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Description:

accession 4.8.4

accession is a Python module and command line tool for submitting genomics pipeline analysis output files and metadata to the ENCODE Portal.

Installation
Note: installation requires Python >= 3.8
$ pip install accession
Next, provide your API keys from the ENCODE portal:
$ export DCC_API_KEY=XXXXXXXX
$ export DCC_SECRET_KEY=yyyyyyyyyyy

It is highly recommended to set the DCC_LAB and DCC_AWARD environment variables for
ease of use. These correspond to the lab and award identifiers given by the ENCODE
portal, e.g. /labs/foo/ and U00HG123456, respectively.

$ export DCC_LAB=XXXXXXXX
$ export DCC_AWARD=yyyyyyyyyyy

If you are accessioning workflows produced using the
Caper local backend, then installation is
complete. However, if using WDL metadata from pipeline runs on Google Cloud, you will
also need to authenticate with Google Cloud. Run the following two commands and follow
the prompts:

$ gcloud auth login --no-launch-browser
$ gcloud auth application-default login --no-launch-browser

If you would like to be able to pass Caper workflow IDs or labels you will
need to configure access to the Caper server. If you are invoking accession from
a machine where you already have a Caper set up, and you have the Caper configuration
file available at ~/.caper/default.conf, then there is no extra setup required.
If the Caper server is on another machine, you will need so configure HTTP access to
it by setting the hostname and port values in the Caper conf file.

(Optional) Finally, to enable using Cloud Tasks to upload files from Google Cloud
Storage to AWS S3, set the following two environment variables. If one or more of them
is not set, then files will be uploaded using the same machine that the accessioning
code is run from. For more information on how to set up Cloud Tasks and the upload
service, see the docs for the gcs-s3-transfer-service

$ export ACCESSION_CLOUD_TASKS_QUEUE_NAME=my-queue
$ export ACCESSION_CLOUD_TASKS_QUEUE_REGION=us-west1

To accession workflows produced on AWS backend you will need to set up AWS
credentials. The easiest way to do this is to install the AWS CLI and run
aws configure

Usage
$ accession -m metadata.json \
-p mirna \
-s dev
Please see the docs for greater detail on these input parameters.

Deploying on Google Cloud

First authenticate with Google Cloud via gcloud auth login if needed. Then install
the API client with pip install google-api-python-client, it is recommended to do
this inside of a venv. Finally, create the firewall rule and deploy the instance by
running python deploy.py –project $PROJECT. This will also install the accession
package. Finally, SSH onto the new instance and run gcloud auth login to
authenticate on the instance.

For Caper integration, once the instance is up, SSH onto it and create the Caper conf
file at ~/.caper/default.conf, use the private IP of the Caper VM instance as the
hostname and use 8000 for the port. For the connection to work the Caper VM
will need to have the tag caper-server. Also note that the deployment assumes the
Cromwell server port is set to 8000.

AWS Notes
To enable S3 to S3 copy from the pipeline buckets to the ENCODE buckets, ensure that the
pipeline bucket policy grants read access to the ENCODE account. Here is an example
policy:
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "DelegateS3AccessGet",
"Effect": "Allow",
"Principal": {
"AWS": [
"arn:aws:iam::618537831167:root",
"arn:aws:iam::159877419961:root"
]
},
"Action": "s3:GetObject",
"Resource": "arn:aws:s3:::PIPELINE-BUCKET/*"
},
{
"Sid": "DelegateS3AccessList",
"Effect": "Allow",
"Principal": {
"AWS": [
"arn:aws:iam::618537831167:root",
"arn:aws:iam::159877419961:root"
]
},
"Action": "s3:ListBucket",
"Resource": "arn:aws:s3:::PIPELINE-BUCKET"
}
]
}

Project Information
accession is released under the MIT license, documentation lives in readthedocs, code is hosted on github and the releases on PyPI.