EPIC-Seq

EPIC-seq, (short for Epigenetic Expression Inference by Cell-free DNA Sequencing), is a high-throughput method that specifically targets gene promoters using cell-free DNA (cfDNA) sequencing. By employing non-invasive techniques such as blood sampling, it infers the expression levels of targeted genes. It consists of both wet and dry lab stages. EPIC-seq involves deep sequencing of the transcription start sites (TSS). It hypothesizes that with deep sequencing of these TSSs, usage of fragmentomic features, chromatin fragmentation patterns or properties, can allow high-resolution analyses, as opposed to its alternatives. The method has been shown effective for gene-level expression inference, molecular subtyping of diffuse large B cell lymphoma (DLBCL), histological classification of nonsmall-cell lung cancer (NSCLC), evaluation of results of immunotherapy agents, and assessment of the genes' prognostic importance. EPIC-seq uses machine learning to deduce the RNA expression of the genes and proposes two new metrics: promoter fragmentation entropy (PFE), an adjusted Shannon Index for entropy, and nucleosome-depleted region (NDR) score, the depth of sequencing in NDR regions. PFE showed superior performance compared to earlier metrics for fragmentomic features.…

EPIC-seq, (short for Epigenetic Expression Inference by Cell-free DNA Sequencing), is a high-throughput method that specifically targets gene promoters using cell-free DNA (cfDNA) sequencing. By employing non-invasive techniques such as blood sampling, it infers the expression levels of targeted genes. It consists of both wet and dry lab stages. EPIC-seq involves deep sequencing of the transcription start sites (TSS). It hypothesizes that with deep sequencing of these TSSs, usage of fragmentomic features, chromatin fragmentation patterns or properties, can allow high-resolution analyses, as opposed to its alternatives. The method has been shown effective for gene-level expression inference, molecular subtyping of diffuse large B cell lymphoma (DLBCL), histological classification of nonsmall-cell lung cancer (NSCLC), evaluation of results of immunotherapy agents, and assessment of the genes' prognostic importance. EPIC-seq uses machine learning to deduce the RNA expression of the genes and proposes two new metrics: promoter fragmentation entropy (PFE), an adjusted Shannon Index for entropy, and nucleosome-depleted region (NDR) score, the depth of sequencing in NDR regions. PFE showed superior performance compared to earlier metrics for fragmentomic features. Additionally, EPIC-seq has been mentioned as a possible solution for detecting tissue damage and esophagus cancer using methylation profiles of cfDNAs, profiling of donor liver molecular networks, and inflammatory bowel disease (IBD) detection. For the measurement of the diversity of fragmentomic features, the PFE metric, derived from Shannon's Index of entropy, is developed. The default number of 201 bins of lengths 100 to 300 are used for density estimation by the maximum likelihood method. The probability of having a fragment with size 99+i , ( \hat{p_i} ) is computed by the division of the number of fragments with size 99+i by the total number of fragments. Shannon's entropy is calculated with the formula: - \sum_{i=1}^{201}(\hat{p_i}\log_2(\hat{p_i})) .

Text derived from https://en.wikipedia.org/w/index.php?oldid=1368833397 under CC BY-SA 4.0; modifications have been made.