Authors
Shabnam Shahidi, Atousa Dabiri Oskoei, Akbar Mohammadzadeh, Hessam Mirshahabi, Kamyar Mansori, Hossein Dinmohammadi, Hassan Rokni-Zadeh
Published in
Prenatal diagnosis. Aug 05, 2026. Epub Aug 05, 2026.
Abstract
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.
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.
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.
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.
PMID:
42555977
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.
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