https://github.com/medvir/VirMet
Set of tools for viral metagenomics.
https://github.com/medvir/VirMet
bacterial-database bioinformatics bioinformatics-pipeline database genome miseq ncbi python viral-database viral-metagenomics
Last synced: about 2 months ago
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Set of tools for viral metagenomics.
- Host: GitHub
- URL: https://github.com/medvir/VirMet
- Owner: medvir
- Created: 2013-08-21T09:24:53.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2025-03-18T09:40:36.000Z (about 2 months ago)
- Last Synced: 2025-03-18T10:36:19.618Z (about 2 months ago)
- Topics: bacterial-database, bioinformatics, bioinformatics-pipeline, database, genome, miseq, ncbi, python, viral-database, viral-metagenomics
- Language: Python
- Size: 1.14 MB
- Stars: 14
- Watchers: 5
- Forks: 5
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
Awesome Lists containing this project
- awesome-virome - VirMet - In-house database download from GenBank of viral references. [Python] (Databases / Gene Finding)
README
VirMet
------
[](http://bioconda.github.io/recipes/virmet/README.html)
[](https://travis-ci.org/ozagordi/VirMet)
[](https://codecov.io/github/ozagordi/VirMet?branch=master)
[](https://codebeat.co/projects/github-com-ozagordi-virmet-master)
Watch out: only a few files are counted in coverage statistics.
Full documentation on [Read the Docs](http://virmet.rtfd.org/en/latest/).
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
- `fetch` download genomes
- `update` update viral/bacterial database
- `index` index genomes
- `wolfpack` analyze a Miseq run
- `covplot` plot coverage for a specific organism
A short help is obtained with `virmet subcommand -h`.
#### The simplest example
[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.
### Installation
#### Bioconda
VirMet is available through [Bioconda](https://bioconda.github.io), a channel
for the [conda](http://conda.pydata.org/docs/intro.html) package manager. Once
conda is [installed](https://bioconda.github.io/#install-conda) and the
[channels](https://bioconda.github.io/#set-up-channels) are set up,
`conda install virmet` installs the package with all its dependencies.
#### `setuptools` or Docker
The classic `python setup.py install` will work, but see the relevant
[page](http://virmet.readthedocs.io/en/latest/installation/) of the
documentation to install third-party tools, or follow the instructions
to run the [docker version](http://virmet.readthedocs.io/en/latest/dockerised/).
### Preparation
VirMet contains programs to download and index the genome sequences,
instructions [here](http://virmet.readthedocs.io/en/latest/preparation/).
### Running a virus scan
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
### Updating the database
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](https://gist.github.com/ozagordi/c1e1c4158ab4e94e4683)).
](./docs/viral_genomes.png "NCBI complete viral genomes per month")
VirMet provides a simple way to [update the viral database](http://virmet.readthedocs.io/en/latest/updating/).