Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Nanopore DNA methylation analysis of brain tumors

ABSTRACT: Nanopore DNA methylation analysis of brain tumors

PROVIDER: PRJEB44439 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Nanopore DNA methylation analysis of brain tumors

Project description:Nanopore DNA methylation analysis of brain tumors

| PRJEB48142 | ENA

Robust methylation based classification of brain tumors using nanopore sequencing

Project description:Robust methylation based classification of brain tumors using nanopore sequencing

| PRJNA862740 | ENA

Robust methylation based classification of brain tumors using nanopore sequencing

Project description:Using a public reference data set of 82 unique entities, 382 nanopore-sequenced brain tumor samples were classified based on their methylation status through an ad hoc random forest algorithm. As a measure of confidence, score recalibration was performed and platform-specific thresholds were defined.

2022-07-30 | GSE209865 | GEO

crossNN is an explainable framework for cross-platform DNA methylation-based classification of tumors

Project description:DNA methylation-based classification of brain tumors has emerged as a powerful and indispensable diagnostic technique. Initial implementations have used methylation microarrays for data generation, but different sequencing approaches are increasingly used. Most current classifiers, however, rely on a fixed methylation feature space, rendering them incompatible with other platforms, especially different flavors of DNA sequencing. Here, we describe crossNN, a neural network-based machine learning framework which can accurately classify tumor entities using DNA methylation profiles obtained from different platforms and with different epigenome coverage and sequencing depth. It outperforms other deep and conventional machine learning models with respect to diagnostic accuracy and computational requirements while still being fully explainable. We use crossNN to train a pan-cancer classifier to discriminate more than 170 tumor types across all organ sites. Validation in an independent cohort of >5,000 tumors profiled using different microarray and sequencing platforms, including low-pass nanopore and targeted bisulfite sequencing, demonstrates the robustness and scalability of the model with 99.1% and 97.8% precision for brain tumor and pan-cancer model, respectively.

2025-02-24 | GSE289137 | GEO

Targeted nanopore resequencing and methylation analysis of LINE-1 retrotransposons

Project description:We used the nanopore Cas9 targeted sequencing (nCATS) strategy to specifically sequence 125 L1HS-containing loci in parallel and measure their DNA methylation levels using nanopore long-read sequencing. Each targeted locus is sequenced at high coverage (~45X) with unambiguously mapped reads spanning the entire L1 element, as well as its flanking sequences over several kilobases. The genome-wide profile of L1 methylation was also assessed by bs-ATLAS-seq in the same cell lines (E-MTAB-10895).

2023-09-22 | E-MTAB-12247 | biostudies-arrayexpress

crossNN is an explainable framework for cross-platform DNA methylation-based classification of tumors [sequencing-based methylome profiling]

2025-02-24 | GSE289246 | GEO

Distinct DNA methylation landscape between pediatric brain and adult brain

Project description:Epigenetics tightly regulates gene expression during brain development, which ensemble distinct cell types and form complicated functional brain organ. DNA methylation is an important mark which undergo dramatically changes during brain development. The disturb of this process will lead to various brain tumors. To study the concordant DNA methylation changes during normal brain development, we sequenced DNA methylome of pediatric brain tissues from autopsy with various ages. We systematically compared the DNA methylome of pediatric brain and adult brain and identified candidate DMRs that contribute to normal brain development. This comprehensive analysis will provide important epigenetic reference for human brain development which will be a valuable data to study the epigenetic mechanism of pediatric brain tumor.

2024-06-26 | GSE179086 | GEO

DNA methylation profiling of ERpHER2n primary tumors

Project description:DNA methylation profiling of ERpHER2n primary tumors

| PRJNA1168039 | ENA

DNA methylation profiling of TNBC primary tumors

Project description:DNA methylation profiling of TNBC primary tumors

| PRJNA1231115 | ENA

DNA methylation profiling of salivary gland tumors

Project description:DNA methylation profiling of salivary gland tumors

| PRJNA1016342 | ENA