/platform-input-support

Open Targets Platform input support project.

Primary LanguagePythonApache License 2.0Apache-2.0

Open Targets: Platform-input-support overview

The aim of this application is to allow the reproducibility of OpenTarget Platform data release pipeline. The input files are copied in a local hard disk and eventually in a specific google storage bucket

Currently, the application executes 11 steps and finally it generates the input resources for the ETL pipeline (https://github.com/opentargets/data_pipeline)

List of available steps:

  • BaselineExpression
  • Disease
  • Drug
  • Evidence
  • Expression
  • Go
  • Homologues
  • Interactions
  • Literature
  • Mousephenotypes
  • OpenFDA
  • Otar
  • Pharmacogenomics
  • PPPEvidence
  • Reactome
  • SO
  • Target
  • TargetEngine

Within this application you can simply download a file from FTP, HTTP or Google Cloud Bucket but at the same time the file can be processed in order to generate a new resource. The final files are located under the output directory while the files used for the computation are saved under stages.

Below more details about how to execute the script.

Running Platform Input Support

Requires Google Cloud SDK and docker (if running locally)

Step 1. Configure the run

  • Set the values in the config or copy it to profiles/config.<profile_name>
  • Note: you're unlikely to need to change anything other than the PIS Config section and maybe the image tag.
  • Set the profile to one you need e.g. make set_profile profile=dev

Step 2. Deploy and run the pipeline remotely

  • Launch with pis arguments e.g. (steps for public) so make launch_remote args='-exclude otar pppevidence'
  • This deploys a vm on GCP, runs PIS with the args given, uploads the PIS output, configs and logs to the configured GCS bucket.
  • To follow the logs while the VM is up, run the following:
    • gcloud compute ssh --zone "<pisvm.zone>" "<pisvm.username>@<pisvm.name" --project "open-targets-eu-dev" --command='journalctl -u google-startup-scripts.service -f'

Step 2 (alt). Run the pipeline locally

  • Launch with pis arguments e.g. (steps for public) so make launch_local args='-exclude otar pppevidence'
  • This runs PIS with the args given, uploads the PIS output, configs and logs to the configured GCS bucket.

Step 3. Clean up

  • If you launched PIS remotely, you'll want to tear down the infrastructure after it's finished: make clean_infrastructure. If you want to tear down all infrastructure from previous runs: make clean_all_infrastructure
  • The session configs are stored locally in "sessions/<session_id>". A helper make target allows you to clear this: make clean_session_metadata or for all sessions: make clean_all_sessions_metadata

Local installation requirements

  • Python3.8
  • Poetry
  • Apache-Jena
  • git
  • jq
  • Google Cloud SDK

Poetry for Linux/MAC

Installation:

curl -sSL https://install.python-poetry.org | python3 -

Running PIS via Docker image

Platform input support is available as docker image, a Dockerfile is provided for building the container image.

For instance

mkdir /tmp/pi
sudo docker run 
 -v path_with_credentials:/usr/src/app/cred 
 -v /tmp/pis:/usr/src/app/output 
 pis_docker_image 
 -steps Evidence 
 -gkey /usr/src/app/cred/open-targets-gac.json 
 -gb ot-team/pis_output/docker

When providing a Google Cloud key file, please, make sure that it is mounted within the container at this exact mount point

/srv/credentials/open-targets-gac.json

This allows the activation of the service account when running the container image, so gcloud-sdk can work with the destination Google Cloud Storage Bucket.

For using an external config file, simply add the option -c and the path where the config file is available

Singularity

This is an example how to run singularity using the docker image.

singularity exec \
   -B /nfs/ftp/private/otftpuser/output_pis:/usr/src/app/output \
   docker://quay.io/opentargets/platform-input-support:cm_singularity \
   conda run --no-cature-output -n pis-py3.8 python3 /usr/src/app/platform-input-support.py -steps drug

Run PIS inside EBI infrastructure

In order to run PIS inside the current EBI infrastructure the best praticse is to use Singularity and LSF.

First of all check the google cloud account rights for the proper project.

ls -la /homes/$USER/.config/gcloud/application_default_credentials.json

Eventually run the following command to generate the file above:

gcloud config set project open-targets-eu-dev
or
gcloud config set project open-targets-prod

gcloud auth application-default login

You can use singularity/ebi.sh to run PIS inside the EBI infrastructure.

 ./singularity docker_tag_image step google_storage_path

where

  • docker_tag_image: docker image tag (quay.io) (under review)
  • step : Eg. drug
  • google_storage_path: gs bucket [not mandatory]
 ./singularity/ebi.sh 21.04 drug 
 
 ./singularity/ebi.sh 21.04 drug ot-snapshots/21.04/input

Apache-Jena : Install Riot

cd ~
wget -O apache-jena.tar.gz https://www.mirrorservice.org/sites/ftp.apache.org/jena/binaries/apache-jena-4.2.0.tar.gz
tar xvf apache-jena.tar.gz --one-top-level=apache-jena --strip-components 1 -C /usr/share/

Add ~/apache-jena/bi to .bashrc
EG.
export PATH="$PATH:/your_path/apache-jena/bin"

source .bashrc

Overview of config.yaml

The config.yaml file contains several sections. Most of the sections are used by the steps in order to download, to extract and to manipulate the input and generate the proper output.

Overview of configuration file sections:

Config

The section config can be used for specify where utility programs riot and jq are installed on the system. If the command shutil.which fails during PIS execution double check these configurations.

"java_vm" parameter will set up the JVM heap environment variable for the execution of the command riot.

Data sources

The majority of the configuration file is specifying metadata for the execution of individual steps, eg tep, ensembl or drug. These can be recognised as they all have a gs_output_dir key which indicates where the files will be saved in GCP.

There is inconsistency between keys in different steps, as they accommodate very different input types and required levels of configuration. See step guides for configuration requirements for specific steps. The section config can be used for specify where riot or jq are installed.

A note on zip files

We are only building the functionality which we need which introduces some limitations. At present if a zip file is downloaded we only extract 1 file from the archive. To configure a zip file create an entry in the config such as:

  - uri: https://probeminer.icr.ac.uk/probeminer_datadump.zip
    output_filename: probeminer-datadump-{suffix}.tsv.zip
    unzip_file: true
    resource: probeminer

The unzip_file flag tells RetrieveResource.py to treat the file as an archive.

The uri field indicates from where to download the data. The archive will be saved under output_filename. The first element of the archive will be extracted under output_filename with the suffix '[gz|zip]' removed, so in this case, probeminer-datadump-{suffix}.tsv.

Set up application (first time)

git clone https://github.com/opentargets/platform-input-support
cd platform-input-support
poetry install --no-root

poetry run python3 platform-input-support.py -h

Usage

cd your_path_application
poetry run python3 platform-input-support -h
usage: platform-input-support.py [-h] [-c CONFIG]
                                 [-gkey GCP_CREDENTIALS]
                                 [-gb GCP_BUCKET] [-o OUTPUT_DIR] [-t]
                                 [-s SUFFIX] [-steps STEPS [STEPS ...]]
                                 [-exclude EXCLUDE [EXCLUDE ...]]
                                 [-l] [--log-level LOG_LEVEL]
                                 [--log-config LOG_CONFIG]

...

optional arguments:
  -h, --help            show this help message and exit
  -c CONFIG, --config CONFIG
                        path to config file (YAML) [env var: CONFIG] (default:
                        None)
  -gkey GOOGLE_CREDENTIAL_KEY, --gcp_credentials GOOGLE_CREDENTIAL_KEY
                        The path were the JSON credential file is stored. [env
                        var: GCP_CREDENTIALS] (default: None)
  -gb GCP_BUCKET, --gcp_bucket GCP_BUCKET
                        Copy the files from the output directory to a specific
                        google bucket (default: None)
  -o OUTPUT_DIR, --output OUTPUT_DIR
                        By default, the files are generated in the root
                        directory [env var: OT_OUTPUT_DIR] (default: None)
  -s SUFFIX, --suffix SUFFIX
                        The default suffix is yyyy-mm-dd [env var:
                        OT_SUFFIX_INPUT] (default: None)
  -steps STEPS [STEPS ...]
                        Run a specific list of sections of the config file. Eg
                        annotations annotations_from_buckets (default: None)
  -exclude EXCLUDE [EXCLUDE ...]
                        Exclude a specific list of sections of the config
                        file. Eg annotations annotations_from_buckets
                        (default: None)
  -l, --list_steps      List of steps callable (default: False)
  --log-level LOG_LEVEL
                        set the log level [env var: LOG_LEVEL] (default: INFO)

Using Docker to run PIS during development

If you want to run PIS in a Docker container follow these steps:

  1. Get the code git clone https://github.com/opentargets/platform-input-support
  2. create container docker build --tag <image tag> <path to Dockerfile>
  3. start container mounting the cloned code as a volume (here I assume you cloned the code into your home directory) docker run -v ~/platform-input-support:/usr/src/app --rm -it --entrypoint bash <image tag> This command will drop you into a bash shell inside the container, where you can execute the code.
  4. execute code poetry run python3 platform-input-support.py -steps <step> --log-level=DEBUG

Logging.ini

The directory "resources" contains the file logging.ini with a list of default value. If the logging.ini is not available or the user removes it than the code sets up a list of default parameters. In both case, the log output file is store under "log"

Google bucket requirements

To copy the files in a specific google storage bucket valid credentials must be used. The required parameter -gkey (--gcp_credentials) allows the specification of Google storage JSON credential. Eg.

python platform-input-support.py -gkey /path/open-targets-gac.json -gb bucket/object_path
or
python platform-input-support.py
         --gcp_credentials /path/open-targets-gac.json
         --gcp_bucket ot-snapshots/es5-sufentanil/tmp

More examples

python platform-input-support.py
         --gcp_credentials /path/open-targets-gac.json
         --gcp_bucket ot-snapshots/es5-sufentanil/tmp
or  
python platform-input-support.py
         --gcp_credentials /path/open-targets-gac.json
         --gcp_bucket ot-snapshots/es5-sufentanil/tmp
         -steps annotations evidence
         -exclude drug
or
python platform-input-support.py
         -gkey /path/open-targets-gac.json
         -gb bucket/object_path -steps drug
         --log-level DEBUG > log.txt

Check if the files generated are corrupted

The zip files generated might be corrupted. The follow command checks if the files are correct. sh check_corrupted_files.sh

Installation command for Google Cloud or Amazon Azure

Create a linux VM server and run the following commands

sudo apt update
sudo apt install git
sudo apt-get install bzip2 wget

wget https://repo.anaconda.com/archive/Anaconda3-2020.07-Linux-x86_64.sh
bash Anaconda3-2020.07-Linux-x86_64.sh
source ~/.bashrc


mkdir gitRepo
cd gitRepo
git clone https://github.com/opentargets/platform-input-support.git
cd platform-input-support
curl -sSL https://install.python-poetry.org | python3 -
poetry install --no-root
poetry run python3 platform-input-support.py -l

Use nohup to avoid that the process hang up.


Eg.
nohup poetry run python3 platform-input-support.py
         -gkey /path/open-targets-gac.json
         -gb bucket/object_path -steps drug
         --log-level DEBUG > log.txt &

Step guides

The program is broken into steps such as drug, interactions, etc. Each step can be configured as necessary in the config file and run using command line arguments.

EFO step (disease)

The EFO step is used to gather the raw data for the platform ETL.

The scope of EFO is to support the annotation, analysis and visualization of data handled by the core ontology for Open Targets.

This step downloads and manipulates the input files and it generates the following output:

  • ontology-efo-v3.xx.yy.jsonl : list of EFO terms
  • ontology-mondo.jsonl : list of MONDO terms
  • ontology-hpo.jsonl : list of HPO terms
  • hpo-phenotypes-yyyy-mm-dd.jsonl : mapping between CrossReference databaseId

Drug step

The Drug step is used to gather the raw data for the platform ETL.

ChEMBL have made an Elasticsearch instance available for querying. To keep data volumes and running times down specify the index and fields which are required in the config file.

Homology Step

This step is used to download raw json data from the ENSEMBL ftp server for specified species. These inputs are then processed with jq to extract the id and name fields which are required by the ETL. The downloaded json files approach 30GB in size, and we only extract ~10MB from them. It is worth considering if we want to retain these files long-term.

Hpa Expression Step

This step is used to download data from the internal OT resources and proteinatlas information. The tissue translation map requires some manipulations due the weird JSON format. All the files generated are required by the ETL.

OpenFDA FAERs

This step collects adversed events of interest from OpenFDA FAERS. It requires two input parameters via the configuation file:

  • URL of the events black list.
  • URL where to find OpenFDA FAERS repository metadata in JSON format.

OTAR

This step collects two kinds of information on OTAR Projects, used in the internal platform:

  1. Metadata information on the projects.
  2. Mapping Information, between the projects and diseases

Application architecture

  • platform-input-support is the entrypoint to the program; it loads the config.yaml which specifies the available steps and any necessary configuration which goes with them. This configuration is represented internally as a dictionary. Platform input support configures a RetrieveResource object and calls the run method triggering the selected steps.
  • RetrieveResource will consult the steps selected and trigger a method for each selected step. Most steps will defer to a helper object in Modules to retrieve the selected resources.

Troubleshooting

sudo apt-get install autoconf libtool