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A deep learning approach reveals unexplored landscape of viral expression in cancer

Medicine and Health

A deep learning approach reveals unexplored landscape of viral expression in cancer

A. Elbasir, Y. Ye, et al.

Discover viRNAtap, a cutting-edge deep learning pipeline that uncovers viral contigs from RNA sequencing data. This innovative tool has unveiled unexpected viruses linked to cancer and highlighted human endogenous viruses associated with poor survival rates. Conducted by Abdurrahman Elbasir, Ying Ye, and team, this research opens new avenues in understanding viral infections in clinical settings.

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Playback language: English
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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