HIV-DRIVES: HIV drug resistance identification, variant evaluation, and surveillance pipeline.
Stephen KanyereziIvan SserwaddaAloysious SsemagandaJulius SeruyangeAlisen AyitewalaHellen Rosette OundoWilson TenywaBrian A KagurusiGodwin TusabeStacy WereIsaac SsewanyanaSusan NabaddaMaria Magdalene NamagandaGerald MboowaPublished in: Access microbiology (2024)
The global prevalence of resistance to antiviral drugs combined with antiretroviral therapy (cART) emphasizes the need for continuous monitoring to better understand the dynamics of drug-resistant mutations to guide treatment optimization and patient management as well as check the spread of resistant viral strains. We have recently integrated next-generation sequencing (NGS) into routine HIV drug resistance (HIVDR) monitoring, with key challenges in the bioinformatic analysis and interpretation of the complex data generated, while ensuring data security and privacy for patient information. To address these challenges, here we present HIV-DRIVES (HIV Drug Resistance Identification, Variant Evaluation, and Surveillance), an NGS-HIVDR bioinformatics pipeline that has been developed and validated using Illumina short reads, FASTA, and Sanger ab1 .seq files.
Keyphrases
- antiretroviral therapy
- hiv infected
- hiv positive
- human immunodeficiency virus
- hiv aids
- hiv testing
- drug resistant
- hiv infected patients
- hepatitis c virus
- men who have sex with men
- public health
- south africa
- case report
- multidrug resistant
- big data
- escherichia coli
- electronic health record
- machine learning
- artificial intelligence
- gene expression
- acinetobacter baumannii
- cystic fibrosis
- health information
- risk factors
- copy number
- drug induced
- circulating tumor cells
- bioinformatics analysis
- combination therapy
- deep learning