/data-provenance

name: data-provenance
description: Track exact provenance for every operation on ENCODE data — tool versions, reference files, scripts, parameters, and timestamps — to enable publication-ready methods writing. Use when the user processes ENCODE files, runs any bioinformatics tool, creates filtered/merged datasets, runs pipelines, performs liftover, uses R/Python/Bash for analysis, or needs to document their analysis chain for reproducibility and publication. Also use when the user says "write me methods" to auto-generate methods sections from the provenance log. This skill implements comprehensive provenance documentation: every tool, every version, every reference file, every parameter, every accession — no shortcuts. Use this skill for ANY processing step, ANY file transformation, ANY analysis operation on ENCODE data.

Exact Provenance Tracking and Methods Writing

When to Use

• User wants to track the full analysis chain from ENCODE download through processing to publication figure
• User asks about "provenance", "reproducibility", "methods section", or "analysis log"
• User needs to log derived files with their processing parameters for audit trail
• User wants to auto-generate publication-ready methods text from logged analysis steps
• Example queries: "log my peak calling step", "generate a methods section from my analysis", "show the provenance chain for this figure"

Track every operation on ENCODE data with exact tool versions, reference files, scripts, parameters, and timestamps to enable publication-ready methods sections.

Scientific Rationale

**The question**: "What exactly was done to this data, and can someone else reproduce it identically?"

Reproducibility is the foundation of science. Yet the "Methods" sections of most genomics papers are vague — "reads were aligned with STAR" tells you nothing about which STAR version, which genome index, which parameters, or which annotation version was used. The difference between GENCODE v38 and v39 gene annotations can change thousands of gene assignments.

Comprehensive Provenance Standard

This skill implements a documentation standard where every operation records: 1. **Tool**: Exact name and version (e.g., `bedtools v2.31.0`, not just "bedtools") 2. **Reference files**: Exact source, version, and download URL (e.g., "GRCh38.p14 chromosome sizes from UCSC, downloaded 2024-01-15") 3. **Parameters**: Complete command or function call, not just key parameters 4. **Input**: Exact file paths, accessions, and checksums 5. **Output**: File path, description, and checksum 6. **Script**: If a custom script was used, store the script alongside the output 7. **Timestamp**: When the operation was performed 8. **Environment**: R version, Python version, package versions, OS

This creates a complete audit trail such that a methods section can be auto-generated with zero ambiguity.

Why This Level of Detail Matters

Consider a simple liftover operation. A vague log says "coordinates were lifted from hg19 to hg38." A comprehensive provenance log says:

> "Genomic coordinates were lifted from GRCh37/hg19 to GRCh38/hg38 using UCSC liftOver (v377, Kent et al. 2002, PMID: 12045153). The chain file hg19ToHg38.over.chain.gz was obtained from UCSC Genome Browser (https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/, accessed 2024-01-15, MD5: abc123...). Of 45,231 input regions, 44,892 (99.25%) were successfully converted; 339 regions (0.75%) failed to map and were excluded. Unmapped regions were logged to unmapped.bed."

The second version can be reproduced exactly. The first cannot.

Step 1: Initialize Experiment Log

At the start of any analysis session, create an experiment log:

Log Structure

project_dir/
├── experiment_log.json          # Machine-readable provenance log
├── scripts/                     # All scripts used in this analysis
│   ├── 001_download.sh
│   ├── 002_filter_peaks.sh
│   └── 003_merge_samples.R
├── reference_files/             # Reference files used (or symlinks)
│   ├── GRCh38.chrom.sizes
│   └── gencode.v44.annotation.gtf
├── data/                        # ENCODE downloads
│   └── (organized by experiment)
├── derived/                     # All derived files
│   ├── filtered_peaks/
│   └── merged_results/
└── methods/                     # Auto-generated methods text
    └── methods_draft.md

Experiment Log Format (experiment_log.json)

{
  "project": "H3K27ac analysis in human pancreas",
  "created": "2024-01-15T10:30:00Z",
  "analyst": "Dr. A. Mawla",
  "organism": "Homo sapiens",
  "assembly": "GRCh38",
  "gene_annotation": "GENCODE v44",
  "operations": [],
  "encode_experiments": [],
  "software_environment": {},
  "reference_files": []
}

Track Source ENCODE Experiments

encode_track_experiment(accession="ENCSR...", notes="Experiment log entry")

For each experiment, record in the log: | Field | Example | Source | |-------|---------|--------| | Accession | ENCSR133RZO | ENCODE portal | | Assay | Histone ChIP-seq | encode_get_experiment | | Target | H3K27ac | encode_get_experiment | | Biosample | pancreas tissue | encode_get_experiment | | Lab | Bing Ren, UCSD | encode_get_experiment | | Replicates | 2 biological | encode_get_experiment | | Sequencer | Illumina HiSeq 4000 | encode_get_experiment | | Read length | 76bp PE | encode_get_experiment | | Read count | 42.3M per rep | File metadata | | Library | TruSeq ChIP | encode_get_experiment | | Batch/date | 2019-06-15 | encode_get_experiment |

Step 2: Log Every Operation

Operation Log Entry Format

Every processing step creates a log entry with these fields:

{
  "operation_id": "op_003",
  "timestamp": "2024-01-15T14:22:00Z",
  "description": "Filter H3K27ac peaks by signalValue",
  "category": "filtering",
  "tool": {
    "name": "bedtools",
    "version": "2.31.0",
    "citation": "Quinlan & Hall 2010, Bioinformatics, DOI:10.1093/bioinformatics/btq033"
  },
  "command": "awk '$7