A phylogeny based comparative genomics software to analyze the genetic environment of genes. The user can select one or several taxa and provide one or several reference protein(s). Genomes and plasmids (based on user choice) will be downloaded from the NCBI Assembly/NR database and searched for the respective gene. Up to 20kb of the genes genetic environment are extracted, annotated and aligned between all genomes. The sequences are then visualized, enabling comparison of synteny and gene content.
GEnView consists of three main scripts - genview_create_db.py, genview_extract.py and genview_visualize.py
The code can be downloaded simply through cloning the git repository:
git clone https://github.com/EbmeyerSt/GEnView.git
We recommend to set up a virtual environment and install all dependencies to run GEnView there.
- Python >=3.7.6
- ete3 >=3.1.1
- pandas >=1.0.3
- sqlite3 >=2.6
- Biopython >=1.77
The following softwares should be located in your $PATH:
- Diamond >=0.9.24
- Prodigal >=2.6.3
- CD-hit >=4.7
- Usearch >=8.0.1445
- mafft >=7.310
- FastTree >=2.1.9 (optional)
genview_create_db.py automatically downloads genomes (and if specied, plasmids) of the specified species from the NCBI Assembly/NR database and search them for the provided reference protein(s). Up to 20kb are extracted upstream and downstream, annotated and aligned.
An example walk-through can be found in the wiki: https://github.com/EbmeyerSt/GEnView/wiki
usage: genview_create_db.py [-h] -d TARGET_DIRECTORY -db DATABASE
[-p PROCESSES] [-id IDENTITY] [--erase]
[-scov SUBJECT_COVERAGE] -split SPLIT
[--is_db IS_DB] [--taxa TAXA [TAXA ...]]
[--assemblies] [--plasmids]
[--save_tmps]
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Creates sqlite3 database with genetic environment from genomes containing the provided reference gene(s).
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optional arguments:
-h, --help show this help message and exit
-d TARGET_DIRECTORY, --target_directory TARGET_DIRECTORY
path to folder containing a folder for each assembly to be processed
-db DATABASE, --database DATABASE
fasta/multifasta file containing genes to be annotated
-p PROCESSES, --processes PROCESSES
of cores to run the script on
-id IDENTITY, --identity IDENTITY
identity cutoff for hits to be saved to the database
-scov SUBJECT_COVERAGE, --subject_coverage SUBJECT_COVERAGE
minimum coverage for a hit to be saved to db
-split SPLIT number of files to obtain for processing flanking regions
--is_db IS_DB database containing IS, integrons, ISCR sequences
--taxa TAXA [TAXA ...]
taxon/taxa names to download genomes for - use "all" do download all available genomes
--assemblies Search NCBI Assembly database
--plasmids Search NCBI Refseq plasmid database
--save_tmps keep temporary files
--acc_list File containing Assembly/plasmid accessions, one accession per row
WARNING: if you specify --taxa 'all', enview_db.py will attempt to download all available genomes and plasmids - > 4TB of data! Processing of all data at once will take several days. Consider downloading smaller subsets instead. If you do not know in which taxa your gene is present, we recommend doing a manual blast at https://blast.ncbi.nlm.nih.gov/Blast.cgi first - This will show you in which taxa your protein is found!
genview_extract.py extracts the gene coding for the reference protein and its genetic environment as multifasta from the previously created database.
usage: genview_extract.py [-h] [-genes GENES [GENES ...]] -o O -db DB -id ID
[-taxa TAXA [TAXA ...]]
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Extract specified genes and flanking regions from db
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optional arguments:
-h, --help show this help message and exit
-genes GENES [GENES ...]
genes to extract. If list: "qnrA" "qnrB" ...
-o O path to output directory
-db DB path to sqlite db file in original directory
-id ID percent identity threshold for genes to extract
-taxa TAXA [TAXA ...]
list of genera and/or species to extract.
By default all taxa are extracted
genview_visualize.py takes the previously extracted sequences as input and creates a pdf visualizing the reference gene in its genetic environment
usage: genview_visualize.py [-h] -db DB -o O [--force] [--compressed] [--all]
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Visualize annotate genes and genetic environments
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optional arguments:
-h, --help show this help message and exit
-db DB sqlite3 db containing annotations
-o O target directory
--force Force new alignment and phylogeny
--compressed Compress number of displayed sequences
specifying --compressed will cluster all sequences at 95% nucleotide identity and remove duplicates. Only the centroids of the resulting clusters will be visualized.
The following output files will be produced in the specified output directory when running visualize.py:
annotation_meta.csv - Contains information on annotated genes in the extracted range of the target gene, such as name, position and sequence
yourgenename_contexts._tree_annotated.pdf - PDF containing a phylogeny based visualization of the target genes genetic environment
yourgenename_contexts.fna - FASTA file containing extracted sequence (target gene+genetic environment) for every genome
yourgenename_contexts.unique.fna - FASTA file containing extracted sequence (target gene+genetic environment) for every genome after removing duplicates
yourgenename_contexts.unique.aln Alignment of unique extracted sequences
yourgenename_contexts.unique.tree Tree file created by FastTree2