Table 1.
Selected bioinformatic tools and pipeline for virome analysis.
| Tools | Description | Strength | Year | References |
|---|---|---|---|---|
| Viruses | ||||
| METAVIRALSPADES | Identification of viral genomes by using a set of virus-specific hidden Markov models for metagenomic assembly analyzing with variations and coverage depth | • Neural network analysis • Novel virus discovery in diverse metagenomic datasets |
2020 | Antipov et al. 58 |
| virMine | Identification of viral genomes from raw reads representative of viral or mixed (viral and bacterial) communities using an iterative approach for read quality control, assembly, and annotation | • Alternative mode between specific study system and/or feature(s) of interest • Novel species detection |
2019 | Garretto et al. 59 |
| Kraken 2 | Classification and assigning taxonomy of metagenomic sequences with BLAST program in the fastest mode | • Low memory usage • High speed • High sensitivity |
2019 | Wood et al. 60 |
| FastViromeExplorer | Detection and abundance quantification of viruses and phages in large datasets by performing rapid searches with pseudo-alignment tool for RNA-seq data | • RNA-seq data analysis • Rapid mapping of short metagenome reads • Suitable for limited computing power research |
2018 | Tithi et al. 61 |
| VirMAP | Combination of nucleotide and protein metagenomic datasets for taxonomic classification of viral genome reconstructions | • Combinatorial analysis of nucleotide and protein sequences • Virus surveillance capabilities |
2018 | Ajami et al. 62 |
| EZ-Map | Metagenomic analysis of human virome with python-based tools for filtering, alignment, and analysis from cell-free DNA data sets | • Fully automated computational pipeline for both workstations and computing clusters • Suitable for cell-free DNA datasets |
2017 | Czeczko et al. 63 |
| VirusDetect | Using small RNA sequences strategy with homology of reference-alignment and de novo assembly | • Small RNA sequence analysis • Potential novel virus identification • Highly sensitive and efficient identification |
2017 | Zheng et al. 64 |
| VirFinder | Identification of viruses by using machine learning with k-mer based approach for mixed metagenomes containing both viral and host sequences | • A web-based tool • An alignment-free tool using machine learning • High potential to detect novel virus |
2017 | Ren et al. 65 |
| VirusSeeker | BLAST-based NGS data analysis pipeline for both novel virus discovery and virome composition analyses | • False-positive removal • Detection of both RNA and DNA viruses in different families. |
2017 | Zhao et al. 66 |
| Bacteriophage | ||||
| VIBRANT | The hybrid tool using machine-learning and protein-similarity approach for recovery and annotation of viruses and microbes with the curation of predictions, estimation of genome quality, and infection mechanism | • Low false positive • Discovery of phage–microbe interactions |
2020 | Kieft et al. 67 |
| PPR-Meta | Identification of both phage and plasmid fragments from metagenomic using Bi-path convolutional neural network | • Available for a local PC • Identification of phages and plasmids • Novel phage identification |
2019 | Fang et al. 68 |
| MARVEL | Using a random forest machine-learning approach for prediction of double-stranded DNA bacteriophage sequences in metagenomic bins | • High sensitivity • Novel phage identification |
2018 | Amgarten et al. 69 |
| PHASTER | Phage search tool for identifying and annotating prophage sequences within bacterial genomes and plasmids | • Web-based tool • Identification and annotation of prophage sequences |
2016 | Arndt et al. 70 |
| Endogenous virus | ||||
| DeepVISP | Viral integration site prediction using convolutional 6 neural network (CNN) models in the human genome | • Online tool server • Accurate prediction of oncogenic virus integration sites • Identification of biological or regulatory roles with unknown integration site |
2021 | Ren et al. 71 |
| detectedIS | Identification of exogenous DNA integration sites in a plasmid containing transgenes or virus sequences based on a Nextflow workflow combined with a singularity | • Able to use DNA or RNA paired-end sequencing datasets • Accurate and lower computational demand with less execution times. |
2021 | Grassi et al. 72 |
| SurVirus | Viral integration caller with alignment correction of reads for the discovery of integrated sites | • Detection of novel virus integration site with less noise • Quick scan large data sets |
2021 | Rajaby et al. 73 |
| VIcaller | Identification of viral integration events using high-throughput sequencing (HTS) from human dataset through virome-wide screening of clonal integrations under Linux platform. | • Identification of breakpoint of viral integrations in human genome caused cancers • Compatible with whole genome and RNA-seq datasets |
2019 | Chen et al. 74 |
| Seeksv | Detection of somatic structural variants and viral integration using different types of sequencing data | • High efficiency and precision • Identification of breakpoint located in sequence homology regions |
2017 | Liang et al. 75 |