compare-biosamples
Compare ENCODE experiments across different biosamples, tissues, or cell lines to identify tissue-specific regulatory patterns. Use when the user wants cross-tissue comparison, cell-type comparison, tissue-specific elements, differential chromatin, biosample matching, disease vs normal comparison, developmental time course, constitutive vs variable regulation, or multi-tissue data availability mapping. Handles batch effect detection, biosample hierarchy, and comparison design.
npx skills add majiayu000/claude-skill-registry --skill compare-biosamples-ammawla-encode-toolkit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Prescribes a workflow to compare ENCODE experiments across different biosamples (tissues, cell lines, or biosamples) to identify constitutive versus tissue-specific regulatory elements, map data availability, and design cross-biosample comparisons. It emphasizes matching technical parameters (assay, target, assembly, organism) and planning comparison designs such as cross-tissue, multi-omic in the same biosample, disease vs normal, and developmental time course, while accounting for batch effects and biosample hierarchy. It also guides building an availability matrix, selecting matched datasets, and using tools to check compatibility and batch-effect mitigation, including encode_get_facets, encode_search_experiments, encode_track_experiment, encode_compare_experiments, and encode_list_files, as well as applying ENCODE Blacklist filtering with bedtools intersect.
How it works
- Define the comparison design with the user, focusing on cross-tissue, multi-omic within a biosample, disease vs normal, development, or cross-species patterns.
- Map data availability across biosamples using encode_get_facets for organs, assays, and targets to build an availability matrix.
- Identify matched datasets by filtering experiments with encode_search_experiments (same assay, target, genome assembly, biosample type, organism) and compile candidate pairs.
- Check pairwise compatibility with encode_track_experiment for each tissue and encode_compare_experiments to verify organism, assembly, assay, biosample differences, target, replication strategy, and potential lab batch effects.
- Assess and mitigate batch effects following guidance on lab origin, sequencing platform, library date, antibody lot, and pipeline version; use PCA-based checks and fold-change-over-control signals.
- Select comparable files per experiment using encode_list_files, applying ENCODE Blacklist filtering via bedtools intersect to remove artifactual regions.
- Provide step-by-step file-type guidance for peak overlap (bed narrowPeak), quantitative signal (bigWig), differential expression (tsv), and chromatin state (bed) outputs.
When to use it
- When comparing ENCODE experiments across tissues, cell lines, or biosamples
- When investigating tissue-specific enhancers, constitutive elements, cross-tissue differences, or multi-biosample data availability
- When planning cross-tissue or developmental time course analyses with attention to batch effects
What it can touch
- Tools: claude-code
- Files and parameters to consider include: encode_get_facets, encode_search_experiments, encode_track_experiment, encode_compare_experiments, encode_list_files, bedtools intersect, and outputs like IDR-thresholded peaks, fold-change tracks, and gene quantifications.
Caveats
- Emphasizes matching technical parameters to avoid confounding signals; cross-type comparisons require caution due to potential biological and technical confounders
- Batch effects are a major concern; proper mitigation requires consistent pipelines and diligent metadata documentation
- Blacklist filtering is required before comparison to avoid artifactual tissue-constitutive signals
- The skill notes that same biosample-type is preferred and cross-type comparisons should be interpreted with awareness of potential confounders
# Compare ENCODE Data Across Biosamples ## When to Use - User wants to compare ENCODE experiments across different tissues, cell lines, or biosamples - User asks about "tissue comparison", "cell-type differences", "tissue-specific enhancers", or "cross-tissue" - User needs to identify constitutive vs tissue-specific regulatory elements - User wants to map data availability across multiple biosamples before integrative analysis - Example queries: "compare H3K27ac between liver and pancreas", "what marks are tissue-specific?", "find constitutive promoters across all tissues" Help the user systematically compare data availability and experiments across different biosamples to identify tissue-specific regulatory patterns, constitutive elements, and cross-tissue differences. ## Scientific Rationale Cross-biosample comparison is the foundation of understanding tissue-specific gene regulation. Regulatory elements -- particularly enhancers -- are the primary drivers of cell-type identity, with promoters being largely shared across tissues. Comparing the same assay across multiple biosamples reveals which regulatory elements are constitutive (shared) versus tissue-specific (unique to one or
- When to Use
- Scientific Rationale
- Literature Foundation
- Tissue-Specific Regulation Principles
- What Is Shared vs Tissue-Specific (Heintzman 2009; Andersson 2014)
- Key Insight
- ENCODE Biosample Hierarchy
- Tier 1 Cell Lines (Most Comprehensive ENCODE Data)
- Biosample Comparability Rules
- Step 1: Define the Comparison Design
- Comparison Design Patterns
- Step 2: Map Data Availability Across Biosamples
- 2a. Check What Exists for Each Biosample
- 2b. Check What Exists for a Specific Assay Across Tissues
bedtools intersect -v -a liver_peaks.bed -b hepg2_peaks.bed > liver_specific.bed bedtools intersect -v -a hepg2_peaks.bed -b liver_peaks.bed > hepg2_specific.bed bedtools intersect -a liver_peaks.bed -b hepg2_peaks.bed > shared_peaks.bed
What does the compare-biosamples skill do?
Compare ENCODE experiments across different biosamples, tissues, or cell lines to identify tissue-specific regulatory patterns. Use when the user wants cross-tissue comparison, cell-type comparison, tissue-specific elements, differential chromatin, biosample matching, disease vs normal comparison, developmental time course, constitutive vs variable regulation, or multi-tissue data availability mapping. Handles batch effect detection, biosample hierarchy, and comparison design.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill compare-biosamples-ammawla-encode-toolkit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.