Related Experiment Video
Updated: May 8, 2026
06:02
A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells
Published on: October 28, 2025
CountESS: a flexible, graphical pipeline tool for deep mutational scanning analysis
Nick Moore1, Callum J Sargeant1, Matthew J Wakefield1,2
1The University of Melbourne, Melbourne, Victoria, Australia.
Biorxiv : the Preprint Server for Biology
|May 7, 2026
Summary
CountESS is a new open-source tool that simplifies complex Deep Mutational Scanning (DMS) data analysis. It offers a flexible, graphical interface to process diverse experimental designs and generate variant scores efficiently.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Deep Mutational Scanning (DMS) experiments generate extensive sequencing data requiring multi-step computational analysis.
- Existing DMS analysis tools are fragmented, lacking flexibility for diverse experimental designs and scoring strategies.
Purpose of the Study:
- To introduce CountESS, an open-source pipeline tool for flexible and efficient Deep Mutational Scanning (DMS) data analysis.
- To provide a modular, graphical interface that accommodates a wide range of DMS experimental workflows.
Main Methods:
- Developed CountESS, an open-source pipeline tool using Python and DuckDB.
- Implemented a modular graphical interface supporting various input formats, barcode translation, HGVS variant calling, and user-defined scoring functions.
- Ensured high-performance, memory-efficient processing for large datasets.
Main Results:
- CountESS accommodates diverse DMS experimental designs, including selection assays, time-series, and bin-based assays like VAMP-seq.
- The tool supports user-defined scoring functions, enhancing analytical flexibility.
- Demonstrated high-performance and memory efficiency for processing large sequencing datasets.
Conclusions:
- CountESS provides a unified, flexible, and efficient solution for Deep Mutational Scanning (DMS) data analysis.
- The open-source nature and modular design of CountESS promote broader adoption and customization in biological research.
