bio-clip-seq-binding-site-annotation
This skill annotates CLIP-seq binding peaks to genomic features such as 3'UTR, 5'UTR, coding sequences, introns, and non-coding RNAs using ChIPseeker and related bioinformatics tools. Use this when characterizing where RNA-binding proteins interact with transcripts and need to assign binding locations to specific functional genomic regions.
git clone --depth 1 https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills /tmp/bio-clip-seq-binding-site-annotation && cp -r /tmp/bio-clip-seq-binding-site-annotation/skills/bio-clip-seq-binding-site-annotation ~/.claude/skills/bio-clip-seq-binding-site-annotationSKILL.md
<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA
-->
---
name: bio-clip-seq-binding-site-annotation
description: Annotate CLIP-seq binding sites to genomic features including 3'UTR, 5'UTR, CDS, introns, and ncRNAs. Use when characterizing where an RBP binds in transcripts.
tool_type: mixed
primary_tool: ChIPseeker
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
---
# Binding Site Annotation
## Using ChIPseeker (R)
```r
library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
peaks <- readPeakFile('peaks.bed')
anno <- annotatePeak(peaks, TxDb = txdb)
plotAnnoPie(anno)
```
## Using BEDTools
```bash
# Annotate to UTRs
bedtools intersect -a peaks.bed -b 3utr.bed -wa -wb > peaks_3utr.bed
```
## Python Annotation
```python
import pandas as pd
def annotate_peaks(peaks_bed, annotation_gtf):
'''Annotate peaks to genomic features'''
# Load peaks and annotations
# Intersect and categorize
pass
```
## Related Skills
- clip-peak-calling - Get peaks
- genome-intervals/interval-arithmetic - Intersect peaks with genomic features
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Browse the web for any task — research topics, read articles, interact with web apps, fill forms, take screenshots, extract data, and test web pages. Use whenever a browser would be useful, not just when the user explicitly asks.
AI驱动的综合健康分析系统，整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。
Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.