Utilizing the VirIdAl Pipeline to Search for Viruses in the Metagenomic Data of Bat Samples

Viruses

Kamil Khafizov¹  Anna Y. Budkina¹  Vasily G. Akimkin¹  Ivan A. Kotov¹  Anna S. Speranskaya¹  Elena V. Korneenko¹  Daniil A. Kiselev²  Ilya V. Artyushin³ 

[1] FSBI Central Research Institute for Epidemiology of the Federal Service for Surveillance of Consumer Rights Protection and Human Wellbeing, 111123 Moscow, Russia;I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia;Lomonosov Moscow State University, 119991 Moscow, Russia;

关键词: viruses;    NGS;    bioinformatics;    coronavirus;    bats;   

DOI  :  10.3390/v13102006

来源: DOAJ

【 摘 要 】

According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This problem particularly impedes viral screening, due to vast heterogeneity in viral genomes. In this paper, we present a new bioinformatic pipeline, VirIdAl, for detecting and identifying viral pathogens in sequencing data. We also demonstrate the utility of the new software by applying it to viral screening of the feces of bats collected in the Moscow region, which revealed a significant variety of viruses associated with bats, insects, plants, and protozoa. The presence of alpha and beta coronavirus reads, including the MERS-like bat virus, deserves a special mention, as it once again indicates that bats are indeed reservoirs for many viral pathogens. In addition, it was shown that alignment-based methods were unable to identify the taxon for a large proportion of reads, and we additionally applied other approaches, showing that they can further reveal the presence of viral agents in sequencing data. However, the incompleteness of viral databases remains a significant problem in the studies of viral diversity, and therefore necessitates the use of combined approaches, including those based on machine learning methods.

【 授权许可】

Unknown