Optimizing ancestral trait reconstruction of large HIV Subtype C datasets through multiple-trait subsampling.

ABSTRACT: Large datasets along with sampling bias represent a challenge for phylodynamic reconstructions, particularly when the study data are obtained from various heterogeneous sources and/or through convenience sampling. In this study, we evaluate the presence of unbalanced sampled distribution by collection date, location, and risk group of human immunodeficiency virus Type 1 Subtype C using a comprehensive subsampling strategy and assess their impact on the reconstruction of the viral spatial and risk group dynamics using phylogenetic comparative methods. Our study shows that a most suitable dataset for ancestral trait reconstruction can be obtained through subsampling by all available traits, particularly using multigene datasets. We also demonstrate that sampling bias is inflated when conside

SUBMITTER: Li X 

PROVIDER: S-EPMC10691791 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature