<HashMap><database>GEO</database><file_versions><headers><Content-Type>application/xml</Content-Type></headers><body><files><Other>ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE273nnn/GSE273521/</Other></files><type>primary</type></body><statusCodeValue>200</statusCodeValue><statusCode>OK</statusCode></file_versions><scores/><additional><omics_type>Methylation profiling</omics_type><species>Mus musculus</species><gds_type>Methylation profiling by high throughput sequencing</gds_type><full_dataset_link>https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273521</full_dataset_link><repository>GEO</repository><entry_type>GSE</entry_type></additional><is_claimable>false</is_claimable><name>A theoretical and experimental framework enables low-coverage sequencing for accurate quantification of genome-wide cytosine modification levels</name><description>5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) regulate gene expression and exhibit dynamic levels during development and disease. While high-depth, base-resolution studies offer the most detailed view of epigenetic landscapes, many open questions are answered by surveying changes in 5mC/5hmC levels across larger cohorts. Nonetheless, current global quantification methods, including mass spectrometry, are typically limited in accessibility, accuracy, or throughput. Here, to evaluate the viability of low-coverage sequencing as an alternative, we first computationally downsampled deeply sequenced data to resolve the three-way relationship between sequencing coverage, modification levels, and measurement error. This relationship allowed us to develop a facile online tool for error calculation and to define experimental targets: &lt;0.24% genome coverage can quantify 5mC and low-abundance 5hmC with minimal and predictable errors (&lt;5%). Importantly, in direct comparisons, low-depth sequencing (Sparse-Seq) demonstrated high accuracy and less variability than mass spectrometry, while distinctively preserving genomic context. Applied serially to developing mouse brains, Sparse-Seq revealed an earlier emergence of 5hmCpG compared to 5mCpH and uncovered previously overlooked, genomic feature-specific epigenetic changes. This work establishes a rigorous foundation for employing Sparse-Seq as a highly accessible approach for 5mC/5hmC quantification, enabling economical first-pass analysis of epigenetic landscapes suited for large cohort studies and new hypothesis generation.</description><dates><publication>2026/06/21</publication></dates><accession>GSE273521</accession><cross_references><GSM>GSM8431616</GSM><GSM>GSM8431585</GSM><GSM>GSM8431584</GSM><GSM>GSM8431583</GSM><GSM>GSM8431582</GSM><GSM>GSM8431581</GSM><GSM>GSM8431580</GSM><GSM>GSM8431604</GSM><GSM>GSM8431603</GSM><GSM>GSM8431569</GSM><GSM>GSM8431602</GSM><GSM>GSM8431601</GSM><GSM>GSM8431589</GSM><GSM>GSM8431600</GSM><GSM>GSM8431588</GSM><GSM>GSM8431587</GSM><GSM>GSM8431586</GSM><GSM>GSM8431609</GSM><GSM>GSM8431608</GSM><GSM>GSM8431607</GSM><GSM>GSM8431606</GSM><GSM>GSM8431605</GSM><GSM>GSM8431596</GSM><GSM>GSM8431574</GSM><GSM>GSM8431595</GSM><GSM>GSM8431573</GSM><GSM>GSM8431572</GSM><GSM>GSM8431594</GSM><GSM>GSM8431593</GSM><GSM>GSM8431571</GSM><GSM>GSM8431592</GSM><GSM>GSM8431570</GSM><GSM>GSM8431591</GSM><GSM>GSM8431590</GSM><GSM>GSM8431615</GSM><GSM>GSM8431614</GSM><GSM>GSM8431613</GSM><GSM>GSM8431612</GSM><GSM>GSM8431579</GSM><GSM>GSM8431578</GSM><GSM>GSM8431611</GSM><GSM>GSM8431599</GSM><GSM>GSM8431577</GSM><GSM>GSM8431610</GSM><GSM>GSM8431598</GSM><GSM>GSM8431576</GSM><GSM>GSM8431575</GSM><GSM>GSM8431597</GSM><GPL>16417</GPL><GSE>273521</GSE><taxon>Mus musculus</taxon></cross_references></HashMap>