Identification of viruses and further assembly of viral genomes from the next-generation-sequencing (NGS) data are essential steps in virome studies. This study presented an one-stop tool named VIGA (available at https://github.com/viralInformatics/VIGA) for eukaryotic virus identification and genome assembly from NGS data. It was composed of four modules including identification, taxonomic annotation, assembly and novel virus discovery which integrated the homology-based method for virus identification and both the reference-based and de novo assemblers for accurate and effective assembly of virus genomes. Evaluation on multiple simulated and real virome datasets showed that VIGA assembled more complete virus genomes than its competitors on both the metatranscriptomic and metagenomic data, and also performed well in assembling virus genomes at the strain level. Finally, VIGA was used to investigate the virome in metatranscriptomic data from the Human Microbiome Project and revealed different composition and positive rate of viromes in diseases of Prediabetes, Crohn's disease and Ulcerative colitis. Overall, VIGA would help much in identification and characterization of viromes in future studies.
Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is publicly available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely used methods namely miRDeep2 and ShortStack in identifying viral miRNAs with a significantly improved sensitivity. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. vsRNAfinder would greatly facilitate effective identification of vsRNAs from sRNA-Seq data.
Virus-encoded small RNAs (vsRNAs) have been reported to play an important role in viral infections. Unfortunately, there is still a lack of a systematic characterization and resource of vsRNAs. Herein, we identified a total of 19 734 high-confidence vsRNAs including 2746 microRNAs (miRNAs) in 64 viral species from more than 800 samples of public small RNA-Seq data. The number of vsRNAs identified in viruses varied from 1 to 2489 with a median of 170. The length distribution of vsRNAs peaked at 21 and 22 nt. Plant viruses were found to express larger number and higher levels of vsRNAs than those of animal viruses. Besides, the number of vsRNAs identified increased as the viral infection persisted. Interestingly, the vsRNA showed strong expression specificity as little overlap was observed among vsRNAs identified in different strains of a virus, or in different hosts, cells, or tissues infected by the same virus. Little conservation was observed among vsRNAs of different viruses.The viral miRNAs were found to interact with host genes involved in multiple biological processes related to organization, development, action potential, polarity establishment, methylation, immune response, gene regulation, localization, and so on. To facilitate the usage of vsRNAs, a database named vsRNAdb was built for organizing and storing vsRNAs which is available at http://www. computationalbiology.cn/vsRNAdb/#/vsRNAdb/#/. Overall, the study deepens our understanding about the diversity and complexity of vsRNAs and provides a rich resource for further studies of vsRNAs.
Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Besides, a systematic characterization of vsRNAs is also needed. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is public available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely-used methods namely miRDeep2 and ShortStack in identifying viral miRNA, siRNA and piRNA. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. Based on vsRNAfinder, a total of 19,734 high-confidence vsRNAs including 2,746 miRNAs were identified in 64 viral species from public sRNA-Seq data. The number of vsRNAs identified in viruses varied from 1 to 2,489 with a median of 170. The length distribution of vsRNAs peaked at 21 and 22 nt. Plant viruses were found to express larger number and higher levels of sRNAs than those of animal viruses. Besides, the number of vsRNAs identified increased as the viral infection persisted. Interestingly, the vsRNA showed strong expression specificity as little overlap was observed among vsRNAs identified in different strains of a virus, or in different hosts, cells or tissues infected by the same virus. Little conservation was observed among vsRNAs of different viruses. Overall, the study not only presents an effective method for vsRNA identification, but also provides a rich resource for further studies of vsRNAs.
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