{"doi":"10.1101/2021.05.24.445423","title":"Improved HIV-1 drug resistance mutation prediction using quasispecies reconstruction supported analysis","abstract":"Abstract Accurate and sensitive approaches to detect HIV-1 drug resistance mutations (DRMs) are indispensable for the paradigm of ‘treatment as prevention’. While HIV-1 proviral DNA allows sensitive high throughput sequencing (HTS)-based DRM detection, its applicability is limited by presence of defective genomes. This study demonstrates application of quasispecies reconstruction algorithms (QRAs) to improve DRM detection sensitivity from proviral DNA. A robust benchmarking of 5 QRAs was performed with 2 distinct experimental control-datasets including a stringent, novel control: DCPM, simulating in-vivo variant distribution (0.08%-86.5%). Selected QRA was further evaluated for its ability to differentiate DRMs from hypermutated sequences using an in-silico control. PredictHaplo outperformed all others in terms of precision and was selected for further analysis. Near full-genome HTS was performed on proviral DNA from 20 HIV-1C infected individuals, at different stages of ART, from Mumbai, India. DRM detection was performed through residue-wise variation analysis and implementation of QRAs. Both analyses were highly concordant for DRM frequencies >10% (spearman r=0.91, p<0.0001). Phylogenetic association in HTS datasets with shared transmission history could also be demonstrated by PredictHaplo. This study highlights utility of QRAs as an adjunct to traditional residue-wise variation-based DRM detection leading to optimal personalized ART as well as better disease management.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":220866,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9527,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":820574,"name":"Shilpa Bhowmick","orcid":"0000-0002-9586-3989","position":1,"is_corresponding":false},{"id":820995,"name":"Varsha Padwal","orcid":null,"position":2,"is_corresponding":false},{"id":820996,"name":"Vidya Nagar","orcid":null,"position":3,"is_corresponding":false},{"id":820997,"name":"Priya Patil","orcid":null,"position":4,"is_corresponding":false},{"id":820575,"name":"Vainav Patel","orcid":"0000-0001-6661-3238","position":5,"is_corresponding":false},{"id":820998,"name":"A. H. Bandivdekar","orcid":null,"position":6,"is_corresponding":false},{"id":820573,"name":"Jyoti Sutar","orcid":"0000-0002-5869-2156","position":0,"is_corresponding":true}],"reference_count":69,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:53:50.838581Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}