数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-hi-c-analysis-loop-calling

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

Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data.

OpenClawNanoClaw分析处理复现实验bio-hi-c-analysis-loop-calling🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatindetect

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-hi-c-analysis-loop-calling

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

Python：cooltools.dots() 或 chromosight detect --pattern=loops。
Call chromatin loops ，面向 my Hi-C data" → Detect point enrichments in contact matrices representing CTCF-mediated loops 、 enhancer-promoter interactions. Python：cooltools.dots() 或 chromosight detect --pattern=loops。
检测 chromatin loops 、 point interactions ，面向 Hi-C data。
expected = cooltools.expected_cis(clr，view_df=view_df，ignore_diags=2)。

原始文档

SKILL.md 摘录

Call Loops with cooltools (Dot Calling)

clr = cooler.Cooler('matrix.mcool::resolutions/10000')
view_df = bioframe.make_viewframe(clr.chromsizes)

## Call dots (loops)

dots = cooltools.dots(
    clr,
    expected=expected,
    view_df=view_df,
    max_loci_separation=2000000,  # Max loop size (2Mb)
    max_nans_tolerated=0.5,
)

print(f'Found {len(dots)} loops')
print(dots.head())

Call loops with chromosight

chromosight detect
--pattern loops
--min-dist 20000
--max-dist 2000000
matrix.cool
loops_output

适用场景

适合在detecting chromatin loops ，面向 Hi-C data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

hic-data-io - Load Hi-C matrices
hic-visualization - Visualize loops
chip-seq - CTCF ChIP-seq for loop anchor validation

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