cfTools: an R/Bioconductor package for deconvolving cell-free DNA via methylation analysis
Cell-free DNA (cfDNA) released by dying cells from damaged or diseased tissues can lead to elevated tissue-specific DNA, which is traceable and quantifiable through unique DNA methylation patterns. Therefore, tracing cfDNA origins by analyzing its methylation profiles holds great potential for detec...
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Published in | Bioinformatics advances Vol. 5; no. 1; p. vbaf108 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
England
01.01.2025
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Online Access | Get full text |
ISSN | 2635-0041 2635-0041 |
DOI | 10.1093/bioadv/vbaf108 |
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Abstract | Cell-free DNA (cfDNA) released by dying cells from damaged or diseased tissues can lead to elevated tissue-specific DNA, which is traceable and quantifiable through unique DNA methylation patterns. Therefore, tracing cfDNA origins by analyzing its methylation profiles holds great potential for detecting and monitoring a range of diseases, including cancers. However, deconvolving tissue-specific cfDNA remains challenging for broader applications and research due to the scarcity of specialized, user-friendly bioinformatics tools.
To address this, we developed cfTools, an R package that streamlines cfDNA tissue-of-origin analysis for disease detection and monitoring. Integrating advanced cfDNA tissue deconvolution algorithms with R/Bioconductor compatibility, cfTools offers data preparation and analysis functions with flexible parameters for user-friendliness. By identifying abnormal cfDNA compositions, cfTools can infer the presence of underlying pathological conditions, including but not limited to cancer. It simplifies bioinformatics tasks and enables users without advanced expertise to easily derive biologically interpretable insights from standard preprocessed sequencing data, thus increasing its accessibility and broadening its application in cfDNA-based disease studies.
cfTools and its supplementary package cfToolsData are freely available at Bioconductor: https://bioconductor.org/packages/release/bioc/html/cfTools.html and https://bioconductor.org/packages/release/data/experiment/html/cfToolsData.html. The development version of cfTools is maintained on GitHub: https://github.com/jasminezhoulab/cfTools. |
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AbstractList | Cell-free DNA (cfDNA) released by dying cells from damaged or diseased tissues can lead to elevated tissue-specific DNA, which is traceable and quantifiable through unique DNA methylation patterns. Therefore, tracing cfDNA origins by analyzing its methylation profiles holds great potential for detecting and monitoring a range of diseases, including cancers. However, deconvolving tissue-specific cfDNA remains challenging for broader applications and research due to the scarcity of specialized, user-friendly bioinformatics tools.MotivationCell-free DNA (cfDNA) released by dying cells from damaged or diseased tissues can lead to elevated tissue-specific DNA, which is traceable and quantifiable through unique DNA methylation patterns. Therefore, tracing cfDNA origins by analyzing its methylation profiles holds great potential for detecting and monitoring a range of diseases, including cancers. However, deconvolving tissue-specific cfDNA remains challenging for broader applications and research due to the scarcity of specialized, user-friendly bioinformatics tools.To address this, we developed cfTools, an R package that streamlines cfDNA tissue-of-origin analysis for disease detection and monitoring. Integrating advanced cfDNA tissue deconvolution algorithms with R/Bioconductor compatibility, cfTools offers data preparation and analysis functions with flexible parameters for user-friendliness. By identifying abnormal cfDNA compositions, cfTools can infer the presence of underlying pathological conditions, including but not limited to cancer. It simplifies bioinformatics tasks and enables users without advanced expertise to easily derive biologically interpretable insights from standard preprocessed sequencing data, thus increasing its accessibility and broadening its application in cfDNA-based disease studies.ResultsTo address this, we developed cfTools, an R package that streamlines cfDNA tissue-of-origin analysis for disease detection and monitoring. Integrating advanced cfDNA tissue deconvolution algorithms with R/Bioconductor compatibility, cfTools offers data preparation and analysis functions with flexible parameters for user-friendliness. By identifying abnormal cfDNA compositions, cfTools can infer the presence of underlying pathological conditions, including but not limited to cancer. It simplifies bioinformatics tasks and enables users without advanced expertise to easily derive biologically interpretable insights from standard preprocessed sequencing data, thus increasing its accessibility and broadening its application in cfDNA-based disease studies.cfTools and its supplementary package cfToolsData are freely available at Bioconductor: https://bioconductor.org/packages/release/bioc/html/cfTools.html and https://bioconductor.org/packages/release/data/experiment/html/cfToolsData.html. The development version of cfTools is maintained on GitHub: https://github.com/jasminezhoulab/cfTools.Availability and implementationcfTools and its supplementary package cfToolsData are freely available at Bioconductor: https://bioconductor.org/packages/release/bioc/html/cfTools.html and https://bioconductor.org/packages/release/data/experiment/html/cfToolsData.html. The development version of cfTools is maintained on GitHub: https://github.com/jasminezhoulab/cfTools. Cell-free DNA (cfDNA) released by dying cells from damaged or diseased tissues can lead to elevated tissue-specific DNA, which is traceable and quantifiable through unique DNA methylation patterns. Therefore, tracing cfDNA origins by analyzing its methylation profiles holds great potential for detecting and monitoring a range of diseases, including cancers. However, deconvolving tissue-specific cfDNA remains challenging for broader applications and research due to the scarcity of specialized, user-friendly bioinformatics tools. To address this, we developed cfTools, an R package that streamlines cfDNA tissue-of-origin analysis for disease detection and monitoring. Integrating advanced cfDNA tissue deconvolution algorithms with R/Bioconductor compatibility, cfTools offers data preparation and analysis functions with flexible parameters for user-friendliness. By identifying abnormal cfDNA compositions, cfTools can infer the presence of underlying pathological conditions, including but not limited to cancer. It simplifies bioinformatics tasks and enables users without advanced expertise to easily derive biologically interpretable insights from standard preprocessed sequencing data, thus increasing its accessibility and broadening its application in cfDNA-based disease studies. cfTools and its supplementary package cfToolsData are freely available at Bioconductor: https://bioconductor.org/packages/release/bioc/html/cfTools.html and https://bioconductor.org/packages/release/data/experiment/html/cfToolsData.html. The development version of cfTools is maintained on GitHub: https://github.com/jasminezhoulab/cfTools. |
Author | Li, Qingjiao Stackpole, Mary L Li, Wenyuan Hu, Ran Zhou, Xianghong Jasmine Li, Shuo |
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Cites_doi | 10.1093/bioinformatics/btr167 10.1186/s13059-024-03275-x 10.3389/fgene.2021.671057 10.1038/s41467-021-22901-x 10.1038/nrc.2017.7 10.1093/nargab/lqad048 10.1038/s41586-022-05580-6 10.1080/15592294.2020.1816774 10.1038/s41467-018-07466-6 10.1093/nar/gky423 10.1038/npp.2012.112 10.1093/bib/bby029 10.3390/cancers11111741 10.3389/fimmu.2023.1148722 10.1073/pnas.2305236120 10.1016/j.annonc.2020.02.011 10.1038/s41467-022-32995-6 10.1073/pnas.1508736112 |
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