical user interface for the visualization and handling of multiple melting curve signals. CONCLUSIONS: Tm-shifted melting curve SNP assays, together with the proposed base calling algorithm and software, provide a practical solution for genetic tests on a clinical setting. The software is available in http://www.bioinformatics.org/mcsnp/wiki/Main/HomePage BioMed Central 2011-01-20 /pmc/articles/PMC3143900/ /pubmed/21884624 http://dx.doi.org/10.1186/2043-9113-1-3 Text en Copyright ©2011 Liang et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Liang, Kung-Hao
Fen, Jun-Jeng
Chang, Hsien-Hsun
Wang, Hsei-Wei
Hwang, Yuchi
A base-calling algorithm for Tm-shifted melting curve SNP assay
title A base-calling algorithm for Tm-shifted melting curve SNP assay
title_full A base-calling algorithm for Tm-shifted melting curve SNP assay
title_fullStr A base-calling algorithm for Tm-shifted melting curve SNP assay
title_full_unstemmed A base-calling algorithm for Tm-shifted melting curve SNP assay
title_short A base-calling algorithm for Tm-shifted melting curve SNP assay
title_sort base-calling algorithm for tm-shifted melting curve snp assay
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143900/
https://www.ncbi.nlm.nih.gov/pubmed/21884624
http://dx.doi.org/10.1186/2043-9113-1-3
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