Track Experiments

Track ENCODE Experiments

When to Use

• User wants to save/bookmark ENCODE experiments for later reference
• User needs to build a collection of experiments for a project
• User asks to "track", "save", or "bookmark" an experiment
• User wants to manage citations and publications for ENCODE data
• User needs to compare experiments for compatibility
• User wants to export their experiment collection as CSV/TSV/JSON
• User asks about data provenance (linking derived files to ENCODE sources)

Help the user manage their local collection of ENCODE experiments. This skill covers the full lifecycle of experiment management: discovery, tracking, annotation, citation, comparison, provenance, and export.

Tracking Capabilities

1. Track an experiment: Use encode_track_experiment to save experiment metadata, publications, and pipeline info locally.

   • Automatically extracts GEO accessions and PMIDs from experiment metadata
   • Fetches associated publications with authors, journal, DOI
   • Stores 18 metadata fields per experiment (see schema below)
   • Idempotent: re-tracking the same accession updates metadata without creating duplicates

2. View tracked collection: Use encode_list_tracked to see all tracked experiments. Filter by assay, organism, or organ.

3. Get citations: Use encode_get_citations to export publication data.

   • "json": Structured data
   • "bibtex": For LaTeX/reference managers
   • "ris": For Endnote, Zotero, Mendeley

4. Compare experiments: Use encode_compare_experiments to check if two experiments are compatible for combined analysis (same organism, assembly, assay, biosample, etc.).

5. Collection overview: Use encode_summarize_collection for grouped statistics across your tracked experiments.

6. Export data: Use encode_export_data to export tracked experiments as CSV, TSV, or JSON for use in R, pandas, Excel.

Stored Metadata

When you track an experiment, the following fields are captured from the ENCODE Portal API and stored locally:

Additionally, the tracker stores timestamps (tracked_at, updated_at) for audit trail purposes.

SQLite Schema Overview

The tracker uses a local SQLite database with WAL journal mode and foreign keys enabled. The schema consists of six tables:

tracked_experiments -- One row per ENCODE experiment. The accession column is the primary key. Indexes on assay_title, organism, and organ for fast filtered queries.

publications -- Publications linked to experiments. Stores PMID, DOI, title, authors (first 10), journal, year, abstract. Unique constraint on (experiment_accession, pmid) prevents duplicates.

pipeline_info -- ENCODE uniform processing pipeline details. Stores pipeline title, version, software list (as JSON array), and analysis status.

quality_metrics -- Per-file quality metrics from ENCODE audits. Stores file accession, metric type, and metric data (as JSON).

derived_files -- User-created files derived from ENCODE data. Stores file path, source accessions (as JSON array), tool used, parameters, and description. This is the backbone of provenance tracking.

external_references -- Cross-database links. Stores reference type (pmid, doi, geo_accession, nct_id, biorxiv_doi, dbgap), reference ID, and description. Unique constraint on (experiment_accession, reference_type, reference_id).

The database location is ~/.encode_connector/tracker.db (macOS/Linux) or %USERPROFILE%\.encode_connector\tracker.db (Windows). The directory is created automatically on first use.

Data Provenance

7. Log derived files: Use encode_log_derived_file when the user creates files from ENCODE data (filtered peaks, merged signals, etc.).

8. View provenance: Use encode_get_provenance to trace derived files back to source ENCODE data.

Cross-References

9. Link external references: Use encode_link_reference to attach PubMed IDs, DOIs, ClinicalTrials NCT IDs, bioRxiv DOIs, or GEO accessions to tracked experiments.

10. Get references: Use encode_get_references to retrieve linked external identifiers. These IDs can be passed to PubMed, bioRxiv, or ClinicalTrials MCP servers for further analysis.

Walkthrough 1: Building a Pancreatic Islet Epigenome Reference Collection

Goal: Curate a comprehensive set of histone modification ChIP-seq, ATAC-seq, and RNA-seq from human pancreatic islets for enhancer analysis. This is the foundational workflow for any tissue-specific integrative analysis.

Step 1: Discover what data exists

Before tracking anything, survey the landscape. Use facets to understand the breadth of available data for your tissue of interest.

encode_get_facets(facet_field="assay_title", organ="pancreas", organism="Homo sapiens")

Expected output (example):

Histone ChIP-seq: 15 experiments
ATAC-seq: 3 experiments
RNA-seq: 8 experiments
TF ChIP-seq: 4 experiments
WGBS: 2 experiments
DNase-seq: 1 experiment

This tells you that pancreatic tissue has strong histone ChIP-seq coverage (15 experiments across multiple marks), adequate ATAC-seq (3), and solid RNA-seq (8). The 2 WGBS experiments are a bonus for methylation analysis.

Step 2: Search for histone ChIP-seq experiments

Now retrieve the actual experiments. Focus on one assay type at a time to keep notes organized.

encode_search_experiments(assay_title="Histone ChIP-seq", organ="pancreas", organism="Homo sapiens")

Expected return: 15 experiments with targets including H3K27ac, H3K4me1, H3K4me3, H3K27me3, H3K36me3. Review the biosample summaries -- some may be whole pancreas tissue, others isolated islets, and others acinar or ductal cells. This distinction matters for enhancer analysis.

Step 3: Track each histone experiment with descriptive notes

Notes are your lab notebook. Record the histone mark, the specific biosample, and the intended analytical role. This context is invaluable weeks later when you revisit the collection.

encode_track_experiment(accession="ENCSR123ABC", notes="H3K27ac pancreatic islets - active enhancers and super-enhancers")
encode_track_experiment(accession="ENCSR456DEF", notes="H3K4me1 pancreatic islets - primed/poised enhancers")
encode_track_experiment(accession="ENCSR789GHI", notes="H3K4me3 pancreatic islets - active promoters, CpG islands")
encode_track_experiment(accession="ENCSR012JKL", notes="H3K27me3 pancreatic islets - Polycomb repression, bivalent domains")
encode_track_experiment(accession="ENCSR345MNO", notes="H3K36me3 pancreatic islets - gene body transcription elongation")

Why these five marks? Together they define the core chromatin states:

• H3K27ac marks active enhancers and promoters (the primary mark for enhancer identification)
• H3K4me1 marks primed enhancers (H3K4me1-positive, H3K27ac-negative = poised enhancer)
• H3K4me3 marks active promoters (distinguishes promoters from enhancers)
• H3K27me3 marks Polycomb-repressed regions (identifies bivalent promoters when co-occurring with H3K4me3)
• H3K36me3 marks actively transcribed gene bodies (helps define transcription units)

Step 4: Add chromatin accessibility data

ATAC-seq or DNase-seq provides an orthogonal measure of regulatory element activity. Open chromatin overlapping H3K27ac peaks gives higher confidence enhancer calls.

encode_search_experiments(assay_title="ATAC-seq", organ="pancreas", organism="Homo sapiens")
encode_track_experiment(accession="ENCSR...", notes="ATAC-seq pancreatic islets - open chromatin map, enhancer validation")

If ATAC-seq is unavailable, check for DNase-seq:

encode_search_experiments(assay_title="DNase-seq", organ="pancreas", organism="Homo sapiens")

Both measure chromatin accessibility. ATAC-seq is preferred for newer datasets due to lower input requirements and nucleosome positioning information from fragment sizes.

Step 5: Add gene expression data

RNA-seq anchors the epigenomic data to functional output. Active enhancers (H3K27ac) near expressed genes have higher regulatory confidence.

encode_search_experiments(assay_title="total RNA-seq", organ="pancreas", organism="Homo sapiens")
encode_track_experiment(accession="ENCSR...", notes="RNA-seq pancreatic islets - gene expression baseline for enhancer-gene linking")

If multiple RNA-seq experiments exist, prefer those from the same lab or biosample batch as your ChIP-seq experiments. Matched samples reduce technical variability.

Step 6: Review the full collection

After tracking all experiments, get the bird's-eye view.

encode_summarize_collection()

Expected output:

Total experiments: 8
By assay: Histone ChIP-seq (5), ATAC-seq (1), RNA-seq (2)
By organ: pancreas (8)
By organism: Homo sapiens (8)
By target: H3K27ac (1), H3K4me1 (1), H3K4me3 (1), H3K27me3 (1), H3K36me3 (1), none (2)
By biosample_type: tissue (5), primary cell (3)
By lab: /labs/bradley-bernstein/ (3), /labs/john-stamatoyannopoulos/ (2), /labs/michael-snyder/ (3)
Total publications: 4
Total derived files: 0
Total external references: 0

Check the summary for consistency:

• All experiments should be the same organism (Homo sapiens) and organ (pancreas)
• Biosample types should be compatible (tissue and primary cells from the same organ are generally fine)
• Multiple labs is normal but means batch effects are possible -- note this for downstream analysis

Step 7: Export for spreadsheet review

Create a permanent record outside the database for lab notebook documentation, sharing with collaborators, or loading into R/pandas.

encode_export_data(format="csv")

This produces a CSV with columns: accession, assay_title, target, organism, organ, biosample_type, biosample_summary, lab, assembly, status, date_released, replication_type, life_stage, publication_count, pmids, derived_file_count, external_reference_count.

Save this CSV alongside your analysis scripts. It is the metadata backbone of your study.

Walkthrough 2: From Tracked Experiments to LaTeX Bibliography

Goal: Generate a complete bibliography for a manuscript using tracked experiments. Every ENCODE experiment should cite its associated publications, and the bibliography should be formatted for your reference manager.

Step 1: Track experiments used in analysis (if not already tracked)

If you have been building your collection following Walkthrough 1, your experiments are already tracked. If starting fresh:

encode_track_experiment(accession="ENCSR123ABC", notes="Used in Figure 3A - H3K27ac peaks")
encode_track_experiment(accession="ENCSR456DEF", notes="Used in Figure 3B - H3K4me1 peaks")
encode_track_experiment(accession="ENCSR789GHI", notes="Used in Table S1 - expression values")

Step 2: Verify all publications were fetched

encode_list_tracked()

Check the "publications" column. A count of 0 means publications were not fetched at tracking time. This can happen if fetch_publications=False was passed or if the ENCODE Portal had a temporary issue.

Step 3: Re-track with publication fetch if needed

If any experiment shows 0 publications, re-track it. Tracking is idempotent -- it updates metadata without duplicating the experiment record.

encode_track_experiment(accession="ENCSR123ABC", fetch_publications=True)

The publications table uses a unique constraint on (experiment_accession, pmid), so re-fetching is safe and will not create duplicate publication records.

Step 4: Export BibTeX for LaTeX

encode_get_citations(export_format="bibtex")

Returns entries like:

@article{12345678,
  title = {Genome-wide maps of chromatin state in pancreatic islets},
  author = {Smith J, Jones K, ...},
  journal = {Nature},
  year = {2020},
  doi = {10.1038/...},
  pmid = {12345678},
  note = {ENCODE experiment: ENCSR123ABC},
}

Save this to a .bib file and include it in your LaTeX document with \bibliography{encode_refs}.

Step 5: Export RIS for Zotero/Mendeley

encode_get_citations(export_format="ris")

Returns entries like:

TY  - JOUR
TI  - Genome-wide maps of chromatin state in pancreatic islets
AU  - Smith J
AU  - Jones K
JO  - Nature
PY  - 2020
DO  - 10.1038/...
AN  - PMID:12345678
N1  - ENCODE experiment: ENCSR123ABC
ER  -

Import this .ris file directly into Zotero, Mendeley, or Endnote. The ENCODE experiment accession is preserved in the notes field for traceability.

Step 6: Add consortium papers manually

Publications extracted from individual experiments are the papers that generated that specific dataset. You also need to cite the ENCODE consortium papers that describe the project itself. These are NOT auto-extracted because they are project-level, not experiment-level.

Essential consortium citations:

• ENCODE Phase 3: ENCODE Project Consortium. Nature 583, 699-710 (2020). DOI: 10.1038/s41586-020-0636-z
• ENCODE Phase 2: ENCODE Project Consortium. Nature 489, 57-74 (2012). DOI: 10.1038/nature11247
• ENCODE Portal: Hitz et al. Nucleic Acids Research 51, D853-D858 (2023). DOI: 10.1093/nar/gkac1067

Add these to your .bib or .ris file manually, or link them using encode_link_reference:

encode_link_reference(experiment_accession="ENCSR123ABC", reference_type="doi", reference_id="10.1038/s41586-020-0636-z", description="ENCODE Phase 3 consortium paper")

Walkthrough 3: Full Analysis Provenance -- From ENCODE to Publication Figure

Goal: Track every step from downloading ENCODE data to generating a publication figure. Complete provenance enables reproducibility and auto-generation of methods sections.

Step 1: Track source experiment

encode_track_experiment(accession="ENCSR123ABC", notes="H3K27ac source for Figure 3A - pancreatic islet active enhancers")

Step 2: Log the peak calling step

After running peak calling on the downloaded BAM files:

encode_log_derived_file(
  file_path="/analysis/h3k27ac_peaks.narrowPeak",
  source_accessions=["ENCSR123ABC"],
  description="MACS2 narrow peaks from H3K27ac ChIP-seq, 2 biological replicates pooled",
  tool_used="MACS2 v2.2.7.1",
  parameters="--gsize hs --qvalue 0.05 --keep-dup all --call-summits --bdg --SPMR"
)

Note: Record the exact version and every parameter. Omitting --keep-dup all vs the default --keep-dup 1 produces dramatically different peak sets. Future you (or a reviewer) needs this.

Step 3: Log the filtering step

Apply ENCODE Blacklist v2 and restrict to canonical chromosomes:

encode_log_derived_file(
  file_path="/analysis/h3k27ac_peaks_filtered.bed",
  source_accessions=["ENCSR123ABC"],
  description="Filtered peaks: ENCODE blacklist v2 removed (Amemiya et al. 2019), canonical chromosomes only (chr1-22, chrX). Input: 45,231 peaks. Output: 42,876 peaks (94.8% retained).",
  tool_used="bedtools subtract v2.31.0 + grep",
  parameters="bedtools subtract -A -a peaks.narrowPeak -b hg38-blacklist.v2.bed | grep -E '^chr([0-9]+|X)\t' > peaks_filtered.bed"
)

Record input and output counts at every filtering step. This is essential for methods sections and reviewer response letters.

Step 4: Log the intersection with regulatory elements

encode_log_derived_file(
  file_path="/analysis/enhancer_peaks.bed",
  source_accessions=["ENCSR123ABC"],
  description="H3K27ac peaks overlapping pancreatic islet enhancers from Roadmap Epigenomics (E087). Input: 42,876 peaks. Overlapping enhancers: 18,432 (43.0%).",
  tool_used="bedtools intersect v2.31.0",
  parameters="-a h3k27ac_peaks_filtered.bed -b E087_15_coreMarks_hg38lift_dense.bed -u -f 0.5"
)

Step 5: Log the figure generation step

encode_log_derived_file(
  file_path="/figures/figure_3A_enhancer_heatmap.pdf",
  source_accessions=["ENCSR123ABC"],
  description="Heatmap of H3K27ac signal at pancreatic islet enhancers, sorted by signal intensity. 18,432 enhancers, +/- 3kb window, 50bp bins.",
  tool_used="deepTools computeMatrix + plotHeatmap v3.5.2",
  parameters="computeMatrix reference-point -S h3k27ac.bw -R enhancer_peaks.bed -a 3000 -b 3000 --binSize 50 -o matrix.gz && plotHeatmap -m matrix.gz --colorMap RdYlBu_r --refPointLabel 'Enhancer center' -o figure_3A.pdf"
)

Step 6: Verify complete provenance chain

encode_get_provenance(file_path="/figures/figure_3A_enhancer_heatmap.pdf")

This returns the full chain:

figure_3A_enhancer_heatmap.pdf
  <- deepTools computeMatrix + plotHeatmap v3.5.2
  <- enhancer_peaks.bed
    <- bedtools intersect v2.31.0
    <- h3k27ac_peaks_filtered.bed
      <- bedtools subtract v2.31.0 + grep
      <- h3k27ac_peaks.narrowPeak
        <- MACS2 v2.2.7.1
        <- ENCSR123ABC (ENCODE Portal)

Every step from raw ENCODE data to final figure is documented with tool versions and parameters. This chain can be used to:

• Write the Data Processing section of your manuscript (see scientific-writing skill)
• Respond to reviewer requests for methodology details
• Reproduce the analysis months or years later
• Share the exact workflow with collaborators

Walkthrough 4: Checking Compatibility Before Integrative Analysis

Goal: Verify that two experiments can be combined for differential analysis. Mixing incompatible experiments produces silent errors that invalidate results.

Step 1: Track both experiments

encode_track_experiment(accession="ENCSR111AAA", notes="H3K27ac normal pancreas - control condition")
encode_track_experiment(accession="ENCSR222BBB", notes="H3K27ac diabetic pancreas - disease condition")

Step 2: Run comparison

encode_compare_experiments(accession1="ENCSR111AAA", accession2="ENCSR222BBB")

The comparison checks eight fields and returns a structured report:

Verdict: COMPATIBLE_WITH_CAVEATS

Compatible aspects:
  - Same organism: Homo sapiens
  - Same assembly: GRCh38
  - Same assay: Histone ChIP-seq
  - Same target: H3K27ac-human

Warnings:
  - Different biosample types: tissue vs primary cell
    (Results may reflect sample type differences)
  - Different labs: /labs/bradley-bernstein/ vs /labs/michael-snyder/
    (Batch effects possible)

Issues: (none)

Recommendation: These experiments can be compared, but the warnings
should be addressed in your analysis.

Step 3: Interpret results

The comparison produces three categories:

Compatible aspects (green): Fields that match between the two experiments. These are safe dimensions.

Warnings (yellow): Fields that differ but do not necessarily prevent combined analysis. Each warning needs a decision:

• Biosample type mismatch (tissue vs primary cell): The primary cell isolation process can alter chromatin state. If the tissue and isolated cells are from the same organ, this is often acceptable with a caveat in your methods. If comparing whole pancreas tissue to isolated islets, your results may reflect cell type composition rather than disease state.
• Lab mismatch: Different labs use different protocols, antibody lots, and sequencing platforms. Consider batch correction (ComBat, limma::removeBatchEffect) and report the lab difference in your methods.
• Life stage mismatch (adult vs fetal): Chromatin states differ dramatically between developmental stages. Only combine if the research question specifically involves developmental comparison.
• Replication type mismatch (isogenic vs anisogenic): Different replicate strategies affect statistical power differently. Anisogenic replicates capture biological variation; isogenic replicates do not.

Issues (red): Fields that are fundamentally incompatible:

• Organism mismatch: Do not combine human and mouse data directly. Requires ortholog mapping and synteny analysis.
• Assembly mismatch: Do not combine GRCh38 and hg19 coordinates. Use liftover-coordinates skill to convert first.
• Assay mismatch: Different assays measure different things. ChIP-seq and ATAC-seq cannot be directly compared -- they require multi-omic integration approaches.

Step 4: Decision framework

Walkthrough 5: Cross-Referencing with External Databases

Goal: Link tracked ENCODE experiments to GEO, PubMed, ClinicalTrials.gov, and other databases for a complete research record.

Step 1: Link to GEO

Many ENCODE experiments have corresponding GEO submissions. Linking them enables fetching supplementary data not available on the ENCODE Portal.

encode_link_reference(
  experiment_accession="ENCSR123ABC",
  reference_type="geo_accession",
  reference_id="GSE123456",
  description="GEO submission with supplementary processed files and sample metadata"
)

Use the geo-connector skill to fetch additional data from the linked GEO accession.

Step 2: Link to PubMed

If you find a publication that uses this experiment but is not in the ENCODE Portal metadata (common for recently published papers):

encode_link_reference(
  experiment_accession="ENCSR123ABC",
  reference_type="pmid",
  reference_id="38123456",
  description="2024 paper using this dataset for islet enhancer analysis"
)

Pass this PMID to the PubMed MCP server (search_articles, get_article_metadata) for full citation details.

Step 3: Link to ClinicalTrials.gov

For translational research connecting epigenomic findings to clinical outcomes:

encode_link_reference(
  experiment_accession="ENCSR123ABC",
  reference_type="nct_id",
  reference_id="NCT04567890",
  description="Clinical trial testing epigenetic therapy targeting islet enhancers"
)

Step 4: Link to bioRxiv preprints

For preprints that have not yet been published in a peer-reviewed journal:

encode_link_reference(
  experiment_accession="ENCSR123ABC",
  reference_type="biorxiv_doi",
  reference_id="10.1101/2024.01.15.123456",
  description="Preprint with novel analysis of pancreatic islet enhancer grammar"
)

Step 5: View all references for an experiment

encode_get_references(accession="ENCSR123ABC")

Returns all linked references across all types, giving a complete picture of the external context around this experiment.

Tracking Best Practices

• Track systematically: When building a collection for a study, track all experiments in a single session with consistent notes. This makes the collection coherent and exportable.
• Use notes as a lab notebook: Record the research context (e.g., "Reference dataset for enhancer comparison in T2D islets"). Notes are the only user-facing metadata that distinguish why two similar experiments were tracked.
• Export early and often: After every tracking session, export to CSV (encode_export_data) so you have a snapshot. This protects against accidental database corruption.
• Compare before combining: Always run encode_compare_experiments before combining data from two experiments. Mismatched assemblies or organisms will produce silent errors in downstream analysis.
• Link external references immediately: If you know the GEO accession or PMID at tracking time, link it right away using encode_link_reference. It is easy to forget later.
• Use consistent note conventions: Adopt a standard format for notes across your collection. Example: "[Mark/Assay] [Biosample] - [Analytical purpose]". This makes encode_list_tracked output scannable.
• Track negative results too: If you searched for a specific assay in a tissue and found nothing, note this in a tracked experiment from a related tissue. Example: "H3K27ac whole pancreas - tracked because no islet-specific H3K27ac was available". This documents your decision process.
• Review collection before analysis: Run encode_summarize_collection before starting any integrative analysis to confirm completeness. Are all expected marks present? Are all experiments from the same assembly? Catching gaps early saves hours of debugging.
• Back up the database: Copy ~/.encode_connector/tracker.db to your project directory or version control before major analysis milestones. A SQLite file is a single file and easy to back up.

Pitfalls & Edge Cases

1. Tracking before searching: Do not track experiments blindly. First use encode_search_experiments or encode_get_experiment to confirm the experiment is relevant to the user's research question. Tracking irrelevant experiments clutters the local collection.

2. Not fetching publications: Always use fetch_publications=True (the default) when tracking. Without it, citation export will be incomplete and the user will miss associated papers. Publication metadata is fetched once at tracking time and stored locally.

3. Forgetting to add notes: Use the notes parameter to record WHY this experiment was tracked, e.g., "H3K27ac reference for pancreatic islet enhancer analysis". Notes are searchable and invaluable when revisiting a collection weeks later.

4. Not exporting after tracking: After building a collection, remind users they can export with encode_export_data (CSV/TSV/JSON for spreadsheets or scripts) and encode_get_citations (BibTeX/RIS for reference managers). Tracking without export limits the value of the collection.

5. Comparing untracked experiments: encode_compare_experiments requires both experiments to be tracked first. If the user asks to compare two experiments, check that both are tracked and offer to track them if not.

6. Database location: The SQLite database is stored at ~/.encode_connector/tracker.db. Deleting this file wipes all tracking data permanently. Back up before OS reinstalls, machine migrations, or major environment changes. The database is a single file and can be copied with cp.

7. Concurrent access: The tracker uses a threading lock (threading.Lock) but is designed for single-process access. Running two Claude sessions that both track experiments simultaneously may cause lock contention or WAL checkpoint delays. Best practice: one tracking session at a time. If you need parallel access, copy the database and merge later.

8. Large collections: Collections exceeding 500 experiments may slow encode_summarize_collection because it performs grouped aggregation across all tracked experiments. Use filters (assay_title, organism, organ) to scope the summary to a subset. Export to CSV and use pandas/R for large-scale collection analysis.

9. Publication lag: ENCODE Portal metadata may not include the most recent publications that use an experiment's data. Papers published after the experiment's last metadata update will be missing. If you know of a recent publication, link it manually using encode_link_reference with reference_type="pmid". Periodically re-tracking experiments (encode_track_experiment with the same accession) can pick up newly added publications.

10. GRCh38 vs hg38: These refer to the same human genome assembly. ENCODE uses "GRCh38" consistently in its metadata. If your downstream analysis tools (UCSC tools, IGV, bedtools) expect "hg38", you may need to rename chromosome prefixes or use the liftover-coordinates skill. The genomic coordinates are identical -- only the naming convention differs.

11. Raw metadata size limit: The tracker stores the full API response JSON as raw_metadata, capped at 512KB per experiment. Experiments with extremely large metadata (many files, many analyses) may have their raw metadata truncated. The parsed fields (assay_title, target, etc.) are always stored regardless.

12. Superseded experiments: Some ENCODE experiments are superseded by newer versions with better data or updated processing. The tracker does not automatically detect supersession. Check the experiment status field -- experiments with status="revoked" should be replaced. Use encode_get_experiment to check for superseding experiments.

Code Examples

1. Track, Cite, Export: "Track 5 H3K27ac ChIP-seq experiments and export citations in BibTeX"

Step 1: Track each experiment
  encode_track_experiment(accession="ENCSR123ABC", notes="H3K27ac pancreas tissue rep1")
  encode_track_experiment(accession="ENCSR456DEF", notes="H3K27ac pancreas tissue rep2")
  ... (repeat for all 5)

Step 2: View collection
  encode_list_tracked(assay_title="Histone ChIP-seq")
  -> Shows all tracked ChIP-seq experiments with publication counts

Step 3: Export citations
  encode_get_citations(export_format="bibtex")
  -> Returns BibTeX entries for all publications across tracked experiments

2. Track and Compare: "Track two RNA-seq experiments and check compatibility"

Step 1: Track both experiments
  encode_track_experiment(accession="ENCSR111AAA", notes="RNA-seq pancreas adult")
  encode_track_experiment(accession="ENCSR222BBB", notes="RNA-seq pancreas fetal")

Step 2: Compare compatibility
  encode_compare_experiments(accession1="ENCSR111AAA", accession2="ENCSR222BBB")
  -> Returns compatibility report: organism, assembly, assay match; warns about life_stage difference

3. Track, Link, Provenance: "Track experiment, link to GEO, log derived analysis file"

Step 1: Track the experiment
  encode_track_experiment(accession="ENCSR789XYZ", fetch_publications=True, notes="ATAC-seq liver for enhancer study")

Step 2: Link to GEO
  encode_link_reference(experiment_accession="ENCSR789XYZ", reference_type="geo_accession", reference_id="GSE123456", description="Corresponding GEO submission")

Step 3: Log a derived file
  encode_log_derived_file(
    file_path="/Users/you/analysis/liver_enhancers.bed",
    source_accessions=["ENCSR789XYZ"],
    description="Filtered ATAC-seq peaks overlapping liver enhancers",
    tool_used="bedtools intersect",
    parameters="-a peaks.bed -b enhancers.bed -u"
  )

Step 4: Verify provenance chain
  encode_get_provenance(file_path="/Users/you/analysis/liver_enhancers.bed")
  -> Shows: derived file <- ENCSR789XYZ <- ENCODE

4. Multi-assay collection with summary: "Build a complete epigenome collection and review"

Step 1: Track histone marks
  encode_track_experiment(accession="ENCSR...", notes="H3K27ac liver - active enhancers")
  encode_track_experiment(accession="ENCSR...", notes="H3K4me1 liver - primed enhancers")
  encode_track_experiment(accession="ENCSR...", notes="H3K4me3 liver - active promoters")

Step 2: Track accessibility
  encode_track_experiment(accession="ENCSR...", notes="ATAC-seq liver - open chromatin")

Step 3: Track expression
  encode_track_experiment(accession="ENCSR...", notes="RNA-seq liver - gene expression")

Step 4: Track methylation
  encode_track_experiment(accession="ENCSR...", notes="WGBS liver - DNA methylation")

Step 5: Summarize the collection
  encode_summarize_collection()
  -> Total experiments: 6
  -> By assay: Histone ChIP-seq (3), ATAC-seq (1), RNA-seq (1), WGBS (1)
  -> By organ: liver (6)
  -> Completeness check: all major data types represented

Step 6: Export the full collection
  encode_export_data(format="csv")
  -> CSV with all 6 experiments, metadata, publication counts
  encode_export_data(format="json")
  -> JSON for programmatic use in Python/R scripts

5. Re-tracking to update metadata: "Refresh stale experiment metadata"

Step 1: Check current metadata
  encode_list_tracked()
  -> Notice ENCSR123ABC has status="submitted" (pre-release)

Step 2: Re-track to refresh
  encode_track_experiment(accession="ENCSR123ABC")
  -> Returns: {"accession": "ENCSR123ABC", "action": "updated"}
  -> Status now shows "released" with updated date_released and assembly

Step 3: Verify publications were updated
  encode_list_tracked()
  -> Publication count increased from 0 to 2 (new publications were added to the portal)

Re-tracking is safe and idempotent. It updates all metadata fields, re-fetches publications (merging without duplicates), and updates the updated_at timestamp. The tracked_at timestamp is preserved.

Integration

Presenting Results

When presenting tracking results to the user:

• Show tracked experiment summary as a table: accession | assay | biosample | publications | notes
• For citations, always offer the format choice: JSON (structured), BibTeX (LaTeX), or RIS (Endnote/Zotero/Mendeley)
• After tracking multiple experiments, show the collection summary using encode_summarize_collection to give a bird's-eye view grouped by assay, organ, and target
• When comparing experiments, present the compatibility verdict clearly:
  • COMPATIBLE: All key fields match. Safe to combine.
  • WARNINGS: Minor differences (e.g., different labs, dates). Proceed with caution.
  • INCOMPATIBLE: Critical mismatches (e.g., different organisms, assemblies). Do not combine without justification.
• For provenance results, show the full chain: derived file -> tool/parameters -> source ENCODE accessions
• When exporting, confirm the format and offer to save to a specific path
• For large collections (10+ experiments), present a summary table first and offer to show details on request
• Always include the database path (~/.encode_connector/tracker.db) when users ask about data persistence

Key Literature

• FAIR Principles: Wilkinson et al. 2016 (Scientific Data, ~5,000 citations) DOI: 10.1038/sdata.2016.18 -- The tracking system supports FAIR data management by maintaining provenance, citations, and cross-references. Findable (accessions), Accessible (local SQLite), Interoperable (CSV/JSON/BibTeX export), Reusable (provenance chains).
• ENCODE Portal: Hitz et al. 2023 (Nucleic Acids Research) DOI: 10.1093/nar/gkac1067 -- Documents the experiment metadata structure that tracking relies on. The 18 fields stored per experiment map directly to the ENCODE Portal API schema.
• ENCODE Phase 3: ENCODE Project Consortium. Nature 583, 699-710 (2020) DOI: 10.1038/s41586-020-0636-z -- The source of experiment metadata structure and quality standards. All tracked experiments derive from this data resource. This paper should be cited in any publication using ENCODE data.
• ENCODE Blacklist: Amemiya et al. 2019 (Scientific Reports) DOI: 10.1038/s41598-019-45839-z (~1,400 citations) -- Critical for peak filtering in provenance workflows. The blacklist regions should be removed from any ChIP-seq or ATAC-seq peak file before downstream analysis.
• STAR Methods: Cell Press STAR Methods guidelines -- The gold standard for structured, transparent, and reproducible methods reporting. The provenance tracking system is designed to capture all information needed for STAR Methods-compliant methods sections: tool versions, parameters, input/output counts, and reference file sources.
• Landt et al. 2012: Genome Research 22, 1813-1831. DOI: 10.1101/gr.136184.111 (~4,000 citations) -- ENCODE ChIP-seq quality standards. Defines the QC metrics (FRiP, NSC, RSC, NRF) that should be checked for any tracked ChIP-seq experiment before inclusion in analysis.

Related Skills

For the request: "$ARGUMENTS"