gen3 2.3.2

The Gen3 SDK makes it easy to utilize functionality in Gen3 data commons.

Gen3 SDK for Python

The Gen3 PSDK for Python provides classes and functions for handling common tasks when interacting with a Gen3 commons. The API for a commons can be overwhelming, so this SDK aims to simplify communication with various microservices in a clear Python package.

The docs here contain general descriptions of the different pieces of the SDK and example scripts. For detailed API documentation, see the link below:

• Detailed API Documentation

Table of Contents

• Installation
• Quickstart Example
• Quickstart Example w/ Auth
• Available Classes
  • Gen3Auth
  • Gen3Index
  • Gen3Submission
  • Gen3Jobs
• Indexing Tools
  • Download Manifest
  • Verify Manifest
  • Indexing Manifest
• Metadata Tools
  • Ingest Manifest
  • Searching Indexd to get GUID for Metadata Ingestion
  • Manifest Merge
    • Ideal Scenario (Column to Column Match, Indexing:Metadata Manifest Rows)
    • Non-Ideal Scenario (Partial URL Matching)
  • Using Gen3 Jobs

---

Installation

To get the latest released version of the SDK:

pip install gen3

To use the latest code in this repo you can clone this and then run:

python setup.py install

Developer Note: If you want to edit this SDK and test it you can do a development install with python setup.py develop.

Quickstart Example

"""
This script will use an instance of the Gen3Index class to communicate with a Gen3
Commons indexing service to get some basic information.

The example commons we're using is an open Canine Data Commons.
"""
from gen3.index import Gen3Index

# Gen3 Commons URL
COMMONS = "https://caninedc.org/"


def main():
    index = Gen3Index(COMMONS)
    if not index.is_healthy():
        print(f"uh oh! The indexing service is not healthy in the commons {COMMONS}")
        exit()

    print("some file stats:")
    print(index.get_stats())

    print("example GUID record:")
    print(index.get(guid="afea506a-62d0-4e8e-9388-19d3c5ac52be"))


if __name__ == "__main__":
    main()

Quickstart Example w/ Auth

Some Gen3 API endpoints require authentication and special privileges to be able to use. The SDK can automate a lot of this by simply providing it with an API Key you download from the Gen3 Commons UI after logging in.

NOTE: The below script will most likely fail for you because your user doesn't have access to create in that commons. However, the example is still important because if you did have access, this would handle passing your access token to the commons API correctly.

"""
This script will use an instance of the Gen3Index class to attempt to create a
new indexed file record in the specified Gen3 Commons indexing service.

The example commons we're using is an open Canine Data Commons.
"""
from gen3.index import Gen3Index
from gen3.auth import Gen3Auth

# Gen3 Commons URL
COMMONS = "https://caninedc.org/"

# An API Key downloaded from the above commons' "Profile" page
API_KEY_FILEPATH = "credentials.json"


def main():
    auth = Gen3Auth(COMMONS, refresh_file=API_KEY_FILEPATH)
    index = Gen3Index(COMMONS, auth_provider=auth)
    if not index.is_healthy():
        print(f"uh oh! The indexing service is not healthy in the commons {COMMONS}")
        exit()

    print("trying to create new indexed file object record:\n")
    try:
        response = index.create_record(
            hashes={"md5": "ab167e49d25b488939b1ede42752458b"}, size=42, acl=["*"]
        )
    except Exception as exc:
        print(
            "\nERROR ocurred when trying to create the record, you probably don't have access."
        )


if __name__ == "__main__":
    main()

Available Classes

Gen3Auth

This contains an auth wrapper for supporting JWT based authentication with requests. The access token is generated from the refresh token and is regenerated on expiration.

Gen3Index

This is the client for interacting with the Indexd service for GUID brokering and resolution.

Gen3Submission

This is the client for interacting with the Gen3 submission service including GraphQL queries.

Gen3Jobs

This is client for interacting with Gen3's job dispatching service. A complex example script which calls a job that combines dbGaP data with indexed file objects can be seen below:

import sys
import logging
import asyncio

from gen3.index import Gen3Index
from gen3.auth import Gen3Auth
from gen3.jobs import Gen3Jobs, DBGAP_METADATA_JOB, INGEST_METADATA_JOB

# Gen3 Commons URL
COMMONS = "https://example.org/"

# An API Key downloaded from the above commons' "Profile" page
API_KEY_FILEPATH = "credentials.json"

logging.basicConfig(filename="output.log", level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))


def metadata_ingest():
    auth = Gen3Auth(COMMONS, refresh_file=API_KEY_FILEPATH)
    jobs = Gen3Jobs(COMMONS, auth_provider=auth)

    job_input = {
        "URL": "https://cdistest-public-test-bucket.s3.amazonaws.com/04_28_20_21_55_13_merged_metadata_manifest.tsv",
        "metadata_source": "dbgaptest",
    }

    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)

    job_output = loop.run_until_complete(
        jobs.async_run_job_and_wait(job_name=INGEST_METADATA_JOB, job_input=job_input)
    )
    print(job_output)


def main():
    auth = Gen3Auth(COMMONS, refresh_file=API_KEY_FILEPATH)
    jobs = Gen3Jobs(COMMONS, auth_provider=auth)

    job_input = {
        "phsid_list": "phs000920 phs000921 phs000946 phs000951 phs000954 phs000956 phs000964 phs000972 phs000974 phs000988 phs000993 phs000997 phs001024 phs001032 phs001040 phs001062 phs001143 phs001189 phs001207 phs001211 phs001215 phs001217 phs001218 phs001237 phs001293 phs001345 phs001359 phs001368 phs001387 phs001402 phs001412 phs001416",
        "indexing_manifest_url": "https://cdistest-public-test-bucket.s3.amazonaws.com/release_manifest_no_dbgap_no_sample.csv",
        "manifests_mapping_config": {
            "guid_column_name": "guid",
            "row_column_name": "submitted_sample_id",
            "indexing_manifest_column_name": "gcp_uri",
        },
        "partial_match_or_exact_match": "partial_match",
    }

    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)

    job_output = loop.run_until_complete(
        jobs.async_run_job_and_wait(job_name=DBGAP_METADATA_JOB, job_input=job_input)
    )
    print(job_output)


if __name__ == "__main__":
    metadata_ingest()

import sys
import logging
import asyncio

from gen3.auth import Gen3Auth
from gen3.jobs import Gen3Jobs, DBGAP_METADATA_JOB

# Gen3 Commons URL
COMMONS = "https://example.net/"

# An API Key downloaded from the above commons' "Profile" page
API_KEY_FILEPATH = "credentials.json"

logging.basicConfig(filename="output.log", level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))

# NOTE: The indexing_manifest_url must exist and be publically accessible
JOB_INPUT = {
    "phsid_list": "phs000956 phs000920",
    "indexing_manifest_url": "https://example.com/public_indexing_manifest.csv",
    "manifests_mapping_config": {
        "guid_column_name": "guid",
        "row_column_name": "submitted_sample_id",
        "indexing_manifest_column_name": "urls",
    },
    "partial_match_or_exact_match": "partial_match",
}


def example_async_run_job():
    auth = Gen3Auth(COMMONS, refresh_file=API_KEY_FILEPATH)
    jobs = Gen3Jobs(COMMONS, auth_provider=auth)

    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)

    job_output = loop.run_until_complete(
        jobs.async_run_job_and_wait(job_name=DBGAP_METADATA_JOB, job_input=JOB_INPUT)
    )
    print(job_output)

def example_non_async_run_job():
    auth = Gen3Auth(COMMONS, refresh_file=API_KEY_FILEPATH)
    jobs = Gen3Jobs(COMMONS, auth_provider=auth)

    is_healthy = jobs.is_healthy()
    print(is_healthy)

    version = jobs.get_version()
    print(version)

    create_job = jobs.create_job(job_name=DBGAP_METADATA_JOB, job_input=JOB_INPUT)
    print(create_job)

    status = "Running"
    while status == "Running":
        status = jobs.get_status(create_job.get("uid")).get("status")
        print(status)

    get_output = jobs.get_output(create_job.get("uid"))
    print(get_output)


if __name__ == "__main__":
    example_async_run_job()

Metadata

For interacting with Gen3's metadata service.

import sys
import logging
import asyncio

from gen3.auth import Gen3Auth
from gen3.metadata import Gen3Metadata

logging.basicConfig(filename="output.log", level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))

COMMONS = "https://{{insert-commons-here}}/"

def main():
    auth = Gen3Auth(COMMONS, refresh_file="credentials.json")
    mds = Gen3Metadata(COMMONS, auth_provider=auth)

    if mds.is_healthy():
        print(mds.get_version())

        guid = "95a41871-444c-48ae-8004-63f4ed1f0691"
        metadata = {
            "foo": "bar",
            "fizz": "buzz",
            "nested_details": {
                "key1": "value1"
            }
        }
        mds.create(guid, metadata, overwrite=True)

        guids = mds.query("nested_details.key1=value1")

        print(guids)
        # >>> ['95a41871-444c-48ae-8004-63f4ed1f0691']

if __name__ == "__main__":
    main()

Indexing Tools

Download Manifest

How to download a manifest object-manifest.csv of all file objects in indexd for a given commons:

import sys
import logging
import asyncio

from gen3.tools import indexing
from gen3.tools.indexing.verify_manifest import manifest_row_parsers

logging.basicConfig(filename="output.log", level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))

COMMONS = "https://{{insert-commons-here}}/"

def main():
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)

    loop.run_until_complete(
        indexing.async_download_object_manifest(
            COMMONS,
            output_filename="object-manifest.csv",
            num_processes=8,
            max_concurrent_requests=24,
        )
    )


if __name__ == "__main__":
    main()

The output file will contain columns guid, urls, authz, acl, md5, file_size, file_name with info populated from indexd.

Verify Manifest

How to verify the file objects in indexd against a "source of truth" manifest.

Bonus: How to override default parsing of manifest to match a different structure.

In the example below we assume a manifest named alternate-manifest.csv already exists with info of what's expected in indexd. The headers in the alternate-manifest.csv are guid, urls, authz, acl, md5, size, file_name.

NOTE: The alternate manifest headers differ rfom the default headers described above (file_size doesn't exist and should be taken from size)

import sys
import logging
import asyncio

from gen3.tools import indexing
from gen3.tools.indexing.verify_manifest import manifest_row_parsers

logging.basicConfig(filename="output.log", level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))

COMMONS = "https://{{insert-commons-here}}/"


def main():
    def _get_file_size(row):
        try:
            return int(row.get("size"))
        except Exception:
            logging.warning(f"could not convert this to an int: {row.get('size')}")
            return row.get("size")

    # override default parsers
    manifest_row_parsers["file_size"] = _get_file_size

    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)

    loop.run_until_complete(
        indexing.async_verify_object_manifest(
            COMMONS, manifest_file="alternate-manifest.csv"
        )
    )


if __name__ == "__main__":
    main()

A more complex example is below. In this example:

• The input file is a tab-separated value file (instead of default CSV)
  • Note the manifest_file_delimiter argument
• The arrays in the file are represented with Python-like list syntax
  • ex: ['DEV', 'test'] for the acl column
• We are using more Python processes (20) to speed up the verify process
  • NOTE: You need to be careful about this, as indexd itself needs to support scaling to this number of concurrent requests coming in

import sys
import logging

from gen3.tools import indexing
from gen3.tools.indexing.verify_manifest import manifest_row_parsers

logging.basicConfig(filename="output.log", level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))

COMMONS = "https://{{insert-commons-here}}/"


def main():
    def _get_file_size(row):
        try:
            return int(row.get("size"))
        except Exception:
            logging.warning(f"could not convert this to an int: {row.get('size')}")
            return row.get("size")

    def _get_acl_from_row(row):
        return [row.get("acl").strip().strip("[").strip("]").strip("'")]

    def _get_authz_from_row(row):
        return [row.get("authz").strip().strip("[").strip("]").strip("'")]

    def _get_urls_from_row(row):
        return [row.get("url").strip()]

    # override default parsers
    manifest_row_parsers["file_size"] = _get_file_size
    manifest_row_parsers["acl"] = _get_acl_from_row
    manifest_row_parsers["authz"] = _get_authz_from_row
    manifest_row_parsers["urls"] = _get_urls_from_row

    indexing.verify_object_manifest(
        COMMONS,
        manifest_file="output-manifest.csv",
        manifest_file_delimiter="\t",
        num_processes=20,
    )


if __name__ == "__main__":
    main()

Indexing Manifest

The module for indexing object files in a manifest (against indexd's API).

The manifest format can be either tsv or csv. The fields that are lists (like acl, authz, and urls) separate the values with commas or spaces (but you must use spaces if the file is a csv). The field values can contain single quote, open bracket and the closed bracket. However, they will be removed in the preprocessing step.

The following is an example of tsv manifest.

guid	md5	size	authz	acl	url
255e396f-f1f8-11e9-9a07-0a80fada099c	473d83400bc1bc9dc635e334faddf33c	363455714	/programs/DEV/project/test	['Open']	[s3://examplebucket/test1.raw]
255e396f-f1f8-11e9-9a07-0a80fada097c	473d83400bc1bc9dc635e334fadd433c	543434443	/programs/DEV/project/test	phs0001 phs0002	s3://examplebucket/test3.raw gs://examplebucket/test3.raw
255e396f-f1f8-11e9-9a07-0a80fada096c	473d83400bc1bc9dc635e334fadd433c	363455714	/programs/DEV/project/test	['phs0001', 'phs0002']	['s3://examplebucket/test4.raw', 'gs://examplebucket/test3.raw']

import sys
import logging

from gen3.auth import Gen3Auth
from gen3.tools.indexing import index_object_manifest

logging.basicConfig(filename="output.log", level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))

COMMONS = "https://{{insert-commons-here}}/"
MANIFEST = "./example_manifest.tsv"


def main():
    auth = Gen3Auth(COMMONS, refresh_file="credentials.json")

    # use basic auth for admin privileges in indexd
    # auth = ("basic_auth_username", "basic_auth_password")

    index_object_manifest(
        commons_url=COMMONS,
        manifest_file=MANIFEST,
        thread_num=8,
        auth=auth,
        replace_urls=False,
        manifest_file_delimiter="\t" # put "," if the manifest is csv file
    )

if __name__ == "__main__":
    main()

Metadata Tools

Ingest Manifest

For populating the metadata service via a file filled with metadata. Uses asynchronous calls for you.

The file provided must contain a "guid" column (or you can use a different column name or different logic entirely by providing a guid_for_row function)

The row contents can contain valid JSON and this script will correctly nest that JSON in the resulting metadata.

import sys
import logging
import asyncio

from gen3.auth import Gen3Auth
from gen3.tools import metadata
from gen3.tools.metadata.ingest_manifest import manifest_row_parsers

logging.basicConfig(filename="output.log", level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))

COMMONS = "https://{{insert-commons-here}}/"

# a file containing a "guid" column and additional, arbitrary columns to populate
# into the metadata service
MANIFEST = "dbgap_extract_guid.tsv"

def main():
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)

    auth = Gen3Auth(COMMONS, refresh_file="credentials.json")

    # must provide a str to namespace the metadata from the file in a block in
    # the metadata service
    metadata_source = "dbgap"

    # (optional) override default guid parsing behavior
    def _custom_get_guid_for_row(commons_url, row, lock):
        """
        Given a row from the manifest, return the guid to use for the metadata object.

        Args:
            commons_url (str): root domain for commons where mds lives
            row (dict): column_name:row_value
            lock (asyncio.Semaphore): semaphones used to limit ammount of concurrent http
                connections if making a call to an external service

        Returns:
            str: guid
        """
        return row.get("guid") # OR row.get("some_other_column")

    # (optional) override default guid parsing behavior
    manifest_row_parsers["guid_for_row"] = _custom_get_guid_for_row

    loop.run_until_complete(
        metadata.async_ingest_metadata_manifest(
            COMMONS, manifest_file=MANIFEST, metadata_source=metadata_source, auth=auth
        )
    )

if __name__ == "__main__":
    main()

Example file:

guid    submitted_sample_id biosample_id    dbgap_sample_id sra_sample_id   submitted_subject_id    dbgap_subject_id    consent_code    consent_short_name  sex body_site   analyte_type    sample_use  repository  dbgap_status    sra_data_details    study_accession study_accession_with_consent    study_with_consent  study_subject_id
95a41871-222c-48ae-8004-63f4ed1f0691    NWD680715   SAMN04109058    1784155 SRS1361261  DBG00391    1360750 2   HMB-IRB-MDS female  Blood   DNA ["Seq_DNA_SNP_CNV"] TOPMed_WGS_Amish    Loaded  {"status": "public", "experiments": "1", "runs": "1", "bases": "135458977924", "size_Gb": "25", "experiment_type": "WGS", "platform": "ILLUMINA", "center": "UM-TOPMed"}    phs000956.v3.p1 phs000956.v3.p1.c2  phs000956.c2    phs000956.v3_DBG00391

Would result in the following metadata records in the metadata service:

{
    _guid_type: "indexed_file_object",
    dbgap: {
        sex: "female",
        body_site: "Blood",
        repository: "TOPMed_WGS_Amish",
        sample_use: [
            "Seq_DNA_SNP_CNV"
        ],
        analyte_type: "DNA",
        biosample_id: "SAMN04109058",
        consent_code: 2,
        dbgap_status: "Loaded",
        sra_sample_id: "SRS1361261",
        dbgap_sample_id: 1784155,
        study_accession: "phs000956.v3.p1",
        dbgap_subject_id: 1360750,
        sra_data_details: {
            runs: "1",
            bases: "135458977924",
            center: "UM-TOPMed",
            status: "public",
            size_Gb: "25",
            platform: "ILLUMINA",
            experiments: "1",
            experiment_type: "WGS"
        },
        study_subject_id: "phs000956.v3_DBG00391",
        consent_short_name: "HMB-IRB-MDS",
        study_with_consent: "phs000956.c2",
        submitted_sample_id: "NWD680715",
        submitted_subject_id: "DBG00391",
        study_accession_with_consent: "phs000956.v3.p1.c2"
    }
}

NOTE: _guid_type is populated automatically, depending on if the provided GUID exists in indexd or not. Either indexed_file_object or metadata_object.

Searching Indexd to get GUID for Metadata Ingestion

It is possible to try and dynamically retrieve a GUID for a row in the manifest file provided. However, this is limited by indexd's ability to scale to the queries you want to run. Indexd's querying capabilities are limited and don't scale well with a large volume of records (it is meant to be a key:value store much like the metadata service).

WARNING: This is not recommended to be used at scale. Consider making these associations to metadata before ingestion. See merging tools

import sys
import logging
import asyncio

from gen3.auth import Gen3Auth
from gen3.tools import metadata
from gen3.tools.metadata.ingest_manifest import manifest_row_parsers

logging.basicConfig(filename="output.log", level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))

COMMONS = "https://{{insert-commons-here}}/"
MANIFEST = "dbgap_extract.tsv"


def main():
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)

    auth = Gen3Auth(COMMONS, refresh_file="credentials.json")

    # must provide a str to namespace the metadata from the file in a block in
    # the metadata service
    metadata_source = "dbgap"

    # (optional) override default indexd querying (NOTE: must be async)
    async def _custom_query_for_associated_indexd_record_guid(commons_url, row, lock, output_queue):
        """
        Given a row from the manifest, return the guid for the related indexd record.

        WARNING: The query endpoint this uses in indexd is incredibly slow when there are
                 lots of indexd records.

        Args:
            commons_url (str): root domain for commons where mds lives
            row (dict): column_name:row_value
            lock (asyncio.Semaphore): semaphones used to limit ammount of concurrent http
                connections
            output_queue (asyncio.Queue): queue for logging output

        Returns:
            str: guid or None
        """
        mapping = {"urls": "submitted_sample_id"}

        # special query endpoint for matching url patterns
        records = []
        if "urls" in mapping:
            pattern = row.get(mapping["urls"])
            logging.debug(
                f"trying to find matching record matching url pattern: {pattern}"
            )
            records = await metadata.async_query_urls_from_indexd(
                pattern, commons_url, lock
            )

        logging.debug(f"matching record(s): {records}")

        if len(records) > 1:
            msg = (
                "Multiple records were found with the given search criteria, this is assumed "
                "to be unintentional so the metadata will NOT be linked to these records:\n"
                f"{records}"
            )
            logging.warning(msg)
            records = []

        guid = None
        if len(records) == 1:
            guid = records[0].get("did")

        return guid

    # (optional) override default indexd querying
    manifest_row_parsers[
        "indexed_file_object_guid"
    ] = _custom_query_for_associated_indexd_record_guid

    loop.run_until_complete(
        # get_guid_from_file=False tells tool to try and get the guid using
        # the provided custom query function
        metadata.async_ingest_metadata_manifest(
            COMMONS,
            manifest_file=MANIFEST,
            metadata_source=metadata_source,
            auth=auth,
            get_guid_from_file=False,
        )
    )


if __name__ == "__main__":
    main()

Setting get_guid_from_file to False tells tool to try and get the guid using the provided custom query function instead of relying on a column in the manifest.

NOTE: By default, the indexed_file_object_guid function attempts to query indexd URLs to pattern match whatever is in the manifest column submitted_sample_id.

Manifest Merge

If you have a manifest full of metadata and a manifest of indexed file objects in Indexd, you can use this script to merge the two into a metadata manifest for ingestion.

For example, a common use case for this is if you have a file full of metadata from dbGaP and want to get associated GUIDs for each row. You can then add the dbGaP metadata to the metadata service for those GUIDs with the file output from this merge script.

The script is also fairly configurable depending on how you need to map between the two files.

The ideal scenario is when you can map column to column between your metadata manifest and indexing manifest (e.g. what's in indexd).

The non-ideal scenario is if you need something for partially matching one column to another. For example: if one of the indexed URLs will contain submitted_sample_id somewhere in the filename. In this case, the efficiency of the script becomes O(n^2). If you can reliably parse out the section of the URL to match that could improve this. tl;dr Depending on your logic and number of rows in both files, this could be very very slow.

By default this merge can match multiple GUIDs with the same metadata (depending on the configuration). This supports situations where there may exist metadata that applies to multiple files. For example: dbGaP sample metadata applied to both CRAM and CRAI genomic files.

So while this supports metadata matching multiple GUIDs, it does not support GUIDs matching multiple sets of metadata.

IMPORTANT NOTE: The tool will log warnings about unmatched records but it will not halt execution, so be sure to check logs when using these tools.

Ideal Scenario (Column to Column Match, Indexing:Metadata Manifest Rows)

Consider the following example files.

metadata manifest: dbGaP extract file perhaps by using this tool:

submitted_sample_id, dbgap_subject_id, consent_short_name, body_site, ....

indexing manifest (perhaps provided by the data owner):

guid, sample_id, file_size, md5, md5_hex, aws_uri, gcp_uri

The strategy here is to map from the submitted_sample_id from the metadata manifest into the sample_id and then use the guid from the indexing manifest in the final output. That final output will can be used as the ingestion file for metadata ingestion.

import sys
import logging

from gen3.tools.merge import merge_guids_into_metadata
from gen3.tools.merge import manifests_mapping_config


logging.basicConfig(filename="output.log", level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))

COMMONS = "https://{{insert-commons-here}}/"

def main():
    indexing_manifest = (
        "/path/to/indexing_manifest.csv"
    )
    metadata_manifest = (
        "/path/to/metadata_extract.tsv"
    )

    # what column to use as the final GUID for metadata (this MUST exist in the
    # indexing file)
    manifests_mapping_config["guid_column_name"] = "guid"

    # what column from the "metadata file" to use for mapping
    manifests_mapping_config["row_column_name"] = "submitted_sample_id"

    # this configuration tells the function to use the "sample_id" column
    # from the "indexing file" to map to the metadata column configured above
    # (and these should match EXACTLY, 1:1)
    manifests_mapping_config["indexing_manifest_column_name"] = "sample_id"

    output_filename = "metadata-manifest.tsv"

    merge_guids_into_metadata(
        indexing_manifest, metadata_manifest, output_filename=output_filename,
        manifests_mapping_config=manifests_mapping_config
    )

if __name__ == "__main__":
    main()

The final output file will contain all the columns from the metadata manifest in addition to a new GUID column which maps to indexed records.

output manifest (to be used in metadata ingestion):

guid, submitted_sample_id, dbgap_subject_id, consent_short_name, body_site, ....

Non-Ideal Scenario (Partial URL Matching)

Consider the following example files.

metadata manifest: dbGaP extract file perhaps by using this tool:

submitted_sample_id, dbgap_subject_id, consent_short_name, body_site, ....

indexing manifest (perhaps by using the download manifest tool):

guid, urls, authz, acl, md5, file_size, file_name

NOTE: The indexing manifest contains no exact column match to the metadata manifest.

The strategy here is to look for partial matches of the metadata manifest's submitted_sample_id in the indexing manifest's urls field.

import sys
import logging

from gen3.tools.merge import (
    merge_guids_into_metadata,
    manifest_row_parsers,
    manifests_mapping_config,
    get_guids_for_manifest_row_partial_match,
)


logging.basicConfig(filename="output.log", level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))

COMMONS = "https://{{insert-commons-here}}/"


def main():
    indexing_manifest = (
        "/path/to/indexing_manifest.csv"
    )
    metadata_manifest = (
        "/path/to/metadata_extract.tsv"
    )
    # what column to use as the final GUID for metadata (this MUST exist in the
    # indexing file)
    manifests_mapping_config["guid_column_name"] = "guid"

    # what column from the "metadata file" to use for mapping
    manifests_mapping_config["row_column_name"] = "submitted_sample_id"

    # this configuration tells the function to use the "gcp_uri" column
    # from the "indexing file" to map to the metadata column configured above
    # (for partial matching the metdata data column to this column )
    manifests_mapping_config["indexing_manifest_column_name"] = "urls"

    # by default, the functions for parsing the manifests and rows assumes a 1:1
    # mapping. There is an additional function provided for partial string matching
    # which we can use here.
    manifest_row_parsers["guids_for_manifest_row"] = get_guids_for_manifest_row_partial_match

    output_filename = "metadata-manifest-partial.tsv"

    merge_guids_into_metadata(
        indexing_manifest=indexing_manifest,
        metadata_manifest=metadata_manifest,
        output_filename=output_filename,
        manifests_mapping_config=manifests_mapping_config,
        manifest_row_parsers=manifest_row_parsers,
    )


if __name__ == "__main__":
    main()

WARNING: The efficiency here is O(n2) so this does not scale well with large files.

The final output file will contain all the columns from the metadata manifest in addition to a new GUID column which maps to indexed records.

output manifest (to be used in metadata ingestion):

guid, submitted_sample_id, dbgap_subject_id, consent_short_name, body_site, ....

Using Gen3 Jobs

There are some Gen3 jobs that were tailored for metadata ingestions and getting metadata from a public dbGaP API. The following are some example scripts that could be useful for utilizing those new jobs:

NOTE: All of these jobs require specific permissions in the Gen3 environment

import sys
import logging
import asyncio

from gen3.index import Gen3Index
from gen3.auth import Gen3Auth
from gen3.jobs import Gen3Jobs, DBGAP_METADATA_JOB, INGEST_METADATA_JOB

# Gen3 Commons URL
COMMONS = "https://example.net/"

# An API Key downloaded from the above commons' "Profile" page
API_KEY_FILEPATH = "credentials.json"

logging.basicConfig(filename="output.log", level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))

def get_dbgap_merged_metadata_manifest():
    auth = Gen3Auth(COMMONS, refresh_file=API_KEY_FILEPATH)
    jobs = Gen3Jobs(COMMONS, auth_provider=auth)

    # this configuration tells the job to pull sample information from the public dbgap
    # api for the list of dbgap phsids (AKA study accession numbers) provided.
    #
    # The indexing_manifest_url is a publically available indexing manifest with at
    # a minimum columns to represent the GUID and some other field we can map to
    # a field from dbgap, in this example, we're doing a partial string match of
    # "submitted_sample_id" from dbgap to the indexing manifest's "urls" column
    #
    # If there is an exact match available, you can set "partial_match_or_exact_match"
    # to "exact_match" and this will perform the merging MUCH faster
    job_input = {
        "phsid_list": "phs000920 phs000921 phs000946 phs000951 phs000954 phs000956 phs000964 phs000972 phs000974 phs000988 phs000993 phs000997 phs001024 phs001032 phs001040 phs001062 phs001143 phs001189 phs001207 phs001211 phs001215 phs001217 phs001218 phs001237 phs001293 phs001345 phs001359 phs001368 phs001387 phs001402 phs001412 phs001416",
        "indexing_manifest_url": "https://example-test-bucket.s3.amazonaws.com/indexing_manifest_with_guids.csv",
        "manifests_mapping_config": {
            "guid_column_name": "guid",
            "row_column_name": "submitted_sample_id",
            "indexing_manifest_column_name": "urls",
        },
        "partial_match_or_exact_match": "partial_match",
    }

    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)

    job_output = loop.run_until_complete(
        jobs.async_run_job_and_wait(job_name=DBGAP_METADATA_JOB, job_input=job_input)
    )

    # output contains signed URLs to download the new merged metadata manifest
    print(job_output)


def metadata_ingest():
    auth = Gen3Auth(COMMONS, refresh_file=API_KEY_FILEPATH)
    jobs = Gen3Jobs(COMMONS, auth_provider=auth)

    # provide a URL for a manifest that contains a GUID column along with arbitrary
    # other columns to add to the metadata service. The "metadata_source" namespaces
    # this data in the metadata service to support multiple different sources of metadata
    #
    # For example, this will create a metadata blob like:
    # {"dbgap": {"colA": "valA", "colB": valB}}
    job_input = {
        "URL": "https://example-bucket/merged_metadata_manifest.tsv",
        "metadata_source": "dbgap",
    }

    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)

    job_output = loop.run_until_complete(
        jobs.async_run_job_and_wait(job_name=INGEST_METADATA_JOB, job_input=job_input)
    )
    print(job_output)


if __name__ == "__main__":
    get_dbgap_merged_metadata_manifest()

    # TODO: QA the manifest from the above step, make it available to the next job for
    #       actual ingestion into the metadat service

    metadata_ingest()

Changelog

0.1.0

Initial release Functionality for IndexClient, and Submission client