DOI: 10.5061/dryad.zgmsbccq9
title: Flongle data for KnowYourCG: Facilitating base-level sparse methylome interpretation

description: ONT-based 5mC and 5hmC signals across four mouse tissues (lung, blood,
 uterus, and cortex) profiled with low-pass Flongle flowcells (~1M
 CpGs per sample). Decoding DNA methylomes for biological insights
 is critical in epigenetics research. We present KnowYourCG (KYCG), a data
 interpretation framework designed for functional DNA methylation analysis.
 Unlike existing tools that target genes or genomic intervals, KYCG
 features direct base-level screenings of diverse biological and technical
 influences, including sequence motifs, transcription factor binding,
 histone modifications, replication timing, cell-type-specific methylation,
 and trait associations. Through implementing efficient infrastructure that
 rapidly screens and investigates thousands of knowledge bases, KYCG
 addresses the challenges of data sparsity in various methylation datasets,
 including low-pass or single-cell DNA methylomes, 5-hydroxymethylation
 profiles, spatial DNA methylation maps, and array-based datasets for
 epigenome-wide association studies. Applying KYCG to these datasets
 provides valuable insights into cell differentiation, cancer origins,
 epigenome-trait associations, and technical issues such as array
 artifacts, single-cell batch effects, and Nanopore 5hmC detection
 accuracy. Our tool simplifies large-scale methylation analysis and
 integrates seamlessly with standard assay technologies.
description: # Flongle data for KnowYourCG: Facilitating base-level sparse methylome
 interpretation Dataset DOI:
 [10.5061/dryad.zgmsbccq9](10.5061/dryad.zgmsbccq9) ## Description of the
 data and file structure Oxford Nanopore Technology (ONT) is an emerging
 approach to directly discriminate 5mC, 5hmC, and unmodified C from ion
 current signals, bypassing cytosine deamination methods that cannot
 separate 5mC and 5hmC. Here, we provide ONT-based 5mC signals across four
 mouse tissues (lung, blood, uterus, and cortex) profiled with low-pass
 Flongle flowcells (~1M CpGs per sample). Each of the four mouse tissues is
 stored as .cg file to capture the methylation information.  ### Files and
 variables #### File: uterus.tar
 **Description:** uterus 20231207_1654_MN36407_AQY317_deaa9eb5 * Contents:
 One `.cg` file (`uterus_20231207_1654_MN36407_AQY317_deaa9eb5.cg`) *
 Tissue type: Mouse uterus * Sequencing date: 2023-12-07 * Flowcell ID:
 MN36407 * Experiment tag: AQY317 * Approx. coverage: ~1M CpGs * Notes:
 Low-pass Flongle run capturing genome-wide 5mC signal calls. #### File:
 blood.tar **Description:** blood 20231213_1441_MN36407_ARH615_cbdd1451 *
 Contents: One `.cg` file
 (`blood_20231213_1441_MN36407_ARH615_cbdd1451.cg`) * Tissue type: Mouse
 blood * Sequencing date: 2023-12-13 * Flowcell ID: MN36407 * Experiment
 tag: ARH615 * Approx. coverage: ~1M CpGs * Notes: Profiles peripheral
 blood tissue; sparse coverage typical of Flongle output. #### File:
 lung.tar **Description:** lung 20231128_1200_MN36407_AQY190_5c40b3aa *
 Contents: One `.cg` file (`lung_20231128_1200_MN36407_AQY190_5c40b3aa.cg`)
 * Tissue type: Mouse lung * Sequencing date: 2023-11-28 * Flowcell ID:
 MN36407 * Experiment tag: AQY190 * Approx. coverage: ~1M CpGs * Notes:
 Genome-wide 5mC calls with low-pass resolution. #### File: cortex.tar
 **Description:** cortex 20221226_1552_MN36407_ANB527_cd04e856 * Contents:
 One `.cg` file (`cortex_20221226_1552_MN36407_ANB527_cd04e856.cg`) *
 Tissue type: Mouse cortex * Sequencing date: 2022-12-26 * Flowcell ID:
 MN36407 * Experiment tag: ANB527 * Approx. coverage: ~1M CpGs * Notes:
 Captures brain cortex methylation landscape at sparse genome coverage. ##
 Code/software The sequence-level enrichment analysis is available as a
 command-line C program available at
 [https://github.com/zhou-lab/YAME](https://github.com/zhou-lab/YAME). The
 YAME documentation is available at
 [https://zhou-lab.github.io/YAME/](https://zhou-lab.github.io/YAME/). To
 unpack the data, simply use the command `yame unpack` which will output
 the desired methylation matrix for all the samples.