QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data.

Alfred Ssekagiri ORCID logo; Daudi Jjingo; Ibra Lujumba; Nicholas Bbosa ORCID logo; Daniel L Bugembe; David P Kateete; I King Jordan ORCID logo; Pontiano Kaleebu ORCID logo; Deogratius Ssemwanga ORCID logo; (2022) QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data. Bioinformatics advances, 2 (1). vbac089. ISSN 2635-0041 DOI: 10.1093/bioadv/vbac089
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SUMMARY: Next-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%. AVAILABILITY AND IMPLEMENTATION: QuasiFlow and corresponding documentation are publicly available at https://github.com/AlfredUg/QuasiFlow. The pipeline is implemented in Nextflow and requires regular updating of the Stanford HIV drug resistance interpretation algorithm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.


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