A Simplified Workflow for the Prediction of Putative Viral Reads Using NIPT Data

The authors aim to develop a cost-effective and efficient workflow for predicting viral sequences from non-invasive prenatal testing (NIPT) data, which traditionally focuses on identifying fetal chromosomal abnormalities. By comparing two bioinformatic approaches, they demonstrate that their proposed method can effectively identify viral DNA in 24.2% of samples while reducing computational complexity. The study highlights the potential diversity of the maternal virome and calls for further experimental validation of the identified viral sequences.