ArticleInternational journal of molecular sciences2018
sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Prevalence of chromosomal alterations in first-trimester spontaneous pregnancy loss.Nature medicine · 2023Article
- At age 9, the methylome of assisted reproductive technology children that underwent embryo culture in different media is not significantly different on a genome-wide scale.Human reproduction (Oxford, England) · 2022Article
- Methylome-wide analysis of IVF neonates that underwent embryo culture in different media revealed no significant differences.NPJ genomic medicine · 2022Article
- Autosomal sex-associated co-methylated regions predict biological sex from DNA methylation.Nucleic acids research · 2021Article
- DNA methylation-based sex classifier to predict sex and identify sex chromosome aneuploidy.BMC genomics · 2021Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Registered trials
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