Ssekagiri, AlfredJjingo, DaudiLujumba, IbraBbosa, NicholasBugembe, Daniel L.Kateete, David P.Kaleebu, PontianoSsemwanga, Deogratius2023-07-032023-07-032022Ssekagiri, A., Jjingo, D., Lujumba, I., Bbosa, N., Bugembe, D. L., Kateete, D. P., ... & Ssemwanga, D. (2022). QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data. Bioinformatics advances, 2(1), vbac089.https://doi.org/10.1093/bioadv/vbac089https://nru.uncst.go.ug/handle/123456789/9032Next-generation sequencing (NGS) enables reliable detection of resistance mutations in minority variants of human immunodeficiency virus type 1 (HIV-1). There is paucity of evidence for the association of minority resistance to treatment failure, and this requires evaluation. However, the tools for analyzing HIV-1 drug resistance (HIVDR) testing data are mostly web-based which requires uploading data to webservers. This is a challenge for laboratories with internet connectivity issues and instances with restricted data transfer across networks. We present QuasiFlow, a pipeline for reproducible analysis of NGS-based HIVDR testing data across different computing environments. Since QuasiFlow entirely depends on command-line tools and a local copy of the reference database, it eliminates challenges associated with uploading HIV-1 NGS data onto webservers. The pipeline takes raw sequence reads in FASTQ format as input and generates a user-friendly report in PDF/HTML format. The drug resistance scores obtained using QuasiFlow were 100% and 99.12% identical to those obtained using web-based HIVdb program and HyDRA web respectively at a mutation detection threshold of 20%.enMutationsHuman immunodeficiency virus type 1DataArticle