Computational challenges in detection of cancer using cell-free DNA methylation
| Autor: | Madhu Sharma, Rohit Kumar Verma, Sunil Kumar, Vibhor Kumar |
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| Rok vydání: | 2022 |
| Předmět: |
RRBS
Reduced-Representation Bisulfite Sequencing Biophysics Review Article Biochemistry MBD-seq Methyl-CpG Binding Domain Protein Capture Sequencing ddMCP droplet digital methylation-specific PCR MCTA-seq Methylated CpG tandems amplification and sequencing MeDIP-seq Methylated DNA Immunoprecipitation Sequencing Structural Biology Cancer heterogeneity Diagnosis Genetics scCGI methylated CGIs at single cell level MSCC Methylation Sensitive Cut Counting ctDNA circulating tumor DNA HELP-seq HpaII-tiny fragment Enrichment by Ligation-mediated PCR sequencing ddPCR droplet digital polymerase chain reaction DMR Differentially methylated regions Cell free DNA MSRE methylation sensitive restriction enzymes cfDNA cell free DNA Computer Science Applications DMP Differentially methylated base position Computation WGBS Whole Genome Bisulfite Sequencing dPCR digital polymerase chain reaction TP248.13-248.65 Biotechnology |
| Zdroj: | Computational and Structural Biotechnology Journal, Vol 20, Iss, Pp 26-39 (2022) Computational and Structural Biotechnology Journal |
| ISSN: | 2001-0370 |
| DOI: | 10.1016/j.csbj.2021.12.001 |
| Popis: | Cell-free DNA(cfDNA) methylation profiling is considered promising and potentially reliable for liquid biopsy to study progress of diseases and develop reliable and consistent diagnostic and prognostic biomarkers. There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis. Here we also contemplate upon the multi-marker approach to handle the scenario of cancer heterogeneity and explore the utility of markers for 5hmC based cfDNA methylation pattern. Further, we provide a critical overview of deconvolution and machine learning methods for cfDNA methylation analysis. Our review of current methods reveals the potential for further improvement in analysis strategies for detecting early cancer using cfDNA methylation. |
| Databáze: | OpenAIRE |
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