数据与复现统计与数据分析FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-methylation-methylkit

维护者 FreedomIntelligence · 最近更新 2026年4月1日

bio-methylation-methylkit：DNA methylation analysis ，支持 methylKit in R。 Import Bismark coverage files，filter by coverage，normalize samples，、 perform statistical comparisons。适合在analyzing single-base methylation patterns，comparing samples，或 preparing data ，用于 DMR detection时使用。

OpenClawNanoClaw分析处理复现实验bio-methylation-methylkit🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatindna

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-methylation-methylkit

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

R：methylKit：：methRead() → filterByCoverage() → normalizeCoverage() → calculateDiffMeth()。
分析 methylation patterns across my samples" → Import per-cytosine methylation data，filter by coverage，normalize across samples，、 test ，用于 differential methylation at individual CpG sites. R：methylKit：：methRead() → filterByCoverage() → normalizeCoverage() → calculateDiffMeth()。
getMethylationStats(meth_obj[[1]]，plot = TRUE，both.strands = FALSE)。

原始文档

SKILL.md 摘录

Coverage per sample

getCoverageStats(meth_obj[[1]], plot = TRUE, both.strands = FALSE)


## Remove CpGs with very low or very high coverage

meth_filtered <- filterByCoverage(
    meth_obj,
    lo.count = 10,        # Minimum 10 reads
    lo.perc = NULL,
    hi.count = NULL,
    hi.perc = 99.9        # Remove top 0.1% (likely PCR artifacts)
)

Normalize coverage between samples (recommended)

meth_norm <- normalizeCoverage(meth_filtered, method = 'median')

适用场景

适合在analyzing single-base methylation patterns，comparing samples，或 preparing data ，用于 DMR detection时使用。

不适用场景

Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。

上游相关技能

bismark-alignment - Generate input BAM files
methylation-calling - Extract coverage files
dmr-detection - Advanced DMR methods
pathway-analysis/go-enrichment - Functional annotation

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