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Abstract
This paper introduces viRNAtap, an alignment-free pipeline based on deep learning, designed to identify and assemble viral contigs from RNA sequencing data. Applied to 14 cancer types from TCGA, viRNAtap revealed the expression of unexpected and divergent viruses not previously linked to cancer, as well as human endogenous viruses (HERVs) whose expression correlated with poor overall survival. The pipeline offers a novel approach to studying viral infections in various clinical conditions.
Publisher
Nature Communications
Published On
Feb 11, 2023
Authors
Abdurrahman Elbasir, Ying Ye, Daniel E Schaffer, Xue Hao, Jayamanna Wickramasinghe, Konstantinos Tsilas, Paul M Lieberman, Qi Long, Quaid Morris, Rugang Zhang, Alejandro A Schaffer, Noam Auslander
Tags
viRNAtap
RNA sequencing
viral contigs
cancer
HERVs
deep learning
survival

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