Watch out: only a few files are counted in coverage statistics.
Full documentation on Read the Docs.
A set of tools for viral metagenomics.
virmet is called with a command subcommand
syntax: virmet fetch --viral n, for example, downloads the bacterial
database. Other available subcommands so far are
fetchdownload genomesupdateupdate viral/bacterial databaseindexindex genomeswolfpackanalyze a Miseq runcovplotplot coverage for a specific organism
A short help is obtained with virmet subcommand -h.
[user@host ~]$ virmet wolfpack --run path_to_run_directory
... some time later ...
[user@host ~]$ wc virmet_output_name_of_the_run/sample_name/orgs_list.csv
9 128 963 orgs_list2.tsv
Reads are filtered, decontaminated, and finally blasted against a (large) set of viral sequences. Results for each database sequence to which similar reads were found are summarised in a tsv file is with columns
species: scientific name of the species corresponding to the database sequence;reads: number of reads assigned to this specific sequence;stitle: title of the sequence in the database (fasta header);ssciname: scientific name of the sequence;covered_region: number of nucleotides covered by at least one read;seq_len: length of the sequence.
An example of such a file is reported here.
VirMet is available through Bioconda, a channel
for the conda package manager. Once
conda is installed and the
channels are set up,
conda install virmet installs the package with all its dependencies.
The classic python setup.py install will work, but see the relevant
page of the
documentation to install third-party tools, or follow the instructions
to run the docker version.
VirMet contains programs to download and index the genome sequences, instructions here.
This can be run on a single file or on a directory. It will try to guess from
the naming scheme if it is a Miseq output directory (i.e. with
Data/Intensities/BaseCalls/ structure) and analyze all fastq files in there.
The extension must be .fastq or .fastq.gz. It will then run a filtering
step based on quality, length and entropy (in short: reads with a lot of
repeats will be discarded), followed by a decontamination step where reads
of human/bacterial/bovine/fungal origin will be discarded. Finally, remaining
reads are blasted against the viral database. The list of organisms with the
count of reads is in files orgs_list.csv in the output directory
(naming is virmet_output_...). For example, if we have a directory named
exp_01 with files
exp_01/AR-1_S1_L001_R1_001.fastq.gz
exp_01/AR-2_S2_L001_R1_001.fastq.gz
exp_01/AR-3_S3_L001_R1_001.fastq.gz
exp_01/AR-4_S4_L001_R1_001.fastq.gz
we could run
[user@host test_virmet]$ virmet wolfpack --dir exp_01
and, after some time, find the results in virmet_output_exp01. Many files are
present, the most important ones being orgs_list.csv and stats.tsv. The
first lists the viral organisms found with a count of reads that could be
matched to them.
[user@host test_virmet]$ cat virmet_output_test_dir_150123/3-1-65_S5/orgs_list.tsv
organism reads
Human adenovirus 7 126
Human poliovirus 1 strain Sabin 45
Human poliovirus 1 Mahoney 29
Human adenovirus 3+11p 19
Human adenovirus 16 1
The second file is a summary of all reads analyzed for this sample and how many were passing a specific step of the pipeline or matching a specific database.
[user@host test_virmet]$ cat virmet_output_exp01/AR-1_S1/stats.tsv
raw_reads 6250
trimmed_too_short 462
low_entropy 1905
low_quality 0
passing_filter 3883
matching_humanGRCh38 3463
matching_bact1 0
matching_bact2 0
matching_bact3 0
matching_fungi1 0
matching_bt_ref 0
reads_to_blast 420
viral_reads 257
undetermined_reads 163
More and more sequences are uploaded to NCBI database every month. The figure shows the number of viral sequences with complete genome in the title that are submitted every month to NCBI (code).
VirMet provides a simple way to update the viral database.