functional-screen-analysis
Analyze ENCODE functional genomics screens including CRISPR screens, MPRA (Massively Parallel Reporter Assays), and STARR-seq. Find screen data in ENCODE, process results, identify functional elements, and integrate with epigenomic annotations.
npx skills add majiayu000/claude-skill-registry --skill functional-screen-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
Interpret ENCODE functional genomics screens (CRISPR, MPRA, STARR-seq) to identify functionally validated regulatory elements and relate them to epigenomic annotations.
How it works
- Provides methods to find ENCODE CRISPR screen data via encode_search_experiments(assay_title="CRISPR screen") and to list related files with encode_list_files(...).
- Describes MPRA and STARR-seq data outputs and typical analyses, including barcode counting, normalization, activity scoring, and allelic comparisons, with pipelines like MPRAflow (nextflow run MPRAflow/MPRAflow.nf).
- Details analytical steps for CRISPR screen analysis: filtering guides, normalization, statistical testing (MAGeCK, BAGEL2 or custom models), hit calling with FDR, and integration with ENCODE cCREs and epigenomic marks.
- Provides commands for integrating hits with histone marks (e.g., H3K27ac) and ATAC-seq data, including encode_search_experiments calls and encode_list_files to obtain IDR peaks, followed by bedtools intersect for overlap enrichment.
- Includes data types and formats for each assay (e.g., TSVs for counts and quantifications, BED for peaks, bigWig tracks) and example workflows.
When to use it
- When the user wants to find or analyze CRISPR screen data in ENCODE, MPRA results, or STARR-seq results.
- When the user aims to identify functionally validated regulatory elements and integrate results with epigenomic annotations.
- When the user needs to compare functional screens across assays and relate activity to chromatin context.
What it can touch
- Tools: claude-code (as declared).
- Data types: CRISPR guide counts (TSV), element quantifications (TSV), differential expression (TSV), barcode counts (DNA and RNA TSVs), activity scores (TSV), BAM/FASTQ for STARR-seq inputs, bigWig tracks, BED files for peaks.
- External utilities: MAGeCK, BAGEL2 command lines, nextflow (MPRAflow), bedtools intersect.
Caveats
- The skill references limitations and context: CRISPRi repression windows (1–2 kb) and TSS proximity bias; MPRA context is episomal, which may affect activity interpretation; STARR-seq is also episomal with position effects.
- No outcomes are guaranteed; typical thresholds and interpretations are described (e.g., FDR < 0.05 and |log2FC| > 1.0 for MPRA significance) but the exact criteria may vary by experiment.
# Analyze ENCODE Functional Genomics Screens ## When to Use - User wants to find or analyze CRISPR screen, MPRA, or STARR-seq data from ENCODE - User asks about "functional screens", "CRISPR perturbation", "reporter assay", or "enhancer validation" - User needs to identify functionally validated regulatory elements from screen results - User wants to integrate screen results with epigenomic annotations (ChIP-seq, ATAC-seq peaks) - Example queries: "find CRISPR screen data in ENCODE", "analyze MPRA results for enhancer activity", "which regulatory elements have functional validation?" Discover and interpret functional validation data from CRISPR screens, MPRA (Massively Parallel Reporter Assays), and STARR-seq experiments in the ENCODE catalog. These assays directly test whether candidate regulatory elements have functional activity, complementing the correlative evidence from ChIP-seq, ATAC-seq, and Hi-C. ## Scientific Rationale **The question**: "Which of the candidate regulatory elements identified by ENCODE actually have functional activity, and what genes do they regulate?" The central challenge in regulatory genomics is that biochemical signatures (histone marks, chromatin acc
- When to Use
- Scientific Rationale
- The Validation Gap
- Assay Comparison
- Literature Support
- Finding ENCODE Screen Data
- CRISPR Screens
- MPRA Experiments
- STARR-seq Experiments
- Combined Discovery
- CRISPR Screen Analysis
- Data Types and File Formats
- Analytical Workflow
- Key Analysis Tools
Count sgRNAs from FASTQ mageck count -l library.tsv -n experiment \ Test for enrichment/depletion mageck test -k experiment.count.txt \ Calculate Bayes Factors BAGEL.py fc -i counts.txt -o foldchange.txt -c control_columns BAGEL.py bf -i foldchange.txt -o bayes_factors.txt \ BAGEL.py pr -i bayes_factors.txt -o precision_recall.txt Intersect CRISPR hits with H3K27ac peaks bedtools intersect -a crispr_hits.bed -b h3k27ac_peaks.bed -wa -wb > hits_in_enhancers.bed
What does the functional-screen-analysis skill do?
Analyze ENCODE functional genomics screens including CRISPR screens, MPRA (Massively Parallel Reporter Assays), and STARR-seq. Find screen data in ENCODE, process results, identify functional elements, and integrate with epigenomic annotations.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill functional-screen-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.