Note this is the original version of Phenopolis Browser, we are working on the much improved 2021 version that you can find at https://github.com/phenopolis/phenopolis_browser
Preprint on biorxiv. Published in Bioinformatics.
Phenopolis is used for research into the molecular diagnosis of rare genetic diseases by clinicians, geneticists and bioinformaticians at:
You can access a demo version of the server at: https://www.phenopolis.org
We are especially interested in contributions to the UI (html, css, js) which could be greatly refactored and vastly improved. Also any performance improvements to the db queries would be also greatly appreciated. Let us know if you run into difficulties getting the code running! Our goal is to make it easy for you to contribute so the project continues to grow!
This section includes guides to a quick install and a full installation.
print
syntax are not compatible with python3. https://www.python.org/downloads/This quick install is for people who want to get a local version up and running quickly to contribute to the codebase of the project.
I have a written a shell script for quick installation, easy_install.sh, on some example data that is downloadable from our website. This will only take ~256M of disk space.
Start Neo4j and, if this is the first time you've run Neo4j, log in and change the password. Set your Neo4j uri and password in easy_install.sh.
Clone this repo and run easy_install.sh. This will install packages and get data. When complete, you should be able to browse to: http://localhost:8000/gene/TTLL5
The example dataset covers only gene TTLL5. Web pages for other genes will show no information.
Phenopolis can be developed under Windows but requires some additional steps and some lesser-used functionality will not be available.
python -m pip install -U pip
IMPORT_PYSAM_PRIMER3 = False
git clone
, wget
, mongoimport
and mongo
).To debug in Visual Studio, first turn off the Flask debug by setting app.run(..,..,..,debug=False)
in runserver.py
.
When this is installed you should be able to browse to: http://localhost:8000/gene/TTLL5
The example dataset covers only gene TTLL5. Web pages for other genes will show no information.
Phenopolis requires:
You will then be able to run phenopolis.py
, the python Flask server.
The first step is to clone the repository.
git clone git@github.com:phenopolis/phenopolis.git
If you wish to download the Exomiser stand-alone server, please get in touch with Julius Jacobsen.
First make sure mongoDB is running:
DBPATH=<path to db>
mongod --dbpath $DBPATH --port 27017 --smallfiles
The variants found in the VCF files are processed with Variant Effect Predictor (VEP) and the output is written to JSON standard output.
The standard output is piped into another python script, VEP/postprocess_VEP_json.py
, which adds further annotation, formatting and writes output to JSON, which is then imported with mongoimport into the variants collection.
The bash command to run the VEP, assuming your variant files are in VCF format:
bash VEP/runVEP.sh --input <infile> --output <outfile>
Importing of the variants can then be done:
mongoimport --db $DBNAME --collection variants --host $HOST < <infile>
Load individual for individual page (this is currently tedious, we are going to streamline this):
python views/load_individual.py --individual $ID --auth Admin:$PASSWORD
The pubmedscore, written by Jing Yu, scores genes based on their pubmed relevance.
The scripts can be found in pubmedScore:
Before running the script, it is preferable to write patients ids in patients.txt
, which pubmedScore.py
takes by default.
python pubmedScore.py
-i patients.txt
-g ABCA4 (if specified, will ignore -i and -p)
-p patientID_1 (if specified, will ignore -i)
-k retina,retinal,retinitis,blindness,macula,macular,stargardt,pigmentosa (Keywords to search on pubmed. Displayed is default)
The phenogenon, written by Jing Yu, does an enrichment test (Fisher test) per gene and HPO term.
The scripts can be found in phenogenon:
First, the user has to run python snapshot_patient_hpo.py
to take a snapshot of patients' HPO at the time. Since the phenogenon analysis will take some time, this is to avoid any inconsistency that might be introduced by editing patients' HPO in the database when phenogenon is running.
Second, python get_hpo_freq.py
will produce an HPO frequency file that phenogenon will use for its analysis.
Phenogenon can then be run as python gene_hpo_analysis --chrom X
per chromosome. This feature can be utilised to parallelise the jobs on chromosomes. It uses ExAC_freq
and CADD_phred
scores to help filter the variants. The defaults are ExAC_freq <= 0.01 and CADD_phred >= 15
for recessive inheritance mode, and ExAC_freq <= 0.001 and CADD_phred >= 15
for dominant inheritance mode. It will produce a JSON file for each gene.
If one wishes to change the cutoffs to filter the variants after phenogenon is done, one can use python recalculate_p.py --chrom X
to do the job quickly, without having to re-extracting info using the slow gene_hpo_analysis.py
After this, python hpo_gene_anlaysis.py
will extract all genes with significant p values for each valid HPO term, and write to a JSON file for each HPO term.
Run Exomiser standalone:
EXOMISER_DATA=
cd $EXOMISER_DATA
wget ftp://ftp.sanger.ac.uk/pub/resources/software/exomiser/downloads/exomiser/exomiser-cli-7.2.1-data.zip
unzip exomiser-cli-7.2.1-data.zip
java -jar exomiser-rest-prioritiser-7.3.0-SNAPSHOT.jar --exomiser.data-directory=$EXOMISER_DATA
There is also an online version available at the Monarch Initiative.
https://monarch-exomiser-prod.monarchinitiative.org/exomiser/api/prioritise/
Run Phenopolis:
cd phenopolis
python runserver.py
This code was originally forked from the ExAC browser but has since diverged considerably.