Appendix B — Viromics Tool Summary Table
A one-stop reference for every tool used in this book, plus close alternatives you may meet in the literature. Install commands use the same channels as Chapter 3 — always -c conda-forge -c bioconda — so environments resolve consistently. When two heavy tools conflict, install each in its own conda environment rather than forcing them together.
NoteReading the install column
Every mamba install below assumes you have already added the channels shown, or that you pass them each time. A safe, copy-paste-ready form is:
mamba create -n <env> -c conda-forge -c bioconda <package>Tools without a conda package (DeepVirFinder, WIsH) are installed from source or from the authors’ repository, as noted.
B.1 Full tool table
| Tool | Category | Purpose | Input | Output | Install | Notes |
|---|---|---|---|---|---|---|
| FastQC | QC | per-sample read quality reports | FASTQ | HTML, ZIP | mamba install -c conda-forge -c bioconda fastqc |
first QC step |
| MultiQC | QC | combine many reports into one | report folders | HTML | mamba install -c conda-forge -c bioconda multiqc |
run after FastQC across samples |
| fastp | trimming | trim adapters and low-quality bases | FASTQ | cleaned FASTQ, HTML | mamba install -c conda-forge -c bioconda fastp |
fast all-in-one preprocessor |
| Trimmomatic | trimming | alternative read trimmer | FASTQ | paired/unpaired FASTQ | mamba install -c conda-forge -c bioconda trimmomatic |
useful for teaching comparisons |
| MEGAHIT | assembly | metagenomic assembly | clean FASTQ | contigs | mamba install -c conda-forge -c bioconda megahit |
fast, memory efficient; good on 32 GB RAM |
| metaSPAdes | assembly | metagenomic assembly | clean FASTQ | contigs, scaffolds | mamba install -c conda-forge -c bioconda spades |
higher quality, heavier than MEGAHIT |
| QUAST | assembly QC | assembly statistics | contigs | reports | mamba install -c conda-forge -c bioconda quast |
reports N50, length, GC; N50 alone is not enough |
| Prodigal | gene calling | predict protein-coding ORFs | contigs | FAA, GFF | mamba install -c conda-forge -c bioconda prodigal |
use -p meta; feeds protein-based tools (Hyatt et al. 2010) |
| VirSorter2 | viral prediction | identify viral contigs | contigs | viral FASTA, scores | mamba install -c conda-forge -c bioconda virsorter=2 |
multi-classifier; pair with CheckV (Guo et al. 2021) |
| geNomad | viral prediction | identify viruses and plasmids | contigs | predictions, taxonomy | mamba install -c conda-forge -c bioconda genomad |
strong modern default; end-to-end mode (Camargo et al. 2024) |
| VirFinder | viral prediction | k-mer model for viral contigs | contigs | scores, p-values | mamba install -c conda-forge -c bioconda r-virfinder |
reference-free; R package (Ren et al. 2017) |
| DeepVirFinder | viral prediction | deep-learning viral scoring | contigs | scores, p-values | install from source (GitHub) | successor to VirFinder; no conda package (Ren et al. 2020) |
| VIBRANT | viral prediction/annotation | recover and annotate viruses | contigs | viral FASTA, annotations | mamba install -c conda-forge -c bioconda vibrant |
flags AMGs; microbial viruses |
| CheckV | quality | completeness and contamination | viral FASTA | quality tables, trimmed FASTA | mamba install -c conda-forge -c bioconda checkv |
essential QC filter; trims host flanks (Nayfach et al. 2021) |
| CD-HIT | clustering | fast sequence dereplication | FASTA | representatives, clusters | mamba install -c conda-forge -c bioconda cd-hit |
simple and fast; cd-hit-est for nucleotides (Fu et al. 2012) |
| vClust | clustering | ANI-based vOTU clustering | viral FASTA | ANI table, clusters | mamba install -c conda-forge -c bioconda vclust |
95% ANI vOTUs; add --qcov 0.85 for the MIUViG alignment-fraction cutoff (Zielezinski et al. 2025) |
| vConTACT2 | taxonomy | gene-sharing network taxonomy | proteins, gene map | viral clusters (VCs) | mamba install -c conda-forge -c bioconda vcontact2 |
prokaryotic viruses (Bin Jang et al. 2019) |
| PhaBOX2 / PhaGCN | taxonomy/lifestyle | phage taxonomy, lifestyle, host | viral FASTA | taxonomy, lifestyle, host | mamba create -n phabox -c conda-forge -c bioconda phabox |
integrated phage toolkit; graph + learning models (Shang et al. 2026) |
| DRAM-v | annotation | viral functions and AMGs | VirSorter2 output | annotation tables | install in a dedicated DRAM env | database heavy; distilled AMG summary (Shaffer et al. 2020) |
| eggNOG-mapper | annotation | orthology and function | proteins | annotation table | mamba install -c conda-forge -c bioconda eggnog-mapper |
broad functional annotation (Cantalapiedra et al. 2021) |
| Bowtie2 | mapping | align reads to vOTUs | reads, index | SAM/BAM | mamba install -c conda-forge -c bioconda bowtie2 |
abundance step; build index first (Langmead and Salzberg 2012) |
| samtools | BAM handling | sort, index, filter alignments | SAM/BAM | sorted, indexed BAM | mamba install -c conda-forge -c bioconda samtools |
glue for the mapping workflow (Danecek et al. 2021) |
| CoverM | coverage | per-contig coverage and TPM | BAM or FASTQ | coverage/abundance table | mamba install -c conda-forge -c bioconda coverm |
builds the vOTU abundance table |
| iPHoP | host prediction | integrated host prediction | viral FASTA | host predictions | mamba install -c conda-forge -c bioconda iphop |
database heavy; combines multiple signals |
| WIsH | host prediction | composition-based host prediction | host + viral FASTA | log-likelihood scores | install from source (GitHub) | needs a relevant host genome set (Galiez et al. 2017) |
| MAFFT | phylogeny | multiple sequence alignment | marker FASTA | aligned FASTA | mamba install -c conda-forge -c bioconda mafft |
align homologous markers (Katoh and Standley 2013) |
| IQ-TREE 2 | phylogeny | maximum-likelihood trees | alignment | tree, support values | mamba install -c conda-forge -c bioconda iqtree |
model selection + bootstraps (Minh et al. 2020) |
Hyatt, Doug, Gwo-Liang Chen, Philip F. LoCascio, Miriam L. Land, Frank W. Larimer, and Loren J. Hauser. 2010. “Prodigal: Prokaryotic Gene Recognition and Translation Initiation Site Identification.” BMC Bioinformatics 11: 119. https://doi.org/10.1186/1471-2105-11-119.
Guo, Jiarong, Benjamin Bolduc, Ahmed A. Zayed, Arvind Varsani, Gabriela Dominguez-Huerta, Tom O. Delmont, Akbar A. Pratama, et al. 2021. “VirSorter2: A Multi-Classifier, Expert-Guided Approach to Detect Diverse DNA and RNA Viruses.” Microbiome 9: 37. https://doi.org/10.1186/s40168-020-00990-y.
Camargo, Antonio Pedro, Simon Roux, Frederik Schulz, Michal Babinski, Yan Xu, Bin Hu, Patrick S. G. Chain, Stephen Nayfach, and Nikos C. Kyrpides. 2024. “Identification of Mobile Genetic Elements with geNomad.” Nature Biotechnology 42: 1303–12. https://doi.org/10.1038/s41587-023-01953-y.
Ren, Jie, Nathan A. Ahlgren, Yang Young Lu, Jed A. Fuhrman, and Fengzhu Sun. 2017. “VirFinder: A Novel k-Mer Based Tool for Identifying Viral Sequences from Assembled Metagenomic Data.” Microbiome 5: 69. https://doi.org/10.1186/s40168-017-0283-5.
Ren, Jie, Kai Song, Chao Deng, Nathan A. Ahlgren, Jed A. Fuhrman, Yi Li, Xiaohui Xie, Ryan Poplin, and Fengzhu Sun. 2020. “Identifying Viruses from Metagenomic Data Using Deep Learning.” Quantitative Biology 8 (1): 64–77. https://doi.org/10.1007/s40484-019-0187-4.
Nayfach, Stephen, Antonio Pedro Camargo, Frederik Schulz, Emiley Eloe-Fadrosh, Simon Roux, and Nikos C. Kyrpides. 2021. “CheckV Assesses the Quality and Completeness of Metagenome-Assembled Viral Genomes.” Nature Biotechnology 39: 578–85. https://doi.org/10.1038/s41587-020-00774-7.
Fu, Limin, Beifang Niu, Zhengwei Zhu, Sitao Wu, and Weizhong Li. 2012. “CD-HIT: Accelerated for Clustering the Next-Generation Sequencing Data.” Bioinformatics 28 (23): 3150–52. https://doi.org/10.1093/bioinformatics/bts565.
Zielezinski, Andrzej, Adam Gudyś, Jakub Barylski, Krzysztof Siminski, Piotr Rozwalak, Bas E. Dutilh, and Sebastian Deorowicz. 2025. “Ultrafast and Accurate Sequence Alignment and Clustering of Viral Genomes.” Nature Methods 22: 1191–94. https://doi.org/10.1038/s41592-025-02701-7.
Bin Jang, Ho, Benjamin Bolduc, Olivier Zablocki, Jens H. Kuhn, Simon Roux, Evelien M. Adriaenssens, J. Rodney Brister, et al. 2019. “Taxonomic Assignment of Uncultivated Prokaryotic Virus Genomes Is Enabled by Gene-Sharing Networks.” Nature Biotechnology 37: 632–39. https://doi.org/10.1038/s41587-019-0100-8.
Shang, Jiayu, Cheng Peng, Jiaojiao Guan, Dehan Cai, Donglin Wang, and Yanni Sun. 2026. “PhaBOX2: An Enhanced Web Server for Discovering and Analyzing Viral Contigs in Metagenomic Data.” Nucleic Acids Research 54: W169–76. https://doi.org/10.1093/nar/gkag382.
Shaffer, Michael, Mikayla A. Borton, Brendan B. McGivern, Ahmed A. Zayed, Sabina L. La Rosa, Lindsey M. Solden, Pengfei Liu, et al. 2020. “DRAM for Distilling Microbial Metabolism to Automate the Curation of Microbiome Function.” Nucleic Acids Research 48 (16): 8883–8900. https://doi.org/10.1093/nar/gkaa621.
Cantalapiedra, Carlos P., Ana Hernandez-Plaza, Ivica Letunic, Peer Bork, and Jaime Huerta-Cepas. 2021. “eggNOG-Mapper V2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale.” Molecular Biology and Evolution 38 (12): 5825–29. https://doi.org/10.1093/molbev/msab293.
Langmead, Ben, and Steven L. Salzberg. 2012. “Fast Gapped-Read Alignment with Bowtie 2.” Nature Methods 9 (4): 357–59. https://doi.org/10.1038/nmeth.1923.
Danecek, Petr, James K. Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O. Pollard, Andrew Whitwham, et al. 2021. “Twelve Years of SAMtools and BCFtools.” GigaScience 10 (2): giab008. https://doi.org/10.1093/gigascience/giab008.
Galiez, Clément, Matthias Siebert, François Enault, Jonathan Vincent, and Johannes Söding. 2017. “WIsH: Who Is the Host? Predicting Prokaryotic Hosts from Metagenomic Phage Contigs.” Bioinformatics 33 (19): 3113–14. https://doi.org/10.1093/bioinformatics/btx383.
Katoh, Kazutaka, and Daron M. Standley. 2013. “MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability.” Molecular Biology and Evolution 30 (4): 772–80. https://doi.org/10.1093/molbev/mst010.
Minh, Bui Quang, Heiko A. Schmidt, Olga Chernomor, Dominik Schrempf, Michael D. Woodhams, Arndt von Haeseler, and Robert Lanfear. 2020. “IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era.” Molecular Biology and Evolution 37 (5): 1530–34. https://doi.org/10.1093/molbev/msaa015.
B.2 Choosing between overlapping tools
| If you need to… | Reasonable default | Alternative | Why |
|---|---|---|---|
| Call viral contigs | geNomad | VirSorter2 | geNomad is fast and current; run both for agreement |
| Score novel/short contigs | DeepVirFinder | VirFinder | reference-free k-mer/deep-learning signal fills reference gaps |
| Assemble on 32 GB RAM | MEGAHIT | metaSPAdes | MEGAHIT is lighter; metaSPAdes is higher quality when RAM allows |
| Dereplicate into vOTUs | vClust | CD-HIT | vClust clusters by ANI; pass --qcov 0.85 with --ani 0.95 for the MIUViG 85% alignment-fraction rule |
| Predict host | iPHoP | WIsH | iPHoP integrates many signals; WIsH is lightweight but needs candidate hosts |
| Assign phage taxonomy | geNomad | vConTACT2 / PhaBOX | geNomad is quick; the others add network and graph evidence |