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MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data
BACKGROUND: DNA methylation of CpG dinucleotides is an essential epigenetic modification that plays a key role in transcription. Widely used DNA enrichment-based methods offer high coverage for measuring methylated CpG dinucleotides, with the lowest cost per CpG covered genome-wide. However, these m...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6303941/ https://www.ncbi.nlm.nih.gov/pubmed/30577750 http://dx.doi.org/10.1186/s12859-018-2574-7 |
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author | Xu, Jingting Liu, Shimeng Yin, Ping Bulun, Serdar Dai, Yang |
author_facet | Xu, Jingting Liu, Shimeng Yin, Ping Bulun, Serdar Dai, Yang |
author_sort | Xu, Jingting |
collection | PubMed |
description | BACKGROUND: DNA methylation of CpG dinucleotides is an essential epigenetic modification that plays a key role in transcription. Widely used DNA enrichment-based methods offer high coverage for measuring methylated CpG dinucleotides, with the lowest cost per CpG covered genome-wide. However, these methods measure the DNA enrichment of methyl-CpG binding, and thus do not provide information on absolute methylation levels. Further, the enrichment is influenced by various confounding factors in addition to methylation status, for example, CpG density. Computational models that can accurately derive absolute methylation levels from DNA enrichment data are needed. RESULTS: We developed “MeDEStrand,” a method that uses a sigmoid function to estimate and correct the CpG bias from enrichment results to infer absolute DNA methylation levels. Unlike previous methods, which estimate CpG bias based on reads mapped at the same genomic loci, MeDEStrand processes the reads for the positive and negative DNA strands separately. We compared the performance of MeDEStrand to that of three other state-of-the-art methods “MEDIPS,” “BayMeth,” and “QSEA” on four independent datasets generated using immortalized cell lines (GM12878 and K562) and human primary cells (foreskin fibroblasts and mammary epithelial cells). Based on the comparison of the inferred absolute methylation levels from MeDIP-seq data and the corresponding reduced-representation bisulfite sequencing data from each method, MeDEStrand showed the best performance at high resolution of 25, 50, and 100 base pairs. CONCLUSIONS: The MeDEStrand tool can be used to infer whole-genome absolute DNA methylation levels at the same cost of enrichment-based methods with adequate accuracy and resolution. R package MeDEStrand and its tutorial is freely available for download at https://github.com/jxu1234/MeDEStrand.git. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2574-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6303941 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63039412018-12-31 MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data Xu, Jingting Liu, Shimeng Yin, Ping Bulun, Serdar Dai, Yang BMC Bioinformatics Methodology Article BACKGROUND: DNA methylation of CpG dinucleotides is an essential epigenetic modification that plays a key role in transcription. Widely used DNA enrichment-based methods offer high coverage for measuring methylated CpG dinucleotides, with the lowest cost per CpG covered genome-wide. However, these methods measure the DNA enrichment of methyl-CpG binding, and thus do not provide information on absolute methylation levels. Further, the enrichment is influenced by various confounding factors in addition to methylation status, for example, CpG density. Computational models that can accurately derive absolute methylation levels from DNA enrichment data are needed. RESULTS: We developed “MeDEStrand,” a method that uses a sigmoid function to estimate and correct the CpG bias from enrichment results to infer absolute DNA methylation levels. Unlike previous methods, which estimate CpG bias based on reads mapped at the same genomic loci, MeDEStrand processes the reads for the positive and negative DNA strands separately. We compared the performance of MeDEStrand to that of three other state-of-the-art methods “MEDIPS,” “BayMeth,” and “QSEA” on four independent datasets generated using immortalized cell lines (GM12878 and K562) and human primary cells (foreskin fibroblasts and mammary epithelial cells). Based on the comparison of the inferred absolute methylation levels from MeDIP-seq data and the corresponding reduced-representation bisulfite sequencing data from each method, MeDEStrand showed the best performance at high resolution of 25, 50, and 100 base pairs. CONCLUSIONS: The MeDEStrand tool can be used to infer whole-genome absolute DNA methylation levels at the same cost of enrichment-based methods with adequate accuracy and resolution. R package MeDEStrand and its tutorial is freely available for download at https://github.com/jxu1234/MeDEStrand.git. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2574-7) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-22 /pmc/articles/PMC6303941/ /pubmed/30577750 http://dx.doi.org/10.1186/s12859-018-2574-7 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Xu, Jingting Liu, Shimeng Yin, Ping Bulun, Serdar Dai, Yang MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data |
title | MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data |
title_full | MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data |
title_fullStr | MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data |
title_full_unstemmed | MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data |
title_short | MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data |
title_sort | medestrand: an improved method to infer genome-wide absolute methylation levels from dna enrichment data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6303941/ https://www.ncbi.nlm.nih.gov/pubmed/30577750 http://dx.doi.org/10.1186/s12859-018-2574-7 |
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