ArticleBioinformatics advances2025
DRAGoN: a robust pipeline for analyzing DRUG-seq datasets.
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Existing bioinformatics pipelines to process DRUG-seq datasets have limited flexibility and can have difficulty analyzing current datasets without requiring excessive computational time or memory. Results: Here, we describe an alternative, DRAGoN, which is fast, robust, and performs as well as or better than competing pipelines on key benchmarks without sacrificing accuracy. This is accomplished primarily via a preliminary demultiplexing step that facilitates the parallelization of the pipeline as well as the collection of per-well statistics that assist with quality control. DRAGoN provides the user maximum flexibility with respect to filtering, alignment, counting, and downsampling, and it efficiently collapses UMIs. Availability and implementation: DRAGoN is a Nextflow pipeline that utilizes open-source software alongside custom C++ programs and Python scripts. It is freely available at https://github.com/MSDLLCPapers/DRAGoN.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.