4.3 Article

Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood

Journal

G3-GENES GENOMES GENETICS
Volume 11, Issue 9, Pages -

Publisher

OXFORD UNIV PRESS INC
DOI: 10.1093/g3journal/jkab141

Keywords

ALS; transcriptomics; RNA-seq; microbiome; virome

Funding

  1. NIH [NS063964]
  2. ALS Association (ALSA) [WA1096, 16-IIP-253]
  3. NINDS [R35NS097273, P01NS084974]
  4. Mayo Clinic Foundation
  5. Neuroscience Focused Research Team Mayo Clinic grant
  6. Association of Frontotemporal Dementia (AFTD)
  7. Alzheimer's Association-AD Strategic Fund
  8. Muscular Dystrophy Association [172123]
  9. ALS Recovery Fund
  10. Kimmelman Estate
  11. Department of Defense (Chem-Bio Diagnostics program) [HDTRA-1-18-10032]

Ask authors/readers for more resources

This study developed a bioinformatic pipeline to identify potential etiological agents in RNA-seq data, successfully confirming the presence of known and novel viral sequences through testing and application in practical cases.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.3
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available