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opentrons-integration

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用于Opentrons OT-2/Flex液路处理机器人的Python协议开发。支持移液、稀释、PCR及模块控制,提供本地模拟功能。多厂商自动化建议使用pylabrobot。

legacy/opentrons-integration/SKILL.md jaechang-hits/SciAgent-Skills

触发场景

编写Opentrons机器人控制脚本 进行液体处理或PCR实验自动化 控制温度或磁力模块 在本地模拟Opentrons协议

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SKILL.md

Frontmatter
{
    "name": "opentrons-integration",
    "license": "Apache-2.0",
    "description": "Opentrons Protocol API v2 for OT-2\/Flex: Python protocols for pipetting, serial dilutions, PCR, plate replication; control thermocycler, heater-shaker, magnetic, temperature modules. Use pylabrobot for multi-vendor."
}

Opentrons Integration — Lab Automation

Overview

Opentrons provides a Python-based Protocol API (v2) for programming OT-2 and Flex liquid handling robots. Protocols are structured Python files with metadata and a run() function that controls pipettes, labware, and hardware modules. All protocols can be simulated locally before running on physical hardware.

When to Use

  • Automating liquid handling workflows (pipetting, mixing, distributing)
  • Writing PCR setup protocols with thermocycler control
  • Performing serial dilutions across plates
  • Replicating plates or reformatting between plate types
  • Controlling hardware modules (temperature, magnetic, heater-shaker, thermocycler)
  • Setting up multi-channel pipetting for 96-well plate operations
  • Simulating protocols before running on the robot
  • For multi-vendor automation (Hamilton, Beckman, etc.), use pylabrobot instead
  • For flow cytometry analysis of automated experiment results, use flowio/flowkit

Prerequisites

pip install opentrons
# Simulate protocols locally (no robot needed)
opentrons_simulate my_protocol.py

Protocol API Version: Always use the latest stable API level (currently 2.19). Set apiLevel in protocol metadata. Protocols are forward-compatible within major versions.

Robot Types: Flex (newer, larger deck, 96-channel pipette) vs OT-2 (smaller, 8-channel max). Key differences: deck slot naming (Flex: A1-D3, OT-2: 1-11), available pipettes, and module support.

Quick Start

from opentrons import protocol_api

metadata = {"protocolName": "Quick Transfer", "apiLevel": "2.19"}

def run(protocol: protocol_api.ProtocolContext):
    tips = protocol.load_labware("opentrons_96_tiprack_300ul", "1")
    source = protocol.load_labware("nest_12_reservoir_15ml", "2")
    plate = protocol.load_labware("corning_96_wellplate_360ul_flat", "3")
    pipette = protocol.load_instrument("p300_single_gen2", "left", tip_racks=[tips])

    pipette.distribute(50, source["A1"], plate.wells()[:12], new_tip="once")

Core API

1. Protocol Structure

Every Opentrons protocol follows a required structure: metadata dict + run() function.

from opentrons import protocol_api

metadata = {
    "protocolName": "My Protocol",
    "author": "Name <email>",
    "description": "Protocol description",
    "apiLevel": "2.19",
}

# Optional: specify robot type
requirements = {"robotType": "Flex", "apiLevel": "2.19"}

def run(protocol: protocol_api.ProtocolContext):
    # All protocol logic goes here
    protocol.comment("Protocol started")

2. Labware and Deck Layout

Load labware (plates, reservoirs, tip racks) onto deck slots and optionally onto adapters.

def run(protocol: protocol_api.ProtocolContext):
    # Tip racks
    tips_300 = protocol.load_labware("opentrons_96_tiprack_300ul", "1")
    tips_20 = protocol.load_labware("opentrons_96_tiprack_20ul", "4")

    # Plates and reservoirs
    plate = protocol.load_labware("corning_96_wellplate_360ul_flat", "2", label="Sample Plate")
    reservoir = protocol.load_labware("nest_12_reservoir_15ml", "3")

    # Labware on adapter (Flex)
    adapter = protocol.load_adapter("opentrons_flex_96_tiprack_adapter", "B1")
    tips_on_adapter = adapter.load_labware("opentrons_flex_96_tiprack_200ul")

    # Pipettes
    p300 = protocol.load_instrument("p300_single_gen2", "left", tip_racks=[tips_300])
    p20 = protocol.load_instrument("p20_single_gen2", "right", tip_racks=[tips_20])

Common pipette names:

  • OT-2: p20_single_gen2, p300_single_gen2, p1000_single_gen2, p20_multi_gen2, p300_multi_gen2
  • Flex: p50_single_flex, p1000_single_flex, p50_multi_flex, p1000_multi_flex

3. Pipette Operations

Basic, compound, and advanced liquid handling operations.

def run(protocol: protocol_api.ProtocolContext):
    # ... (labware loaded above)

    # === Basic operations ===
    p300.pick_up_tip()
    p300.aspirate(100, source["A1"])        # Draw 100 µL
    p300.dispense(100, dest["B1"])          # Expel 100 µL
    p300.drop_tip()

    # === Compound operations (auto tip management) ===
    # Transfer: single source → single dest
    p300.transfer(100, source["A1"], dest["B1"], new_tip="always")

    # Distribute: one source → many dests
    p300.distribute(50, reservoir["A1"],
                    [plate["A1"], plate["A2"], plate["A3"]], new_tip="once")

    # Consolidate: many sources → one dest
    p300.consolidate(50, [plate["A1"], plate["A2"]], reservoir["A1"])

    # === Advanced techniques ===
    p300.pick_up_tip()
    p300.mix(repetitions=3, volume=50, location=plate["A1"])  # Mix in place
    p300.aspirate(100, source["A1"])
    p300.air_gap(20)                        # Prevent dripping
    p300.dispense(120, dest["A1"])
    p300.blow_out(dest["A1"].top())          # Expel residual
    p300.touch_tip(plate["A1"])              # Remove exterior drops
    p300.drop_tip()

4. Well Access and Locations

Navigate wells by name, index, row, or column. Control vertical position within wells.

def run(protocol: protocol_api.ProtocolContext):
    plate = protocol.load_labware("corning_96_wellplate_360ul_flat", "1")

    # Access by name or index
    well = plate["A1"]
    first = plate.wells()[0]           # Same as plate["A1"]

    # Iterate rows/columns
    row_a = plate.rows()[0]            # [A1, A2, ..., A12]
    col_1 = plate.columns()[0]         # [A1, B1, ..., H1]

    # Vertical positions
    pipette.aspirate(100, well.top())          # 1mm below top
    pipette.aspirate(100, well.bottom(z=2))    # 2mm above bottom
    pipette.aspirate(100, well.center())       # Center of well
    pipette.dispense(100, well.top(z=5))       # 5mm above top

5. Hardware Modules

Control temperature, magnetic, heater-shaker, and thermocycler modules.

def run(protocol: protocol_api.ProtocolContext):
    # Temperature module
    temp_mod = protocol.load_module("temperature module gen2", "3")
    temp_plate = temp_mod.load_labware("corning_96_wellplate_360ul_flat")
    temp_mod.set_temperature(celsius=4)
    # temp_mod.temperature → current temp; temp_mod.deactivate()

    # Magnetic module
    mag_mod = protocol.load_module("magnetic module gen2", "6")
    mag_plate = mag_mod.load_labware("nest_96_wellplate_100ul_pcr_full_skirt")
    mag_mod.engage(height_from_base=10)    # Raise magnets (mm)
    mag_mod.disengage()

    # Heater-Shaker module
    hs_mod = protocol.load_module("heaterShakerModuleV1", "1")
    hs_plate = hs_mod.load_labware("corning_96_wellplate_360ul_flat")
    hs_mod.close_labware_latch()
    hs_mod.set_target_temperature(celsius=37)
    hs_mod.wait_for_temperature()
    hs_mod.set_and_wait_for_shake_speed(rpm=500)
    hs_mod.deactivate_shaker()
    hs_mod.deactivate_heater()
    hs_mod.open_labware_latch()

    # Thermocycler (auto-assigned to slots)
    tc_mod = protocol.load_module("thermocyclerModuleV2")
    tc_plate = tc_mod.load_labware("nest_96_wellplate_100ul_pcr_full_skirt")
    tc_mod.open_lid()
    tc_mod.close_lid()
    tc_mod.set_lid_temperature(celsius=105)
    tc_mod.set_block_temperature(95, hold_time_seconds=180)

    profile = [
        {"temperature": 95, "hold_time_seconds": 15},
        {"temperature": 60, "hold_time_seconds": 30},
        {"temperature": 72, "hold_time_seconds": 60},
    ]
    tc_mod.execute_profile(steps=profile, repetitions=30, block_max_volume=50)
    tc_mod.deactivate_lid()
    tc_mod.deactivate_block()

6. Protocol Control and Utilities

Pause, delay, comment, liquid tracking, and simulation detection.

def run(protocol: protocol_api.ProtocolContext):
    # Execution control
    protocol.pause(msg="Replace tip box and resume")
    protocol.delay(seconds=60)
    protocol.delay(minutes=5)
    protocol.comment("Starting serial dilution")
    protocol.home()

    # Liquid tracking (visual in Opentrons App)
    water = protocol.define_liquid(name="Water", description="Ultrapure water",
                                    display_color="#0000FF")
    reservoir["A1"].load_liquid(liquid=water, volume=50000)
    plate["B1"].load_empty()

    # Check simulation vs real run
    if protocol.is_simulating():
        protocol.comment("Simulation mode")

    # Flow rate control (µL/s)
    pipette.flow_rate.aspirate = 150
    pipette.flow_rate.dispense = 300
    pipette.flow_rate.blow_out = 400

Key Concepts

Protocol File Structure

All Opentrons protocols are Python files with this required structure:

┌─ metadata dict ──────────────── protocolName, apiLevel, author
├─ requirements dict (optional) ── robotType
└─ def run(protocol): ─────────── All robot commands

The run() function receives a ProtocolContext object — all labware loading, pipette operations, and module control happen through this single entry point. Protocols cannot import arbitrary packages for execution on the robot.

OT-2 vs Flex Differences

Feature OT-2 Flex
Deck slots 1-11 (numeric) A1-D3 (grid)
Pipettes Gen2 (p20, p300, p1000) Flex (p50, p1000, 96-channel)
Max channels 8-channel multi 96-channel
Modules Gen1/Gen2 V2 modules
Adapters Not supported Supported (tiprack, flat)

Multi-Channel Pipette Behavior

When using multi-channel pipettes, referencing a single well accesses the entire column:

multi = protocol.load_instrument("p300_multi_gen2", "left", tip_racks=[tips])
# This transfers from ALL wells in column 1 of source to column 1 of dest
multi.transfer(100, source["A1"], dest["A1"])

Common Workflows

Workflow: Serial Dilution

from opentrons import protocol_api

metadata = {"protocolName": "Serial Dilution", "apiLevel": "2.19"}

def run(protocol: protocol_api.ProtocolContext):
    tips = protocol.load_labware("opentrons_96_tiprack_300ul", "1")
    reservoir = protocol.load_labware("nest_12_reservoir_15ml", "2")
    plate = protocol.load_labware("corning_96_wellplate_360ul_flat", "3")
    p300 = protocol.load_instrument("p300_single_gen2", "left", tip_racks=[tips])

    # Add diluent to columns 2-12
    p300.transfer(100, reservoir["A1"], plate.rows()[0][1:])

    # Serial dilution across row A
    p300.transfer(
        100,
        plate.rows()[0][:11],
        plate.rows()[0][1:],
        mix_after=(3, 50),
        new_tip="always",
    )

Workflow: PCR Setup with Thermocycler

from opentrons import protocol_api

metadata = {"protocolName": "PCR Setup", "apiLevel": "2.19"}

def run(protocol: protocol_api.ProtocolContext):
    tc_mod = protocol.load_module("thermocyclerModuleV2")
    tc_plate = tc_mod.load_labware("nest_96_wellplate_100ul_pcr_full_skirt")
    tips = protocol.load_labware("opentrons_96_tiprack_300ul", "1")
    reagents = protocol.load_labware("opentrons_24_tuberack_nest_1.5ml_snapcap", "2")
    p300 = protocol.load_instrument("p300_single_gen2", "left", tip_racks=[tips])

    tc_mod.open_lid()
    # Distribute master mix
    p300.distribute(20, reagents["A1"], tc_plate.wells()[:8], new_tip="once")
    # Add samples
    for i in range(8):
        p300.transfer(5, reagents.wells()[i + 1], tc_plate.wells()[i], new_tip="always")

    # Run PCR
    tc_mod.close_lid()
    tc_mod.set_lid_temperature(105)
    tc_mod.set_block_temperature(95, hold_time_seconds=180)  # Initial denaturation
    profile = [
        {"temperature": 95, "hold_time_seconds": 15},
        {"temperature": 60, "hold_time_seconds": 30},
        {"temperature": 72, "hold_time_seconds": 30},
    ]
    tc_mod.execute_profile(steps=profile, repetitions=35, block_max_volume=25)
    tc_mod.set_block_temperature(72, hold_time_minutes=5)  # Final extension
    tc_mod.set_block_temperature(4)  # Hold
    tc_mod.deactivate_lid()
    tc_mod.open_lid()

Workflow: Magnetic Bead Cleanup

  1. Load magnetic module with deep-well plate, reservoir with wash buffers
  2. Engage magnets → aspirate supernatant → dispense to waste
  3. Disengage magnets → add wash buffer → mix → engage → remove wash (repeat 2x)
  4. Disengage → add elution buffer → mix → engage → transfer eluate to clean plate

Key Parameters

Parameter Function Default Range Effect
volume aspirate, dispense, transfer 1–1000 µL Liquid volume
new_tip transfer, distribute, consolidate "always" "always", "once", "never" Tip change strategy
mix_after transfer None (reps, vol) tuple Post-dispense mixing
mix_before transfer None (reps, vol) tuple Pre-aspirate mixing
blow_out transfer False True/False Blow out after dispense
touch_tip transfer False True/False Touch tip after dispense
air_gap transfer 0 0–pipette max µL Air gap volume
flow_rate.aspirate pipette property varies 1–1000 µL/s Aspirate speed
flow_rate.dispense pipette property varies 1–1000 µL/s Dispense speed
height_from_base mag_module.engage 0–20 mm Magnet engagement height

Best Practices

  1. Always simulate first: Run opentrons_simulate my_protocol.py before uploading to the robot. Catches labware conflicts, volume errors, and tip shortages without wasting consumables.

  2. Use compound operations over basic: Prefer transfer(), distribute(), consolidate() over manual pick_up_tip/aspirate/dispense/drop_tip sequences — they handle tip management automatically.

  3. Anti-pattern — ignoring tip count: A protocol that runs out of tips will error mid-run. Count total tip uses vs rack capacity before running.

  4. Track liquids for setup validation: Use define_liquid() and load_liquid() to enable volume tracking in the Opentrons App.

  5. Anti-pattern — hardcoding deck slots across robot types: Flex uses grid coordinates (A1-D3), OT-2 uses numbers (1-11). Write separate metadata or use requirements["robotType"] to ensure compatibility.

  6. Control flow rates for difficult liquids: Reduce aspirate speed for viscous solutions (glycerol, PEG), increase for water-like liquids.

  7. Use pauses for manual intervention: protocol.pause(msg=...) is safer than protocol.delay() when you need user action (e.g., adding reagent, sealing plate).

Common Recipes

Recipe: Plate Replication

from opentrons import protocol_api

metadata = {"protocolName": "Plate Replication", "apiLevel": "2.19"}

def run(protocol: protocol_api.ProtocolContext):
    tips = protocol.load_labware("opentrons_96_tiprack_300ul", "1")
    source = protocol.load_labware("corning_96_wellplate_360ul_flat", "2")
    dest = protocol.load_labware("corning_96_wellplate_360ul_flat", "3")
    p300 = protocol.load_instrument("p300_single_gen2", "left", tip_racks=[tips])
    p300.transfer(100, source.wells(), dest.wells(), new_tip="always")

Recipe: Reagent Distribution with Multi-Channel

from opentrons import protocol_api

metadata = {"protocolName": "Multi-Channel Distribution", "apiLevel": "2.19"}

def run(protocol: protocol_api.ProtocolContext):
    tips = protocol.load_labware("opentrons_96_tiprack_300ul", "1")
    reservoir = protocol.load_labware("nest_12_reservoir_15ml", "2")
    plate = protocol.load_labware("corning_96_wellplate_360ul_flat", "3")
    multi = protocol.load_instrument("p300_multi_gen2", "left", tip_racks=[tips])

    # Fill all 96 wells: 12 columns × 8 rows via multi-channel
    multi.transfer(100, reservoir["A1"], plate.rows()[0], new_tip="once")

Troubleshooting

Problem Cause Solution
OutOfTipsError Protocol needs more tips than available Add multiple tip racks to tip_racks= list, or reload tips with pipette.reset_tipracks()
Labware collision on deck Two items assigned to overlapping slots Check deck map — thermocycler auto-occupies multiple slots; use protocol.deck to inspect
Volume exceeds pipette capacity Attempting to aspirate/dispense > max volume Use distribute() which auto-splits volumes, or switch to a larger pipette
LabwareNotFoundError Wrong labware API name Check names at labware.opentrons.com; use exact API name strings
Protocol works in simulation but fails on robot Hardware-specific timing issue Add protocol.delay() between temperature changes; increase magnet engage time
Inaccurate volumes Pipette calibration or air bubbles Recalibrate pipette; pre-wet tips with mix(); adjust flow rates for viscous liquids
ModuleNotAttachedError Module not connected or wrong model string Verify module serial connection; use exact model strings ("temperature module gen2")

Related Skills

  • pylabrobot — multi-vendor lab automation (Hamilton, Beckman, Tecan) for cross-platform protocols
  • biopython-molecular-biology — sequence design and primer tools for PCR protocol inputs

References

版本历史

  • 02745ef 当前 2026-07-19 09:16

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skills/proteomics-protein-engineering/uniprot-protein-database/SKILL.md
skills/scientific-computing/aeon/SKILL.md
skills/scientific-computing/astropy-astronomy/SKILL.md
skills/scientific-computing/dask-parallel-computing/SKILL.md
skills/scientific-computing/degenerate-input-filtering/SKILL.md
skills/scientific-computing/exploratory-data-analysis/SKILL.md
skills/scientific-computing/geopandas-geospatial/SKILL.md
skills/scientific-computing/hypogenic-hypothesis-generation/SKILL.md
skills/scientific-computing/matlab-scientific-computing/SKILL.md
skills/scientific-computing/nan-safe-correlation/SKILL.md
skills/scientific-computing/networkx-graph-analysis/SKILL.md
skills/scientific-computing/neurokit2/SKILL.md
skills/scientific-computing/neuropixels-analysis/SKILL.md
skills/scientific-computing/nextflow-workflow-engine/SKILL.md
skills/scientific-computing/polars-dataframes/SKILL.md
skills/scientific-computing/pyhealth/SKILL.md
skills/scientific-computing/pymoo/SKILL.md
skills/scientific-computing/scikit-learn-machine-learning/SKILL.md
skills/scientific-computing/shap-model-explainability/SKILL.md
skills/scientific-computing/simpy-discrete-event-simulation/SKILL.md
skills/scientific-computing/snakemake-workflow-engine/SKILL.md
skills/scientific-computing/spikeinterface-electrophysiology/SKILL.md
skills/scientific-computing/sympy-symbolic-math/SKILL.md
skills/scientific-computing/torch-geometric-graph-neural-networks/SKILL.md
skills/scientific-computing/transformers-bio-nlp/SKILL.md
skills/scientific-computing/umap-learn/SKILL.md
skills/scientific-computing/uspto-database/SKILL.md
skills/scientific-computing/vaex-dataframes/SKILL.md
skills/scientific-computing/zarr-python/SKILL.md
skills/scientific-writing/biorxiv-database/SKILL.md
skills/scientific-writing/cancer-research-figure-guide/SKILL.md
skills/scientific-writing/cell-figure-guide/SKILL.md
skills/scientific-writing/citation-management/SKILL.md
skills/scientific-writing/clinical-decision-support-documents/SKILL.md
skills/scientific-writing/elife-figure-guide/SKILL.md
skills/scientific-writing/general-figure-guide/SKILL.md
skills/scientific-writing/hypothesis-generation/SKILL.md
skills/scientific-writing/lancet-figure-guide/SKILL.md
skills/scientific-writing/latex-research-posters/SKILL.md
skills/scientific-writing/literature-review/SKILL.md
skills/scientific-writing/nature-figure-guide/SKILL.md
skills/scientific-writing/nejm-figure-guide/SKILL.md
skills/scientific-writing/openalex-database/SKILL.md
skills/scientific-writing/peer-review-methodology/SKILL.md
skills/scientific-writing/pnas-figure-guide/SKILL.md
skills/scientific-writing/pubmed-database/SKILL.md
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skills/scientific-writing/scientific-brainstorming/SKILL.md
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skills/scientific-writing/scientific-schematics/SKILL.md
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skills/structural-biology-drug-discovery/alphafold-database-access/SKILL.md
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skills/structural-biology-drug-discovery/chembl-database-bioactivity/SKILL.md
skills/structural-biology-drug-discovery/clinicaltrials-database-search/SKILL.md
skills/structural-biology-drug-discovery/dailymed-database/SKILL.md
skills/structural-biology-drug-discovery/datamol-cheminformatics/SKILL.md
skills/structural-biology-drug-discovery/ddinter-database/SKILL.md
skills/structural-biology-drug-discovery/deepchem/SKILL.md
skills/structural-biology-drug-discovery/diffdock/SKILL.md
skills/structural-biology-drug-discovery/drugbank-database-access/SKILL.md
skills/structural-biology-drug-discovery/emdb-database/SKILL.md
skills/structural-biology-drug-discovery/fda-database/SKILL.md
skills/structural-biology-drug-discovery/gtopdb-database/SKILL.md
skills/structural-biology-drug-discovery/mdanalysis-trajectory/SKILL.md
skills/structural-biology-drug-discovery/medchem/SKILL.md
skills/structural-biology-drug-discovery/molfeat-molecular-featurization/SKILL.md
skills/structural-biology-drug-discovery/opentargets-database/SKILL.md
skills/structural-biology-drug-discovery/pdb-database/SKILL.md
skills/structural-biology-drug-discovery/pubchem-compound-search/SKILL.md
skills/structural-biology-drug-discovery/pytdc-therapeutics-data-commons/SKILL.md
skills/structural-biology-drug-discovery/rdkit-cheminformatics/SKILL.md
skills/structural-biology-drug-discovery/sar-analysis/SKILL.md
skills/structural-biology-drug-discovery/torchdrug/SKILL.md
skills/structural-biology-drug-discovery/unichem-database/SKILL.md
skills/structural-biology-drug-discovery/zinc-database/SKILL.md
skills/systems-biology-multiomics/brenda-database/SKILL.md
skills/systems-biology-multiomics/cellchat-cell-communication/SKILL.md
skills/systems-biology-multiomics/cobrapy-metabolic-modeling/SKILL.md
skills/systems-biology-multiomics/kegg-pathway-analysis/SKILL.md
skills/systems-biology-multiomics/lamindb-data-management/SKILL.md
skills/systems-biology-multiomics/libsbml-network-modeling/SKILL.md
skills/systems-biology-multiomics/mofaplus-multi-omics/SKILL.md
skills/systems-biology-multiomics/muon-multiomics-singlecell/SKILL.md
skills/systems-biology-multiomics/omics-analysis-guide/SKILL.md
skills/systems-biology-multiomics/reactome-database/SKILL.md
skills/systems-biology-multiomics/string-database-ppi/SKILL.md
.claude/skills/sciagent-skill-creator/SKILL.md
skills/genomics-bioinformatics/databases/clinpgx-database/SKILL.md
skills/genomics-bioinformatics/databases/mouse-phenome-database/SKILL.md
skills/medical-imaging/imaging-data-commons/SKILL.md
skills/proteomics-protein-engineering/pride-database/SKILL.md
skills/structural-biology-drug-discovery/mdtraj-trajectory-analysis/SKILL.md
skills/structural-biology-drug-discovery/smina-molecular-docking/SKILL.md

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收录时间
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