What question did this study set out to answer?

This research aims to quantify the interactions that drive condensate formation in intrinsically disordered proteins using advanced data analysis techniques.

March 29, 2026Open Access

Proteome-scale quantification of the interactions driving condensate formation of intrinsically disordered proteins

Key Points

This research aims to quantify the interactions that drive condensate formation in intrinsically disordered proteins using advanced data analysis techniques.
Utilized Illumina DNA sequencing for data collection, focusing on sequencing counts of merged reads.
Conducted fluorescence recordings using a Fida 1 instrument in microfluidic channels.
Applied Python 3 for analysis, employing libraries like Numpy, Pandas, and Scikit-learn for statistical evaluations.
Generated and included raw microscopy images and various data file types for further analysis.
Data files include significant insights regarding protein interactions from DNA sequencing experiments.
Fluorescence recording data reveals dynamics of condensate formation over time.
Custom Python packages developed for streamlined data analysis improve reproducibility of results.

Abstract

This archive contains data files the Python 3 code needed to reproduce the analysis done for the preprint "Proteome-scale quantification of the interactions driving condensate formation of intrinsically disordered proteins". Large amounts of the data are Illumina DNA sequencing experiments, where the fastq-file are currently not included due to their large file size. Instead, these files are included as sequencing counts of identical merged reads (CSV-files). Additionally, data recorded on the Fida 1 instrument (Fidabio, Denmark) is included and consists of fluorescence recordings over time at the end of a 1 or 0.5 m long microfluidic channel (Ø 75 µm) presented in CSV-files. Additional data file types include raw microscopy images (Leica SP8 confocal microscope, Germany). The scripts are made using Python 3.11.7 with Jupyter notebooks and uses packages: Numpy (v2.3.5), Pandas (v2.3.3), Matplotlib (v3.10.8), Seaborn (v0.13.2), Scipy (v1.16.3), Scikit-learn (v1.8.0), Biopython (v1.86), Uncertainties (v3.2.3), Emcee (v3.1.6), Metapredict (v3.0.1), and localCIDER (v0.1.21). Two-costum Python packages have been created, CPmD and fidapy, and are included in the uploaded files. CPmD is used to run most of the analysis code and fidapy is used to analyse Fida 1 data. Both packages have README files describing installation and basic usage. All figures are included as Scalable Vector Graphics (SVG) files and can be opened using Inkscape. © Technical University of Denmark

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Cite This Study

Norrild et al. (Thu,) studied this question.

synapsesocial.com/papers/69c8c28cde0f0f753b39cdc2 https://doi.org/https://doi.org/10.5281/zenodo.19262929

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