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Updated: Nov 7, 2025

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples
Published on: July 2, 2016
Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood
Marko Melnick1, Patrick Gonzales1, Thomas J LaRocca2
1Department of Integrative Physiology, University of Colorado, Boulder, CO 80303, USA.
Abstract:
Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated.
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