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sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data
DNA methylation influences predisposition, development and prognosis for many diseases, including cancer. However, it is not uncommon to encounter samples with incorrect sex labelling or atypical sex chromosome arrangement. Sex is one of the strongest influencers of the genomic distribution of DNA m...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6213967/ https://www.ncbi.nlm.nih.gov/pubmed/30326623 http://dx.doi.org/10.3390/ijms19103172 |
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author | Jung, Chol-Hee Park, Daniel J. Georgeson, Peter Mahmood, Khalid Milne, Roger L. Southey, Melissa C. Pope, Bernard J. |
author_facet | Jung, Chol-Hee Park, Daniel J. Georgeson, Peter Mahmood, Khalid Milne, Roger L. Southey, Melissa C. Pope, Bernard J. |
author_sort | Jung, Chol-Hee |
collection | PubMed |
description | DNA methylation influences predisposition, development and prognosis for many diseases, including cancer. However, it is not uncommon to encounter samples with incorrect sex labelling or atypical sex chromosome arrangement. Sex is one of the strongest influencers of the genomic distribution of DNA methylation and, therefore, correct assignment of sex and filtering of abnormal samples are essential for the quality control of study data. Differences in sex chromosome copy numbers between sexes and X-chromosome inactivation in females result in distinctive sex-specific patterns in the distribution of DNA methylation levels. In this study, we present a software tool, sEst, which incorporates clustering analysis to infer sex and to detect sex-chromosome abnormalities from DNA methylation microarray data. Testing with two publicly available datasets demonstrated that sEst not only correctly inferred the sex of the test samples, but also identified mislabelled samples and samples with potential sex-chromosome abnormalities, such as Klinefelter syndrome and Turner syndrome, the latter being a feature not offered by existing methods. Considering that sex and the sex-chromosome abnormalities can have large effects on many phenotypes, including diseases, our method can make a significant contribution to DNA methylation studies that are based on microarray platforms. |
format | Online Article Text |
id | pubmed-6213967 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62139672018-11-14 sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data Jung, Chol-Hee Park, Daniel J. Georgeson, Peter Mahmood, Khalid Milne, Roger L. Southey, Melissa C. Pope, Bernard J. Int J Mol Sci Article DNA methylation influences predisposition, development and prognosis for many diseases, including cancer. However, it is not uncommon to encounter samples with incorrect sex labelling or atypical sex chromosome arrangement. Sex is one of the strongest influencers of the genomic distribution of DNA methylation and, therefore, correct assignment of sex and filtering of abnormal samples are essential for the quality control of study data. Differences in sex chromosome copy numbers between sexes and X-chromosome inactivation in females result in distinctive sex-specific patterns in the distribution of DNA methylation levels. In this study, we present a software tool, sEst, which incorporates clustering analysis to infer sex and to detect sex-chromosome abnormalities from DNA methylation microarray data. Testing with two publicly available datasets demonstrated that sEst not only correctly inferred the sex of the test samples, but also identified mislabelled samples and samples with potential sex-chromosome abnormalities, such as Klinefelter syndrome and Turner syndrome, the latter being a feature not offered by existing methods. Considering that sex and the sex-chromosome abnormalities can have large effects on many phenotypes, including diseases, our method can make a significant contribution to DNA methylation studies that are based on microarray platforms. MDPI 2018-10-15 /pmc/articles/PMC6213967/ /pubmed/30326623 http://dx.doi.org/10.3390/ijms19103172 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jung, Chol-Hee Park, Daniel J. Georgeson, Peter Mahmood, Khalid Milne, Roger L. Southey, Melissa C. Pope, Bernard J. sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data |
title | sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data |
title_full | sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data |
title_fullStr | sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data |
title_full_unstemmed | sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data |
title_short | sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data |
title_sort | sest: accurate sex-estimation and abnormality detection in methylation microarray data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6213967/ https://www.ncbi.nlm.nih.gov/pubmed/30326623 http://dx.doi.org/10.3390/ijms19103172 |
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