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Documentation and scripts for implementation of station A, B and C

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Introduction

This is a collaboration repository originated with the work of covidWarriors in the implementation of Opentrons robots for the automatization of RT-PCR diagnostics of covid19.

BU-ISCIII is in charge of the setup, calibration, configuration, maintenance and protocol development for OT-2 robots in Instituto de Salud Carlos III. These robots have been deployed to automate COVID-19 diagnostics, and once the pandemic is over they will be repurposed to suit new challenges.

This repo is a copy of opentrons repo for covid19.

In this repo you will find:

How to prepare the robots

Reagent preparation and handling

Instructions for the manual part of this protocol (how to prepare reagents, how to set up labware, and so on) are hosted on protocols.io according to Opentrons docs.

Protocols

We're automating a RT-qPCR based diagnostic protocol. We split it into 3 parts, intended to be run on 3 separate robots:

  • Station A: Sample preparation.
    • Protocol 1: dispense buffer to tubes.
    • Protocol 2: dispense beads to deep well plate.
    • Protocol 3: dispense lysates to deep well plate with beads.
  • Station B: RNA extraction.
  • Station C: real-time PCR setup.

See the Python files for more details.

Directory structure

  • /experiments contains the exact Python scripts that were used for certain validation experiments, so we don't get confused about which experimental results correspond to which versions of our Python code.
  • /labware contains the custom labware definitions necessary to run these protocols. After cloning this repository, you should configure the Opentrons App to look in this directory. (Go to More > Custom Labware > Labware Management > Custom Labware Definitions Folder.)
  • /notebooks is for stashing random Jupyter Notebooks that we're using for developing and debugging.
  • /protocols is for the uploadable protocols themselves.

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Documentation and scripts for implementation of station A, B and C

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  • Python 79.2%
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