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A Simplified Workflow for the Prediction of Putative Viral Reads Using NIPT Data
Shabnam Shahidi1, Atousa Dabiri Oskoei2, Akbar Mohammadzadeh1
1Department of Medical Genetics and Molecular Medicine, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.
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.
