Back to Search Start Over

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

Authors :
Ssekagiri A
Jjingo D
Lujumba I
Bbosa N
Bugembe DL
Kateete DP
Jordan IK
Kaleebu P
Ssemwanga D
Source :
Bioinformatics advances [Bioinform Adv] 2022 Nov 28; Vol. 2 (1), pp. vbac089. Date of Electronic Publication: 2022 Nov 28 (Print Publication: 2022).
Publication Year :
2022

Abstract

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%.<br />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.<br />Supplementary Information: Supplementary data are available at Bioinformatics Advances online.<br /> (© The Author(s) 2022. Published by Oxford University Press.)

Details

Language :
English
ISSN :
2635-0041
Volume :
2
Issue :
1
Database :
MEDLINE
Journal :
Bioinformatics advances
Publication Type :
Academic Journal
Accession number :
36699347
Full Text :
https://doi.org/10.1093/bioadv/vbac089