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Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data
MOTIVATION: DNA CpG methylation (CpGm) has proven to be a crucial epigenetic factor in the mammalian gene regulatory system. Assessment of DNA CpG methylation values via whole-genome bisulfite sequencing (WGBS) is, however, computationally extremely demanding. RESULTS: We present FAst MEthylation ca...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10310462/ https://www.ncbi.nlm.nih.gov/pubmed/37326968 http://dx.doi.org/10.1093/bioinformatics/btad386 |
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author | Fischer, Jonas Schulz, Marcel H |
author_facet | Fischer, Jonas Schulz, Marcel H |
author_sort | Fischer, Jonas |
collection | PubMed |
description | MOTIVATION: DNA CpG methylation (CpGm) has proven to be a crucial epigenetic factor in the mammalian gene regulatory system. Assessment of DNA CpG methylation values via whole-genome bisulfite sequencing (WGBS) is, however, computationally extremely demanding. RESULTS: We present FAst MEthylation calling (FAME), the first approach to quantify CpGm values directly from bulk or single-cell WGBS reads without intermediate output files. FAME is very fast but as accurate as standard methods, which first produce BS alignment files before computing CpGm values. We present experiments on bulk and single-cell bisulfite datasets in which we show that data analysis can be significantly sped-up and help addressing the current WGBS analysis bottleneck for large-scale datasets without compromising accuracy. AVAILABILITY AND IMPLEMENTATION: An implementation of FAME is open source and licensed under GPL-3.0 at https://github.com/FischerJo/FAME. |
format | Online Article Text |
id | pubmed-10310462 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103104622023-06-30 Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data Fischer, Jonas Schulz, Marcel H Bioinformatics Original Paper MOTIVATION: DNA CpG methylation (CpGm) has proven to be a crucial epigenetic factor in the mammalian gene regulatory system. Assessment of DNA CpG methylation values via whole-genome bisulfite sequencing (WGBS) is, however, computationally extremely demanding. RESULTS: We present FAst MEthylation calling (FAME), the first approach to quantify CpGm values directly from bulk or single-cell WGBS reads without intermediate output files. FAME is very fast but as accurate as standard methods, which first produce BS alignment files before computing CpGm values. We present experiments on bulk and single-cell bisulfite datasets in which we show that data analysis can be significantly sped-up and help addressing the current WGBS analysis bottleneck for large-scale datasets without compromising accuracy. AVAILABILITY AND IMPLEMENTATION: An implementation of FAME is open source and licensed under GPL-3.0 at https://github.com/FischerJo/FAME. Oxford University Press 2023-06-16 /pmc/articles/PMC10310462/ /pubmed/37326968 http://dx.doi.org/10.1093/bioinformatics/btad386 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Fischer, Jonas Schulz, Marcel H Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data |
title | Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data |
title_full | Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data |
title_fullStr | Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data |
title_full_unstemmed | Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data |
title_short | Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data |
title_sort | efficiently quantifying dna methylation for bulk- and single-cell bisulfite data |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10310462/ https://www.ncbi.nlm.nih.gov/pubmed/37326968 http://dx.doi.org/10.1093/bioinformatics/btad386 |
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