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DRAGoN: a robust pipeline for analyzing DRUG-seq datasets.

Scott Norton¹, John M Gaspar¹

¹Department of Data Science & Scientific Informatics, Research and Development Sciences-Information Technology, Merck & Co., Inc., Cambridge, MA, 02141, United States.

Bioinformatics Advances

|September 26, 2025

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DRAGoN is a new bioinformatics pipeline for processing DRUG-seq data. It offers improved speed, flexibility, and accuracy compared to existing methods, efficiently handling complex datasets.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Existing bioinformatics pipelines for DRUG-seq analysis present challenges with flexibility, computational time, and memory usage.
Current methods struggle to efficiently analyze large and complex DRUG-seq datasets.

Purpose of the Study:

To introduce DRAGoN, a novel bioinformatics pipeline designed for DRUG-seq data processing.
To provide a faster, more robust, and flexible alternative to existing DRUG-seq analysis tools.

Main Methods:

DRAGoN is implemented as a Nextflow pipeline using open-source software, custom C++ programs, and Python scripts.
Key features include a preliminary demultiplexing step for parallelization and per-well statistics for quality control.
The pipeline offers flexible options for filtering, alignment, counting, downsampling, and efficient UMI collapsing.

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Main Results:

DRAGoN demonstrates comparable or superior performance to existing pipelines on key benchmarks without compromising accuracy.
The pipeline effectively handles large datasets with reduced computational time and memory requirements.
DRAGoN provides enhanced flexibility in data processing parameters and efficient UMI collapsing.

Conclusions:

DRAGoN offers a significant advancement in DRUG-seq data analysis, addressing limitations of current bioinformatics pipelines.
Its speed, robustness, accuracy, and flexibility make it a valuable tool for researchers.
The pipeline is freely available, promoting wider adoption and reproducibility in DRUG-seq studies.