{"title":"DRAGoN: a robust pipeline for analyzing DRUG-seq datasets.","authors":"Scott Norton, John M Gaspar","doi":"10.1093/bioadv/vbaf214","DOIUrl":null,"url":null,"abstract":"<p><strong>Motivation: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Availability and implementation: </strong>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.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf214"},"PeriodicalIF":2.8000,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12457737/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioadv/vbaf214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
