STAN -- Standardized proteomic Throughput ANalyzer

Know your instrument.

STAN is an open-source proteomics QC tool for Bruker timsTOF and Thermo Orbitrap mass spectrometers. It watches your raw data directories for new acquisitions, auto-detects DIA or DDA mode, runs standardized search jobs (DIA-NN for DIA, Sage for DDA) directly on your instrument workstation, computes instrument health metrics, gates your sample queue automatically on QC failure, tracks longitudinal performance in a local database, serves a dashboard, and optionally benchmarks your instrument against the global proteomics community through a crowdsourced HeLa digest dataset.

No HPC cluster required. STAN runs DIA-NN and Sage locally on the same machine as your instrument. For labs with SLURM cluster access, remote HPC execution is available as an option.

Built at the UC Davis Proteomics Core by Brett Stanley Phinney.

Key Features

Multi-instrument monitoring -- Bruker timsTOF and Thermo Orbitrap in a single dashboard
DIA and DDA mode intelligence -- auto-detects acquisition mode and routes to the right search engine with the right metrics
Run and Done gating -- automatically pauses your sample queue (HOLD flag) when a QC run fails thresholds
Instrument Performance Score (IPS) -- a single 0-100 composite number for LC health, updated every run
Column health tracking -- longitudinal TIC trend analysis detects column aging before it affects your data
Precursor-first metrics -- benchmarks on precursor count (DIA) and PSM count (DDA), not protein count, because protein count is confounded by FASTA choice and inference settings
Community HeLa benchmark (planned) -- compare your instrument against labs worldwide via an open HuggingFace Dataset (CC BY 4.0)
Instrument health fingerprint -- dual-mode DDA+DIA radar chart for rapid visual diagnosis
Plain-English failure diagnosis -- templated alerts explain what failed and what to check, no guesswork
Privacy by design -- raw files are never uploaded; only aggregate QC metrics leave your lab

Status note: The Python backend (watcher, search dispatch, metric extraction, gating, scoring, DB) is implemented and tested. The React dashboard frontend, community HF Dataset assets, and PyPI packaging are in progress. See Implementation Status below.

Supported Instruments

Vendor	Instruments	Raw Format	Acquisition Modes
Bruker	timsTOF Ultra 2, Ultra, Pro 2, SCP	`.d` directory	diaPASEF, ddaPASEF
Thermo	Astral, Exploris 480, Exploris 240	`.raw` file	DIA, DDA

Quick Start

Prerequisites

STAN runs search engines locally on your instrument workstation. Install these first:

Python 3.10+
DIA-NN -- download from github.com/vdemichev/DiaNN/releases and add to PATH
Sage -- download from github.com/lazear/sage/releases and add to PATH
ThermoRawFileParser (only if running DDA on Thermo instruments) -- github.com/compomics/ThermoRawFileParser

Install STAN

pip install stan-proteomics          # coming soon — not yet on PyPI

Install from source (recommended for now):

git clone https://github.com/bsphinney/stan.git
cd stan
pip install -e ".[dev]"

Initialize

stan init

Creates ~/.stan/ and copies default configuration templates into it.

Run stan setup for an interactive wizard that picks your instrument, directories, LC method, and FASTA -- no YAML editing required. Or create the config files manually:

instruments.yml -- instrument watch directories and settings
thresholds.yml -- QC pass/warn/fail thresholds per instrument model
community.yml -- HuggingFace token and community benchmark preferences

Watch

stan watch

Starts the watcher daemon. It monitors directories configured in instruments.yml, detects new raw files, determines acquisition mode, and runs DIA-NN or Sage locally on your machine. (Requires a working instruments.yml in ~/.stan/ -- see Configuration. DIA-NN and Sage must be installed and on your PATH.)

Dashboard

stan dashboard

Serves the FastAPI backend at http://localhost:8421. The API is fully functional -- browse /docs for Swagger UI. (The React frontend is planned; currently a placeholder page is shown. Use the API endpoints directly or via the Swagger UI.)

Other Commands

stan status           # show configuration and database summary
stan column-health    # assess LC column condition from longitudinal TIC trends
stan version          # print STAN version

Architecture

Raw data directory (watched by watcher daemon)
        |
        |  file stable for stable_secs
        v
detector.py -- reads .d/analysis.tdf or .raw metadata
        |
        +-- DIA --> local DIA-NN --> report.parquet
        +-- DDA --> local Sage   --> results.sage.parquet
                         |
                 extractor.py + chromatography.py
                         |
                 evaluator.py --> PASS / WARN / FAIL
                         |              |
                    SQLite DB     queue.py (HOLD flag)
                         |
                 dashboard (FastAPI, port 8421)
                         |               (React frontend planned)
                 community/submit.py --> HF Dataset (planned)

Data flow: The watcher daemon detects new raw files and checks for file stability (size stops changing). Once stable, the detector reads instrument metadata to determine DIA or DDA mode. STAN runs DIA-NN (for DIA) or Sage (for DDA) locally on your instrument workstation as a subprocess with standardized parameters. After the search completes, STAN extracts QC metrics from the results, evaluates them against per-instrument thresholds, writes a HOLD flag if the run fails, stores everything in SQLite for longitudinal tracking, and optionally submits to the community benchmark.

Execution modes:

Local (default) -- DIA-NN and Sage run as subprocesses on the same machine. Install them once, add to PATH, and STAN handles the rest. A typical QC HeLa run searches in 5-15 minutes on a modern workstation.
SLURM (optional) -- For labs with HPC cluster access, set execution_mode: "slurm" in instruments.yml. STAN submits batch jobs via SSH/paramiko and polls for completion.

QC Metric Hierarchy

STAN uses a deliberate metric hierarchy. This is a core design decision that differentiates STAN from other QC tools:

Fragment XICs / precursor    <-- purest instrument signal
Precursor count @ 1% FDR    <-- PRIMARY metric for DIA (Track B)
PSM count @ 1% FDR          <-- PRIMARY metric for DDA (Track A)
Peptide count                <-- secondary for both modes
Protein count                <-- contextual only, never used for ranking

Protein count is intentionally excluded from primary benchmarking. It is heavily confounded by FASTA database choice, protein inference algorithm, and FDR propagation settings. Precursor and PSM counts with a standardized search provide a much cleaner signal of instrument performance.

Community Benchmark

STAN powers an open, crowdsourced HeLa digest benchmark hosted on HuggingFace. Labs worldwide submit aggregate QC metrics (never raw files) from their HeLa standard runs, enabling cross-lab instrument performance comparisons.

Community submission is entirely opt-in. By default, community_submit is false and nothing leaves your machine. STAN works fully standalone for local QC monitoring, gating, and longitudinal tracking without ever contacting an external service. Set community_submit: true per instrument only if you want to participate in the benchmark.

Browse the community dashboard (planned): huggingface.co/spaces/brettsp/stan

How It Works

All community benchmark submissions use a frozen, standardized search with pinned FASTA, spectral libraries, and search parameters hosted in the HF Dataset repository. This is what makes cross-lab comparisons valid -- every lab searches the same library with the same settings, so differences in precursor counts reflect actual instrument performance, not search configuration.

Benchmark Tracks

Track	Mode	Search Engine	Primary Metric	Secondary Metrics
Track A	DDA	Sage	PSM count @ 1% FDR	Peptide count, mass accuracy, MS2 scan rate
Track B	DIA	DIA-NN	Precursor count @ 1% FDR	Peptide count, median CV, IPS
Track C	Both	Both	Instrument fingerprint	Radar chart (6 axes), peptide recovery ratio

Track C unlocks when a lab submits both a DDA and a DIA run from the same instrument within 24 hours. The resulting six-axis radar chart provides a comprehensive instrument health fingerprint covering mass accuracy, duty cycle, spectral quality, precursor depth, quantitative reproducibility, and fragment sensitivity.

Cohort Bucketing

Submissions are compared only within their cohort, defined by three dimensions: instrument family, throughput (SPD), and injection amount. This ensures a 50 ng run on a timsTOF Ultra at 60 SPD is compared against other 50 ng timsTOF Ultra 60 SPD runs, not against a 500 ng Astral at 200 SPD.

Throughput buckets (SPD -- samples per day):

SPD is the primary throughput unit. Labs set their Evosep, Vanquish Neo, or equivalent method by SPD in instruments.yml. Gradient length in minutes is accepted as a fallback for custom LC methods.

Bucket	SPD Range	Evosep Method	Traditional Equivalent
`200+spd`	200 or more	500/300/200 SPD	~2-5 min gradient
`100spd`	80-199	100 SPD	~11 min gradient
`60spd`	40-79	60 SPD (most popular), Whisper 40	~21-31 min gradient
`30spd`	25-39	30 SPD	~44 min gradient
`15spd`	10-24	Extended	~60-88 min gradient
`deep`	under 10	--	>2h gradient

Amount buckets (injection amount in ng):

Bucket	Range	Typical Use
ultra-low	25 ng or less	Single-cell, very low input
low	26-75 ng	Standard QC (50 ng default)
mid	76-150 ng	Moderate load
standard	151-300 ng	Traditional 200-250 ng QC
high	301-600 ng	High-load methods
very-high	over 600 ng	Specialized applications

The default injection amount is 50 ng and is configurable per instrument in instruments.yml via the hela_amount_ng field.

A minimum of 5 submissions per cohort is required before the leaderboard activates.

Community Composite Scores

DIA Score (Track B):

DIA_Score = 40 x percentile_rank(n_precursors)
          + 25 x percentile_rank(n_peptides)
          + 20 x (100 - percentile_rank(median_cv_precursor))
          + 15 x percentile_rank(ips_score)

DDA Score (Track A):

DDA_Score = 35 x percentile_rank(n_psms)
          + 25 x percentile_rank(n_peptides_dda)
          + 20 x percentile_rank(pct_delta_mass_lt5ppm)
          + 20 x percentile_rank(ms2_scan_rate)

Scores are computed nightly within each cohort by a GitHub Actions workflow. A score of 75 means your instrument outperformed 75% of comparable submissions. (Nightly consolidation is implemented but will not run until the HF Dataset has live submissions.)

Annual Community Awards (planned)

To encourage participation and make QC a point of pride (or healthy shame), STAN will recognize top and bottom performers each year:

Award	Criteria	Prize
Golden Spray Tip	Highest median community score across all cohorts, minimum 50 submissions	Trophy + bragging rights
Most Consistent	Lowest CV of community scores over the year (the lab that never has a bad day)	Trophy
Most Improved	Largest year-over-year score increase	Trophy
The Clogged Emitter	Lowest median community score, minimum 50 submissions	Trophy of Shame (opt-in -- you have to claim it)

Awards are computed from the community benchmark dataset and announced annually. Labs must have community_submit: true and at least 50 submissions in the calendar year to qualify. The Clogged Emitter is opt-in -- your lab is never publicly shamed without consent. All awards are meant in good fun and to motivate better instrument maintenance across the field.

Privacy

Raw files are never uploaded -- only aggregate QC metrics
Patient or sample metadata is never collected
Serial numbers are stored server-side but never exposed in API responses or downloads
Anonymous submissions are supported (display_name can be left blank)
Submissions can be deleted by filing a GitHub issue with the submission_id
Community dataset licensed under CC BY 4.0

Configuration

All configuration files live in ~/.stan/. They are YAML files that can be edited with any text editor. The watcher daemon hot-reloads instruments.yml every 30 seconds without requiring a restart. (Dashboard UI editing is planned; for now edit the YAML files directly.)

Until the default config templates are shipped, create these files manually in ~/.stan/ using the examples below.

instruments.yml

Defines which instruments to monitor, where their raw files land, and instrument-specific settings.

# STAN instrument watcher configuration
# Hot-reloaded every 30 seconds -- no restart needed after edits

instruments:

  - name: "timsTOF Ultra"
    vendor: "bruker"
    model: "timsTOF Ultra"
    watch_dir: "D:/Data/raw"           # where .d directories appear
    output_dir: "D:/Data/stan_out"     # STAN writes results + HOLD flags here
    extensions: [".d"]
    stable_secs: 60              # seconds of no size change before processing
    enabled: true
    hela_amount_ng: 50           # injection amount in ng (default: 50)
    spd: 30                      # samples per day (Evosep 30 SPD)
    community_submit: false      # set true to share QC metrics with community benchmark

  - name: "Astral"
    vendor: "thermo"
    model: "Astral"
    watch_dir: "D:/Data/raw"
    output_dir: "D:/Data/stan_out"
    extensions: [".raw"]
    stable_secs: 30
    enabled: true
    hela_amount_ng: 50
    spd: 60                      # Evosep 60 SPD
    community_submit: false      # set true to opt in

# ── Optional: SLURM HPC execution ──────────────────────────────────
# Uncomment to run searches on a remote cluster instead of locally.
# Most labs do NOT need this — local execution is the default.
#
# hive:
#   host: "hive.ucdavis.edu"
#   user: "your_username"
#
# Then add to each instrument:
#   execution_mode: "slurm"      # default is "local"
#   hive_partition: "high"
#   hive_account: "your-account-grp"

Vendor-specific file stability detection:

Bruker .d: The .d directory size is checked every 10 seconds. The run is considered complete after stable_secs consecutive seconds with no size change (default: 60 seconds).
Thermo .raw: A single binary file. Checked via mtime and size. Stable after stable_secs with no change (default: 30 seconds).

thresholds.yml

Defines QC pass/warn/fail thresholds per instrument model. A default entry applies when no model-specific entry exists.

thresholds:

  default:
    dia:
      n_precursors_min: 5000
      median_cv_precursor_max: 20.0
      missed_cleavage_rate_max: 0.20
      ips_score_min: 50
    dda:
      n_psms_min: 10000
      pct_delta_mass_lt5ppm_min: 0.70
      ms2_scan_rate_min: 10.0

  "timsTOF Ultra":
    dia:
      n_precursors_min: 10000
      median_cv_precursor_max: 15.0
      ips_score_min: 65
    dda:
      n_psms_min: 30000
      pct_delta_mass_lt5ppm_min: 0.90

community.yml

Controls community benchmark participation. No HuggingFace account or token is needed -- STAN submits through a relay API automatically.

display_name: "Your Lab Name"              # shown on leaderboard; blank = anonymous
hela_source: "Pierce HeLa Protein Digest Standard"
institution_type: "core_facility"          # core_facility | academic_lab | industry

Instrument Performance Score (IPS)

IPS is a 0-100 composite computed entirely from search output. No reference run, no blank runs, no historical data needed — works from the very first QC injection.

DIA:

IPS = 30 x precursor_depth + 25 x spectral_quality (frags/precursor)
    + 20 x sampling_quality (pts/peak) + 15 x quant_coverage + 10 x digestion

DDA:

IPS = 30 x identification_depth + 25 x mass_accuracy
    + 20 x sampling_quality (pts/peak) + 15 x scoring_quality + 10 x digestion

Score Range	Interpretation
90-100	Excellent -- instrument performing optimally
80-89	Good -- normal operating range
60-79	Marginal -- investigate soon
Below 60	Investigate -- likely instrument or LC issue

IPS is stored for every run in the local SQLite database and included in community benchmark submissions.

Search Engines

STAN depends on two external search engines that you install separately. Both run locally on your instrument workstation by default.

License note: STAN does not bundle, redistribute, or include any part of DIA-NN, Sage, or ThermoRawFileParser. It calls them as external subprocesses, the same way a Makefile calls gcc. Users must install each tool separately under its own license. This is required for DIA-NN in particular because commercial use requires a paid license from Aptila Biotech or Thermo Fisher Scientific.

DIA: DIA-NN

DIA-NN handles all DIA searches. Both Bruker .d and Thermo .raw files are passed directly to DIA-NN without conversion (DIA-NN 2.1+ has native support for both formats on Linux and Windows).

Install: Download from https://github.com/vdemichev/DiaNN/releases and add to PATH, or place the executable and set diann_path in instruments.yml.

License: DIA-NN is free for academic research use. Since STAN is designed for academic core facilities and research labs, this is the intended use case. Commercial users need to obtain a paid license separately from Aptila Biotech or Thermo Fisher Scientific — STAN does not modify the licensing terms.

Historical note: DIA-NN versions up to 1.9.1 were free for all users (academic and commercial). Starting with 1.9.2, commercial use requires a paid license while academic use remains free. DIA-NN 2.x follows the same model. STAN recommends the latest academic release.

Citation required: If STAN is useful for your work, please cite the DIA-NN paper: Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. Nature Methods (2020).

Community benchmark submissions use a frozen HeLa-specific predicted spectral library and a pinned FASTA, both hosted in the HF Dataset repository. (Library generation is in progress -- the HF Dataset assets are not yet uploaded.)

DDA: Sage

Sage handles all DDA searches. Bruker .d files are read natively by Sage (confirmed working for ddaPASEF). Thermo .raw files require conversion to mzML via ThermoRawFileParser before Sage can process them -- this is the only conversion step in the entire STAN pipeline.

Install: Download from https://github.com/lazear/sage/releases and add to PATH, or place the executable and set sage_path in instruments.yml.

License: Sage is open source under the MIT license.

Sage includes built-in LDA rescoring that is sufficient for QC-level FDR estimation.

ThermoRawFileParser (Thermo DDA only)

If you run DDA on a Thermo instrument, STAN needs ThermoRawFileParser to convert .raw to mzML before Sage can search it. This is only needed for Thermo DDA -- not for Thermo DIA (DIA-NN reads .raw natively) and not for any Bruker workflows.

License: ThermoRawFileParser is open source under the Apache 2.0 license.

Running on an HPC Cluster (optional)

For labs with SLURM cluster access, STAN can submit search jobs via SSH instead of running locally. See the HPC Guide for setup, container paths, bind mount patterns, and common errors. This includes critical gotchas about DIA-NN containers, symlinks, and invalid flags that will save you hours of debugging.

Repository Layout

stan/
+-- pyproject.toml
+-- README.md
+-- STAN_MASTER_SPEC.md            # authoritative design document
+-- CLAUDE.md                      # development context for Claude Code
+-- stan/
|   +-- cli.py                     # CLI entry point (typer)
|   +-- config.py                  # config loader with hot-reload
|   +-- db.py                      # SQLite operations
|   +-- watcher/                   # watchdog daemon, stability, mode detection
|   +-- search/                    # DIA-NN + Sage SLURM job builders
|   |   +-- community_params.py    # frozen community search parameters
|   +-- metrics/                   # metric extraction, IPS, iRT, scoring
|   +-- gating/                    # threshold evaluation, HOLD flag, queue control
|   +-- community/                 # HF Dataset submit/fetch/validate
|   |   +-- scripts/consolidate.py # nightly GitHub Actions consolidation
|   +-- dashboard/                 # FastAPI backend + React frontend
+-- tests/
+-- docs/
+-- .github/workflows/
    +-- ci.yml                     # lint + test on push/PR
    +-- consolidate_benchmark.yml  # nightly benchmark consolidation

Development

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Run a single test file
pytest tests/test_metrics.py -v

# Skip integration tests (require Hive/SLURM)
pytest tests/ -k "not integration"

# Lint
ruff check stan/

# Lint with auto-fix
ruff check stan/ --fix

Tests marked @pytest.mark.integration require Hive SLURM access and real instrument files. They are skipped in CI and can be run manually on the HPC cluster.

Implementation Status

Component	Status	Notes
CLI (`stan init/watch/dashboard/status/column-health/version`)	Done	All commands wired up and working
Watcher daemon (file stability, hot-reload config)	Done	Bruker `.d` and Thermo `.raw` stability detection
Acquisition mode detection (Bruker `.d`)	Done	Reads `MsmsType` from `analysis.tdf`
Acquisition mode detection (Thermo `.raw`)	Done	Via ThermoRawFileParser metadata
Local DIA-NN execution (default)	Done	Subprocess-based, community-standardized params
Local Sage execution (default)	Done	JSON config, Thermo mzML conversion via TRFP
SLURM HPC execution (optional)	Done	SSH/paramiko job submission for labs with clusters
Metric extraction (DIA + DDA)	Done	Polars-based, from `report.parquet` and `results.sage.parquet`
IPS scoring	Done	4-component composite, 0-100 scale
QC gating + HOLD flag	Done	Hard gates, plain-English diagnosis
Column health assessment	Done	Longitudinal TIC trend analysis
SQLite database + migrations	Done	Stores all metrics, gate results, amount_ng, spd
Community validation + submission	Done	Hard gates, soft flags, asset hash verification
Community scoring (DIA + DDA)	Done	Percentile-based within SPD/amount cohorts
Instrument fingerprint (Track C)	Done	6-axis radar, failure pattern matching
Nightly consolidation script	Done	GitHub Actions, recomputes cohort percentiles
FastAPI dashboard backend	Done	API routes for runs, trends, instruments, thresholds, submission
SPD-first cohort bucketing	Done	Evosep 500-30 SPD, Vanquish Neo, traditional LC
Default config files (`config/`)	Done	instruments.yml, thresholds.yml, community.yml templates
Test fixtures (real DIA-NN/Sage output)	Planned	`tests/fixtures/` is empty — need small real output files
React dashboard frontend	Planned	Only a placeholder HTML page exists
PyPI publishing (`pip install stan-proteomics`)	Planned	`pyproject.toml` is ready, not yet published
HF Dataset assets (FASTA + speclibs)	Planned	Library generation in progress, MD5 hashes TODO
HF Space public dashboard	Planned	Space repo exists but not deployed
Community benchmark live data	Planned	Requires HF Dataset assets + first submissions
Setup wizard (`stan setup`)	Done	Interactive instrument picker, LC method, FASTA path, writes YAML
Outlier detection (amount mismatch)	Planned	Flag submissions where metrics don't match declared amount/SPD
Failed run rejection	Planned	Block near-zero results from entering benchmark (failed injection, empty spray)

TODO

Links

Resource	URL
STAN GitHub	github.com/bsphinney/stan
STAN Community Dashboard	huggingface.co/spaces/brettsp/stan
STAN Community Dataset	huggingface.co/datasets/brettsp/stan-benchmark
DE-LIMP (sibling project)	github.com/bsphinney/DE-LIMP

STAN handles QC and instrument health monitoring. For differential expression analysis and full quantitative proteomics workflows, see DE-LIMP.

Contributing

Contributions are welcome. Please:

Fork the repository and create a feature branch
Run ruff check stan/ and pytest tests/ -v before submitting
Include tests for new functionality (use fixtures in tests/fixtures/, prefer real output snippets over synthetic data)
Open a pull request with a clear description of the change

For questions about the spec or design decisions, open a discussion on GitHub before implementing.

License

Code: MIT License

Community benchmark dataset: CC BY 4.0

Citations

If STAN is useful for your work, please cite STAN and the search engines it depends on:

STAN:

Phinney BS. STAN: Standardized proteomic Throughput ANalyzer. UC Davis Proteomics Core (2026). https://github.com/bsphinney/stan

DIA-NN (DIA search engine):

Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nature Methods. 2020;17:41-44. https://doi.org/10.1038/s41592-019-0638-x

Sage (DDA search engine):

Lazear MR. Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale. Journal of Proteome Research. 2023;22(11):3652-3659. https://doi.org/10.1021/acs.jproteome.3c00486

Points-across-peak quantitation quality metric:

Matthews DE, Hayes JM. Systematic Errors in Gas Chromatography-Mass Spectrometry Isotope Ratio Measurements. Analytical Chemistry. 1976;48(9):1375-1382.

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config		config
docs		docs
stan		stan
tests		tests
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
README.md		README.md
STAN_MASTER_SPEC.md		STAN_MASTER_SPEC.md
install_stan.bat		install_stan.bat
install_stan.ps1		install_stan.ps1
pyproject.toml		pyproject.toml

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STAN -- Standardized proteomic Throughput ANalyzer

Key Features

Supported Instruments

Quick Start

Prerequisites

Install STAN

Initialize

Watch

Dashboard

Other Commands

Architecture

QC Metric Hierarchy

Community Benchmark

How It Works

Benchmark Tracks

Cohort Bucketing

Community Composite Scores

Annual Community Awards (planned)

Privacy

Configuration

instruments.yml

thresholds.yml

community.yml

Instrument Performance Score (IPS)

Search Engines

DIA: DIA-NN

DDA: Sage

ThermoRawFileParser (Thermo DDA only)

Running on an HPC Cluster (optional)

Repository Layout

Development

Implementation Status

TODO

Links

Contributing

License

Citations

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