AI Article Synopsis
- Developed a computational tool called STAR to assign HIV-1 subtypes based on protease and reverse transcriptase amino acid sequences for clinical drug resistance assessments.
- Created subtype-specific profiles using scoring matrices from various HIV-1 sequences, enabling alignment and scoring of unknown sequences to determine their subtype.
- STAR demonstrated 99% accuracy in subtype assignation and analyzed 843 clinical isolates, identifying that 26.9% were non-B subtypes.
Article Abstract
We have developed a high throughput computational tool for assigning subtype to HIV-1, based solely on protease and reverse transcriptase (PR-RT) amino acid sequence, generated routinely for clinical assessment of genotypic drug resistance. Subtype-specific profiles were created by generation of position-specific scoring matrices (PSSMs) from multiple amino acids alignments of HIV-1 sequence data from GenBank, phylogenetically divided into subtypes A, AG, B, C, D, F/K, G, H, and J and the separate groups N and O. Query sequences of unknown subtype are aligned with these profiles and a score is derived by comparing each amino acid position in the unknown sequence to the normalized frequency distribution of amino acids at the corresponding positions in the subtype alignments. The highest score is used to assign subtype to the query sequence. Leave one out cross-validation analysis showed the Subtype Analyzer (STAR) was 99% accurate in subtype assignation. STAR can be updated with additional subtype-specific sequence data from sequence databases. STAR was used to classify HIV-1 PR-RT sequences from 843 HIV-1 clinical isolates submitted for drug resistance profiling in London. Within this dataset 26.9% of sequences were classified by STAR as non-B subtypes.
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| http://dx.doi.org/10.1089/088922204323087697 | DOI Listing |
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