An automated bioinformatics pipeline to monitor HIV data in real-time Bioinformatics HIV phylogenetics epidemiology HIVtransmission contacttracing VirusesMDPI BrownUniversity Yale
By Neha MathurMar 15 2023Reviewed by Benedette Cuffari, M.Sc. In a recent study published in Viruses, researchers discuss an open-source and automated bioinformatics pipeline to prospectively and routinely analyze and integrate heterogeneous human immunodeficiency virus -1 sequence data. This approach was applied on 18 monthly datasets generated between January 2020 and June 2022 in Rhode Island in the United States.
Background Challenges associated with real-time data integration, analysis, and interpretation delay public health responses, particularly when HIV is considered. Thus, analyzing genomic HIV data or HIV-1 sequences could inform public health responses and ultimately overcome data management, computational, and analytical challenges.
About the study In the present study, researchers source and integrate statewide molecular HIV data from clinical, sequence, and public health databases. The pipeline implemented five phylogenetic methods and cluster-defining parameters that favored false positive clusters and maximized available information. Likewise, the novel approach used HIV-TRACE v. 0.4.4 to perform distance-only sequence clustering.
Results The pipeline developed in the current study incorporated four new features unavailable in prior HIV cluster analysis automated approaches. First, it had a flagging step that explored sequence quality. Second, it implemented several phylogenetic and distance-only clustering methods. As compared to distance-only methods, the proposed pipeline detected 76% more clustered HIV cases. More specifically, it identified 37 new HIV cases for case management discussions.
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