Mercury

Duncan Muir Nicholas Freitas Jonathan Zhang

Credits: Daniel Mohktari and Scott Longwell

Overview

This is a WORK IN PROGRESS package for processing and analysing various assays from the Mercury/HTBAM/HTMEK platform.

Installation

Conda Environment Setup

(Recommended) Create a fresh conda environment with python=3.12 using:

conda create -n mercury python=3.12

and activate using:

conda activate mercury

Install Stable Release via Wheel File

Download the latest wheel file from the Release Page

Then, install the package to your conda environment using:

pip install /path/to/downloaded/wheel.file

For latest code (Not recommended) clone the repo and install locally

1. Clone this repo from the pinneylab Github
2. Change directory to unzipped package path
   • $ cd /repo-download-dir
3. pip install the package in place and make editable
   • $ pip install -e .

Processing and Analyzing Data

Our processing and analysis is done in Jupyter notebooks. To get started, download the latest release of our notebooks repo.

Then in your conda environment, start the Jupyter server with the command jupyter notebook.

To-Do:

• Flexible initialization of LocalMercuryDBAPI. One should be able to read in any combination of button quant, standard curve, kinetic, binding, and/or stability data.
  • If it makes sense, could repurpose the add_run() method for loading datasets individually. If not, could make a new load_data() method.
• Implement the process_dataframe_binding() function within csv_processing.py.
• Represent binding data as Data3D (no time dimension). The dependent variable dimension is the fluorescence ratio of post-wash prey to post-wash bait.
• Implement a function in fit.py for fitting binding isotherms to data. Again, keep the API and logic consistent with what is already implemented.
  • There should be an optional argument for specifying a list of identifiers for tight-binders. If this argument is provided, the code will fix the value of rmax, estimated from the rmax fit to the tight-binders, to fit the rest of the data.
  • Similar to fitting standards and initial rates, one should be able to specify custom fit windows on a per-concentration basis.
• Implement a function to visualize binding isotherms in plot.py
• Implement plotting methods analgous to export_MM_sample_data, export_MM_chamber_data, and export_end_to_end_summary_by_sample in MercuryExperiment.
• In MercuryExperiment, add enzyme concentration information to export_binding_chamber_data and export_binding_sample_data methods. Mimic export_MM_chamber_data and export_MM_sample_data methods here.
• Add MercuryExperiment methods to export binding isotherm subplots on a per-chamber and per-sample basis. See export_mm_subplots_by_chamber and export_mm_subplots_by_sample for examples
• Add an optional argument to fit_binding_isotherm() to take in a user-defined fixed r_max value.