batch-analysis
Guide for multi-experiment batch operations: QC screening, batch download, comparison, and report generation across many ENCODE experiments simultaneously. Use when users need to process 5+ experiments together, create experiment comparison tables, perform batch quality checks, or generate summary reports. Trigger on: batch analysis, multiple experiments, bulk processing, experiment comparison, batch QC, multi-sample, batch download, experiment table, summary report, collection analysis.
npx skills add majiayu000/claude-skill-registry --skill batch-analysis-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
Guides batch operations across ENCODE experiments, including discovery, QC screening, batch download, pairwise comparison, and report generation for multiple experiments.
How it works
- Starts with Systematic Experiment Discovery: use encode_get_facets to understand scope, then search with encode_search_experiments to obtain candidates. For each candidate, retrieve details with encode_get_experiment(accession="ENCSR...").
- Builds an Experiment Table listing Accession, Target, Biosample, Lab, Replicates, Audit Status, Date Released, and other fields.
- Applies Quality Screening Criteria: excludes Audit level = ERROR or Assembly mismatch or 0 replicates; flags for Audit level = NOT_COMPLIANT, Single replicate, FRiP < 1%, NRF < 0.8, NSC < 1.05, RSC < 0.8; uses Tier 1/2/3/Exclude categories.
- Identifies Batch Variables: groups by Lab, Date Released, Pipeline Version, Sequencing Platform, Library prep method.
- Proceeds to Batch Download with a Dry Run via encode_batch_download(..., dry_run=True) to preview, then executes download with organize_by options (flat, experiment, format, experiment_format) and verify_md5. Handles failures by retrying failed files and checking portal status.
- Manages Storage Planning with typical sizes per file type and suggests downloading signal tracks and peaks first.
- Performs Batch Comparison: track experiments with encode_track_experiment, compute pairwise compatibility with encode_compare_experiments, and build a compatibility matrix across dimensions like Assembly match, Target match, Biosample compatibility, Lab concordance, and Pipeline version.
- Identifies Batch Effects via encode_summarize_collection and uses downstream analyses (deepTools) to assess clustering and correlation; offers guidance to correct if clustering is by lab.
- Generates a Report: export experiment table with encode_export_data(format="csv"), summarize with encode_summarize_collection, and draft methods via encode_get_citations(export_format="bibtex").
- Logs provenance via encode_log_derived_file for visualizations and metadata tables, and links references using encode_link_reference for pmid, geo_accession, and preprint DOIs.
When to use it
- User wants to process, compare, or QC multiple ENCODE experiments simultaneously
- User asks about "batch analysis", "bulk processing", "experiment comparison table", or "multi-sample QC"
- User needs to screen 5+ experiments for quality before analysis
- User wants a summary report or comparison table across many experiments
What it can touch
- Tools mentioned: encode_get_facets, encode_search_experiments, encode_get_experiment, encode_batch_download, encode_track_experiment, encode_compare_experiments, encode_summarize_collection, multiBigwigSummary, plotCorrelation, plotPCA, encode_export_data, encode_log_derived_file, encode_link_reference, encode_get_citations
- File and directory organization options include organize_by values: flat, experiment, format, experiment_format
Caveats
- The workflow depends on ENCODE MCP tools and standard QC criteria; explicit thresholds and categorizations are defined in the steps, but outcomes depend on the input data quality and batch variables.
- Batch effects assessment assumes availability of metadata such as Lab, Date Released, and Pipeline Version; unresolved confounding (lab-based) may require correction strategies not specified here.
# Batch Analysis of ENCODE Experiments ## When to Use - User wants to process, compare, or QC multiple ENCODE experiments simultaneously - User asks about "batch analysis", "bulk processing", "experiment comparison table", or "multi-sample QC" - User needs to screen 5+ experiments for quality before analysis - User wants a summary report or comparison table across many experiments - Example queries: "QC all H3K27ac experiments in liver", "compare quality across 10 ChIP-seq datasets", "batch download and summarize my experiment collection" Help the user perform systematic batch operations across multiple ENCODE experiments. When working with 5 or more experiments -- common in cross-tissue comparisons, multi-mark epigenomic profiling, or large-scale data collection -- individual experiment-by-experiment workflows become impractical and error-prone. This skill covers batch discovery, quality screening, download management, pairwise comparison, and report generation using the ENCODE MCP tools. ## Literature Foundation | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | ENCODE Project Consortium (2020) | Nature | Exp
- When to Use
- Literature Foundation
- Part 1: Batch Discovery and QC Screening
- 1a. Systematic Experiment Discovery
- 1b. Building the Experiment Table
- 1c. Quality Screening Criteria
- 1d. Identifying Batch Variables
- Part 2: Batch Download
- 2a. Dry Run First
- 2b. Organizing Downloads
- 2c. Downloading Multiple File Types
- 2d. Handling Download Failures
- 2e. Storage Planning
- Part 3: Batch Comparison
Build signal matrix across all experiments multiBigwigSummary bins \ Correlation heatmap (should cluster by tissue, not lab) plotCorrelation -in batch_matrix.npz \ PCA (PC1 should separate biology, not batch) plotPCA -in batch_matrix.npz \
What does the batch-analysis skill do?
Guide for multi-experiment batch operations: QC screening, batch download, comparison, and report generation across many ENCODE experiments simultaneously. Use when users need to process 5+ experiments together, create experiment comparison tables, perform batch quality checks, or generate summary reports. Trigger on: batch analysis, multiple experiments, bulk processing, experiment comparison, batch QC, multi-sample, batch download, experiment table, summary report, collection analysis.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill batch-analysis-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.