A Simplified Workflow for the Prediction of Putative Viral Reads Using NIPT Data.
OBJECTIVE: Non-invasive prenatal testing (NIPT) identifies fetal chromosomal abnormalities by sequencing cell-free fetal DNA (cffDNA). Recent studies suggest the prediction of viral sequences from NIPT data, but current methods lack cost-effectiveness for routine use. This study develops a straightforward workflow to investigate potential viral signatures in pregnant women using NIPT data from 888 Iranian participants. METHOD: Two bioinformatic workflows were compared for predicting viral reads: the traditional method involved mapping reads to the human genome, followed by mapping unmapped reads to viral references, and a direct mapping approach to viral genomes, as proposed in this research. RESULTS: While maintaining reproducibility comparable to the conventional method, the proposed workflow minimizes computational complexity and time usage for data processing. Ultimately, this analysis suggested viral DNA in 24.2% of samples, encompassing 29 distinct species, implying the diversity of the maternal virome. CONCLUSION: This study presents a computationally efficient workflow for the in silico prediction of viral-like sequences from routine NIPT data. Further experimental validation is essential to verify the presence, viability, or clinical relevance of these sequences.