AI Article Synopsis
- The analysis of DNA methylation in cell-free DNA (cfDNA) can identify important biomarkers but is complicated by the need for special protocols and sufficient material.
- Millions of cfDNA samples have been sequenced, leading to the development of FinaleMe, a Hidden Markov Model designed to predict methylation patterns from plasma whole-genome sequencing.
- The model's effectiveness was tested with 80 pairs of data from deep and shallow-coverage whole-genome sequencing and whole-genome bisulfite sequencing.
Article Abstract
Analysis of DNA methylation in cell-free DNA (cfDNA) reveals clinically relevant biomarkers but requires specialized protocols and sufficient input material that limits its applicability. Millions of cfDNA samples have been profiled by genomic sequencing. To maximize the gene regulation information from the existing dataset, we developed FinaleMe, a non-homogeneous Hidden Markov Model (HMM), to predict DNA methylation of cfDNA and, therefore, tissues-of-origin directly from plasma whole-genome sequencing (WGS). We validated the performance with 80 pairs of deep and shallow-coverage WGS and whole-genome bisulfite sequencing (WGBS) data.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10802291 | PMC |
| http://dx.doi.org/10.1101/2024.01.02.573710 | DOI Listing |
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