A prototype of the Genotype to phenotype user interface exists here.
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For researchers, who need to investigate genotype phenotype associations, smmart-g2p is a search tool that aggregates evidence from several knowledge bases unlike ad-hoc searches, the product allows the researcher to focus on the evidence, not on the search. more
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Quickly determine the diseases, drugs and outcomes based on evidence from trusted sources. Find relevant articles and (soon) drug response data.
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Inform GA4GH G2P discussions
Now:
- Jackson Lab Clinical Knowledge Base
- Washington University CIViC
- oncokb Precision Oncology Knowledge Base
- Cancer Genome Interpreter Cancer bioMarkers database
- GA4GH reference server
- Cornell pmkb
In progress:
JUST GOOGLE IT:
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Use the search box like a google search. To search your data, enter your search criteria in the Query bar and press Enter or click Search to submit the request. For a full explanation of the search capabilities see here
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The charts and list are all tied to the search. Click to constrain your results
- Start up an elastic search container
- Register and download CosmicMutantExport.csv into the harvester directory
- Make the required files from the harvester Makefile
$ cd harvester
$ make oncokb_all_actionable_variants.tsv cgi_biomarkers_per_variant.tsv cosmic_lookup_table.tsv cgi_mut_benchmarking.tsv oncokb_mut_benchmarking.tsv benchmark_results.txt
- Install required python packages
pip install -r requirements.txt
- Run the harvester
$ python harvester.py -h
usage: harvester.py [-h] [--elastic_search ELASTIC_SEARCH]
[--elastic_index ELASTIC_INDEX] [--delete_index]
[--delete_source]
[--harvesters HARVESTERS [HARVESTERS ...]]
optional arguments:
-h, --help show this help message and exit
--elastic_search ELASTIC_SEARCH, -es ELASTIC_SEARCH
elastic search endpoint
--elastic_index ELASTIC_INDEX, -i ELASTIC_INDEX
elastic search index
--delete_index, -d delete elastic search index
--delete_source, -ds delete all content for source before harvest
--harvesters HARVESTERS [HARVESTERS ...]
harvest from these sources. default: ['cgi_biomarkers', 'jax', 'civic', 'oncokb', 'g2p']
A harvester is a python module that implements this duck typing interface.
#!/usr/bin/python
def harvest(genes):
""" given a list of genes, yield an evidence item """
# for gene in genes:
# gene_data = your_implementation_goes_here
# yield gene_data
pass
def convert(gene_data):
""" given a gene_data in it's original form, produce a feature_association """
# gene: a string gene name
# feature: a dict representing a ga4gh feature https://github.com/ga4gh/ga4gh-schemas/blob/master/src/main/proto/ga4gh/sequence_annotations.proto#L30
# association: a dict representing a ga4gh g2p association https://github.com/ga4gh/ga4gh-schemas/blob/master/src/main/proto/ga4gh/genotype_phenotype.proto#L124
#
# feature_association = {'gene': gene ,
# 'feature': feature,
# 'association': association,
# 'source': 'my_source',
# 'my_source': {... original data from source ... }
# yield feature_association
pass
def harvest_and_convert(genes):
""" get data from your source, convert it to ga4gh and return via yield """
for gene_data in harvest(genes):
for feature_association in convert(gene_data):
yield feature_association
$ cd harvester
$ pytest -s -v
======================================================================================================================================================= test session starts ========================================================================================================================================================
platform darwin -- Python 2.7.13, pytest-3.0.7, py-1.4.33, pluggy-0.4.0 -- /usr/local/opt/python/bin/python2.7
cachedir: ../../.cache
rootdir: /Users/walsbr, inifile:
collected 13 items
tests/integration/test_elastic_silo.py::test_args PASSED
tests/integration/test_elastic_silo.py::test_init PASSED
tests/integration/test_elastic_silo.py::test_save PASSED
tests/integration/test_elastic_silo.py::test_delete_all PASSED
tests/integration/test_elastic_silo.py::test_delete_source PASSED
tests/integration/test_kafka_silo.py::test_populate_args PASSED
tests/integration/test_kafka_silo.py::test_init PASSED
tests/integration/test_kafka_silo.py::test_save PASSED
tests/integration/test_pb_deserialize.py::test_civic_pb PASSED
tests/integration/test_pb_deserialize.py::test_jax_pb PASSED
tests/integration/test_pb_deserialize.py::test_oncokb_pb PASSED
tests/integration/test_pb_deserialize.py::test_molecular_match_pb PASSED
tests/integration/test_pb_deserialize.py::test_cgi_pb PASSED
There is a docker compose configuration file in the root directory.
Launch it by:
ELASTIC_PORT=9200 KIBANA_PORT=5601 docker-compose up -d
This will automatically download elastic search etc. and will expose the standard elastic search and kibana ports (9200 and 5601)
If you would like to host an instance, launch docker-compose with an additional nginx file.
docker-compose -f docker-compose.yml -f cloud-setup/docker-compose-nginx.yml up -d
This will do the same setup, but will also include an nginx proxy to map http and https ports.
- See the README.md in harvester/tests/integration to see how harvested evidence is mapped to protocol buffer messages.
- Work with users, gather feedback
- Load alternative data sources [literome, ensemble]
- Load smmart drugs [Olaparib, Folfox, Pembrolizumab, …]
- Integrate with bmeg (machine learning evidence)
- Improve data normalization
- Variant naming (HGVS)
- Ontologies (diseases, drugs, variants)
- Add GA4GH::G2P api (or successor)
- Harden prototype:
- python notebook
- web app (deprecate kibana UI)