Quickstart

If you are running geNomad for the first time, follow the steps below to learn how to download geNomad’s database, execute the virus and plasmid prediction pipeline, and interpret the results. If you want to understand exactly what is going on when you are executing geNomad, check out the Pipeline page.

Downloading the database

geNomad depends on a database that contains the profiles of the markers that are used to classify sequences, their taxonomic information, their functional annotation, etc. So, you should first download the database to your current directory:

genomad download-database .

The database will be contained within the genomad_db directory. If you prefer, you can also download the database from Zenodo and extract it manually.

Executing geNomad

Now you are ready to go! geNomad works by executing a series of modules sequentially (you can find more information about this in the pipeline documentation), but we provide a convenient end-to-end command that will execute the entire pipeline for you in one go.

In this example, we will use an Klebsiella pneumoniae genome (GCF_009025895.1) as input. You can use any FASTA file containing nucleotide sequences as input. geNomad will work for isolate genomes, metagenomes, and metatranscriptomes.

The command to execute geNomad is structured like this:

genomad end-to-end [OPTIONS] INPUT OUTPUT DATABASE

So, to run the full geNomad pipeline (end-to-end command), taking a nucleotide FASTA file (GCF_009025895.1.fna.gz) and the database (genomad_db) as input, we will execute the following command:

genomad end-to-end --cleanup --splits 8 GCF_009025895.1.fna.gz genomad_output genomad_db

The results will be written inside the genomad_output directory.

Some notes about the parameters

• The --cleanup option was used to force geNomad to delete intermediate files that were generated during the execution. This will save you some storage space.

• The --splits 8 parameter was used here to make it possible to run this example in a notebook. geNomad searches a big database of protein profiles that take up a lot of space in memory. To prevent the execution from failing due to insufficient memory, we can use the --splits parameter to split the search into chuncks. If you are running geNomad in a big server you might not need to split your search, increasing the execution speed.

• Note that the input FASTA file that was used as input is compressed. This is possible because geNomad supports input files compressed as .gz, .bz2, or .xz.

Controlling the classification stringency

By default, geNomad applies a series of post-classification filters to remove likely false positives. For example, sequences are required to have a plasmid or virus score of at least 0.7 and sequences shorter than 2,500 bp are required to encode at least one hallmark gene. If you want to disable the post-classification filters, add the --relaxed flag to your command. On the other hand, if you want to be very conservative with your classification, you may use the --conservative flag. This will make the post-classification filters more aggressive, preventing sequences without strong support from being classified as plasmid or virus. You can check out the default, relaxed, and conservative post-classification filters here.

Understanding the outputs

In this example, the results of geNomad’s analysis will be written to the genomad_output directory, which will look like this:

genomad_output
├── GCF_009025895.1_aggregated_classification
├── GCF_009025895.1_aggregated_classification.log
├── GCF_009025895.1_annotate
├── GCF_009025895.1_annotate.log
├── GCF_009025895.1_find_proviruses
├── GCF_009025895.1_find_proviruses.log
├── GCF_009025895.1_marker_classification
├── GCF_009025895.1_marker_classification.log
├── GCF_009025895.1_nn_classification
├── GCF_009025895.1_nn_classification.log
├── GCF_009025895.1_summary
╰── GCF_009025895.1_summary.log

As mentioned above, geNomad works by executing several modules sequentially. Each one of these will produce a log file (<prefix>_<module>.log) and a subdirectory (<prefix>_<module>).

For this example, we will only look at the files within GCF_009025895.1_summary. The <prefix>_summary directory contains files that summarize the results that were generated across the pipeline. If you just want a list of the plasmids and viruses identified in your input, this is what you are looking for.

genomad_output
╰── GCF_009025895.1_summary
    ├── GCF_009025895.1_plasmid.fna
    ├── GCF_009025895.1_plasmid_genes.tsv
    ├── GCF_009025895.1_plasmid_proteins.faa
    ├── GCF_009025895.1_plasmid_summary.tsv
    ├── GCF_009025895.1_summary.json
    ├── GCF_009025895.1_virus.fna
    ├── GCF_009025895.1_virus_genes.tsv
    ├── GCF_009025895.1_virus_proteins.faa
    ╰── GCF_009025895.1_virus_summary.tsv

First, let’s look at GCF_009025895.1_virus_summary.tsv:

seq_name	length	topology	coordinates	n_genes	genetic_code	virus_score	fdr	n_hallmarks	marker_enrichment	taxonomy
NZ_CP045015.1|provirus_2885510_2934610	49101	Provirus	2885510-2934610	69	11	0.9776	NA	14	76.0892	Viruses;Duplodnaviria;Heunggongvirae;Uroviricota;Caudoviricetes;;
NZ_CP045015.1|provirus_3855947_3906705	50759	Provirus	3855947-3906705	79	11	0.9774	NA	16	75.1552	Viruses;Duplodnaviria;Heunggongvirae;Uroviricota;Caudoviricetes;;
NZ_CP045018.1	51887	No terminal repeats	NA	57	11	0.9774	NA	14	67.7749	Viruses;Duplodnaviria;Heunggongvirae;Uroviricota;Caudoviricetes;;

This tabular file lists all the viruses that geNomad found in your input and gives you some convenient information about them. Here’s what each column contains:

• seq_name: The identifier of the sequence in the input FASTA file. Proviruses will have the following name scheme: <sequence_identifier>|provirus_<start_coordinate>_<end_coordinate>.

• length: Length of the sequence (or the provirus, in the case of integrated viruses).

• topology: Topology of the viral sequence. Possible values are: No terminal repeats, DTR (direct terminal repeats), ITR (inverted terminal repeats), or Provirus (viruses integrated in host genomes).

• coordinates: 1-indexed coordinates of the provirus region within host sequences. Will be NA for viruses that were not predicted to be integrated.

• n_genes: Number of genes encoded in the sequence.

• genetic_code: Predicted genetic code. Possible values are: 11 (standard code for Bacteria and Archaea), 4 (recoded TGA stop codon), or 15 (recoded TAG stop codon).

• virus_score: A measure of how confident geNomad is that the sequence is a virus. Sequences that have scores close to 1.0 are more likely to be viruses than the ones that have lower scores.

• fdr: The estimated false discovery rate (FDR) of the classification (that is, the expected proportion of false positives among the sequences up to this row). To estimate FDRs geNomad requires score calibration, which is turned off by default. Therefore, this column will only contain NA values in this example.

• n_hallmarks: Number of genes that matched a hallmark geNomad marker. Hallmarks are genes that were previously associated with viral function and their presence is a strong indicative that the sequence is indeed a virus.

• marker_enrichment: A score that represents the total enrichment of viral markers in the sequence. The value goes as the number of virus markers in the sequence increases, so sequences with multiple markers will have higher score. Chromosome and plasmid markers will reduce the score.

• taxonomy: Taxonomic assignment of the virus genome. Lineages follow the taxonomy contained in ICTV’s VMR number 19. Viruses can be taxonomically assigned up to the family level, but not to specific genera or species within that family. The taxonomy is presented with a fixed number of fields (corresponding to taxonomic ranks) separated by semicolons, with empty fields left blank.

In our example, geNomad identified several proviruses integrated into the K. pneumoniae genome and one extrachromosomal phage. Since they all have high scores and marker enrichment, we can be confident that these are indeed viruses. They were all predicted to use the genetic code 11 and were assigned to the Caudoviricetes class, which contains all the tailed bacteriphages. In the taxonomy field for these viruses, after Caudoviricetes, there are two consecutive semicolons because geNomad could only assign them to the class level, leaving the order and family ranks empty.

Another important file is GCF_009025895.1_virus_genes.tsv. During its execution, geNomad annotates the genes encoded by the input sequences using a database of chromosome, plasmid, and virus-specific markers. The <prefix>_virus_genes.tsv file summarizes the annotation of the genes encoded by the identified viruses.

gene	start	end	length	strand	gc_content	genetic_code	rbs_motif	marker	evalue	bitscore	uscg	plasmid_hallmark	virus_hallmark	taxid	taxname	annotation_conjscan	annotation_amr	annotation_accessions	annotation_description
NZ_CP045018.1_1	1	399	399	1	0.536	11	None	GENOMAD.108715.VP	2.54E-32	123	0	0	1	2561	Caudoviricetes	NA	NA	PF05100;COG4672;TIGR01600	Phage minor tail protein L
NZ_CP045018.1_2	401	1111	711	1	0.568	11	AGGAG	GENOMAD.168265.VP	9.28E-47	170	0	0	0	2561	Caudoviricetes	NA	NA	PF14464;COG1310;K21140;TIGR02256	Proteasome lid subunit RPN8/RPN11, contains Jab1/MPN domain metalloenzyme (JAMM) motif
NZ_CP045018.1_3	1143	1493	351	1	0.382	11	AGGAG	GENOMAD.147875.VV	1.50E-14	71	0	0	0	2561	Caudoviricetes	NA	NA	COG5633;TIGR03066	NA
NZ_CP045018.1_4	1509	2120	612	1	0.477	11	GGA/GAG/AGG	GENOMAD.143103.VP	1.96E-50	179	0	0	1	2561	Caudoviricetes	NA	NA	PF06805;COG4723;TIGR01687	Phage-related protein, tail component
NZ_CP045018.1_5	2183	13516	11334	1	0.566	11	None	GENOMAD.159864.VP	1.23E-268	923	0	0	0	2561	Caudoviricetes	NA	NA	PF12421;PF09327	Fibronectin type III protein
NZ_CP045018.1_6	13585	15084	1500	1	0.55	11	AGGAG	GENOMAD.195756.VP	2.02E-14	79	0	0	0	2561	Caudoviricetes	NA	NA	NA	NA
NZ_CP045018.1_7	15163	16128	966	-1	0.469	11	GGAGG	NA	NA	NA	0	0	0	1	NA	NA	NA	NA	NA

The columns in this file are:

• gene: Identifier of the gene (<sequence_name>_<gene_number>). Usually, gene numbers start with 1 (first gene in the sequence). However, genes encoded by prophages integrated in the middle of the host chromosome may start with a different number, depending on it’s position within the chromosome.

• start: 1-indexed start coordinate of the gene.

• end: 1-indexed end coordinate of the gene.

• length: Length of the gene locus (in base pairs).

• strand: Strand that encodes the gene. Can be 1 (direct strand) or -1 (reverse strand).

• gc_content: GC content of the gene locus.

• genetic_code: Predicted genetic code (see details in the explanation of the summary file).

• rbs_motif: Detected motif of the ribosome-binding site.

• marker: Best matching geNomad marker. If this gene doesn’t match any markers, the value will be NA.

• evalue: E-value of the alignment between the protein encoded by the gene and the best matching geNomad marker.

• bitscore: Bitscore of the alignment between the protein encoded by the gene and the best matching geNomad marker.

• uscg: Whether the marker assigned to this gene corresponds to a universal single-copy gene (UCSG, as defined in BUSCO v5). These genes are expected to be found in chromosomes and are rare in plasmids and viruses. Can be 1 (gene is USCG) or 0 (gene is not USCG).

• plasmid_hallmark: Whether the marker assigned to this gene represents a plasmid hallmark.

• virus_hallmark: Whether the marker assigned to this gene represents a virus hallmark.

• taxid: Taxonomic identifier of the marker assigned to this gene (you can ignore this as it is meant to be used internally by geNomad).

• taxname: Name of the taxon associated with the assigned geNomad marker. In this example, we can see that the annotated proteins are all characteristic of Caudoviricetes (which is why the provirus was assigned to this class).

• annotation_conjscan: If the marker that matched the gene is a conjugation-related gene (as defined in CONJscan) this field will show which CONJscan acession was assigned to the marker.

• annotation_amr: If the marker that matched the gene was annotated with an antimicrobial resistance (AMR) function (as defined in NCBIfam-AMRFinder), this field will show which NCBIfam acession was assigned to the marker.

• annotation_accessions: Some of the geNomad markers are functionally annotated. This column tells you which entries in Pfam, TIGRFAM, COG, and KEGG were assigned to the marker.

• annotation_description: A text describing the function assigned to the marker.

In the example above we can see the information of the first seven genes encoded by NZ_CP045018.1. The last entry didn’t match any geNomad marker. The first six were all assigned to protein families, some of which are typical of tailed bacteriphages (such as the minor tail protein), reassuring us that these are indeed Caudoviricetes.

One important detail here is that the primary purpose of geNomad’s markers is classification. They were designed to be specific to chromosomes, plasmids, or viruses, enabling the distinction of sequences belonging to these classes. Therefore, you should not expect that every single viral gene will be annotated with a geNomad marker. If you want to annotate the genes within your sequences as throughly as possible, you should use databases such as Pfam or COG.

The other two virus-related files within the summary directory are GCF_009025895.1_virus.fna and GCF_009025895.1_virus_proteins.faa. These are FASTA files of the identified virus sequences and their proteins, respectively. Proviruses are automatically excised from the host sequence.

Moving on to plasmids, the data related to their identification can be found in the <prefix>_plasmid_summary.tsv, <prefix>_genes.tsv, <prefix>_plasmid.fna, and <prefix>_plasmid_proteins.faa files. These are mostly very similar to their virus counterparts. The differences in <prefix>_plasmid_summary.tsv (shown below) are the following:

• Virus-specific columns that are in <prefix>_virus_summary.tsv (coordinates and taxonomy) are not present.

• The conjugation_genes column lists genes that might be involved in conjugation. It’s important to note that the presence of such genes is not sufficient to tell whether a given plasmid is conjugative or mobilizible. If you are interested in identifying conjugative plasmids, we recommend you to analyze the plasmids you identified using geNomad with CONJscan.

• The amr_genes column lists genes annotated with antimicrobial resistance function. You can check the specific functions associated with each accession in AMRFinderPlus website.

seq_name	length	topology	n_genes	genetic_code	plasmid_score	fdr	n_hallmarks	marker_enrichment	conjugation_genes	amr_genes
NZ_CP045020.1	28729	No terminal repeats	36	11	0.9955	NA	7	25.8098	F_traE	NA
NZ_CP045022.1	50635	No terminal repeats	61	11	0.9947	NA	9	46.4657	T_virB1;T_virB3;virb4;T_virB5;T_virB6;T_virB8;T_virB9	NA
NZ_CP045019.1	44850	No terminal repeats	52	11	0.9945	NA	3	28.711	F_traE	NA
NZ_CP045016.1	82240	No terminal repeats	110	11	0.9939	NA	11	33.4021	T_virB8;T_virB9;F_traF;F_traH;F_traG;T_virB1	NF000225;NF000270;NF012171;NF000052
NZ_CP045017.1	61331	No terminal repeats	76	11	0.9934	NA	16	36.2817	I_trbB;I_trbA;MOBP1;I_traI;I_traK;I_traL;I_traN;I_traO;I_traP;I_traQ;I_traR;traU;I_traW;I_traY;F_traE	NA
NZ_CP045021.1	5251	No terminal repeats	7	11	0.991	NA	1	1.4225	NA	NA