Tutorial (Data "beaconization")
If your data is already in json format (the bff), you can go directly to STEP 4).
In this tutorial it is expected to already have the Data ingestion tools and Beacon V2 REST API downloaded, installed and set up following the instructions in Download & Installation.
Let's start by the simplest case. Imagine that you have:
- Metadata (including phenotypic data) in your system, labelled according to your internal nomenclature.
- A VCF file.
Connect to the container
The beacon container is running in detached mode or in the background. To connect, you should invoke from your terminal:
docker exec -ti beacon2-ri-tools bash
If you need to copy your data inside the beacon2-ri-tools container you can use the next command outside the container or create a mounted volume between the container and the host machine.
docker cp your_data.vcf.gz beacon2-ri-tools:/your_data.vcf.gz
container_name, is the id of the container itself. To know it you can type docker ps and look for the name of the container. This tutorial could be different depending on the method of installation (method 1: deploy_beacon-ri-tools_1 / method 2: beacon-ri-tools).
To see all the running container names, execute the next command:
docker ps -a
Now you should be inside the beacon2-ri-tools container to start the Data beaconization.
First, we are going to convert your metadata (sequencing methodology, bioinformatics tools, phenotypic data, etc.) to the format of the Beacon v2 Models. As input, we will be using this XLSX template.
About the XLSX template
The XLSX template consists of seven sheets that match the Beacon v2 Models.
The template has the purpose of facilitating users' transformation of the data (likely in tabular form) to the hierarchical structure that we have in the Beacon v2 Models.
The header nomenclature gives a hint about if the data will be later stored as an
object (naming contains
.) or as an
array (naming contains
Each column has its own format (e.g., string, date, CURIE). These formats can browsed in the documentation.
We recommend using the provided XLSX for the synthetic data as a reference.
The first thing that needs to be done is to map/convert your metadata so that it follows the syntax of the provided XLSX file.
Normally, people don't fill out the sheet (tab) named
genomicVariations as this info will be taken from the annotated VCF (see STEP 2).
Once you have filled the Excel file then you can proceed to validate it. At this stage, it's normal that you have doubts regarding your mapping to Beacon v2 syntax. Fortunately, B2RI's utility
bff-validator will help you complete this task. The validator checks that all values in the XLSX match the specifications present in the Beacon v2 Models default schemas. In technical terms, it's called validating data against JSON Schemas.
./utils/bff_validator/bff-validator -i your_xlsx_file.xlsx --out-dir my_bff_dir
When you run it, it's very likely that you'll find have errors/warnings on your data. The script will catch them and explain the cause of the error. Please address all the issues at the XLSX. You can run the script as many times you want :-)
Unicode characters are allowed as values for the cells. However, if you are copy-pasting from other sources, sometimes "strange" characters are randomly introduced in places where they should not be. If
bff-validator is giving you errors and you can't figure out how to solve them use the flag
--ignore-validation and take a look to the JSON files created. Once you spot the error(s), please fix the original Excel file and re-run the validation without the flag. See extended information here.
At some point you won't get any errors. By now the script should have created 6 text files (what we call the Beacon Friendly Format). The files are in JSON format (JSON arrays) and will be used later (STEP 3).
Congratulations! Now you can go to STEP 2.
Now that you have processed the metadata, it's time to process the VCF file.
About VCF types
Currently, the B2RI only handles VCFs coming from DNAseq experiments (WES, WGS, gene panels, etc.). The VCFs can be single or multisample.
At the time of writting this, structural variants in VCF are not being parsed (there is scout working group currently developing Beacon v2 specifications for structural variants). We hope to implement this feature in future versions.
The VCF file has to be gzipped (or bgzipped). What we are going to do it's to annotate it (or re-annotate it if your file already has annotations) with SnpEff and SnpSift and transform the format so that it becames the 7th BFF file (i.e.,
./beacon vcf -n 1 -i input.vcf.gz -p param_file | | | | | exe mode #cores <vcf> parameters file (optional)
Here we are using
beacon script in mode vcf. This mode is one of the three available [vcf|mongodb|full].
The parameters file is optional if you want to use the default value (hg19) but it is needed if you want to change them. Note that you must provide the reference genome (unless you're using
hg19 which is the default one) that was used to create your VCF. See all the script options here.
param_file should look something like this:
Note about timing: We made the script as fast as we possibly could with a scripting language. In this regard, the processing time scales linearly with the #variants, but it's also affected by the #samples. For instance, 1M variants with 2,500 samples will take around ~20-25 min.
If something is wrong with the input files, the script will complain and provide possible solutions.
Once completed, you will end up with a dir like this one
beacon_XXXXXXX/vcf. Inside, you will find
genomicVariationsVcf.json.gz, the 7th BFF file.
About disk usage
During the annotation process, multiple intermediate VCF files are created (and kept). They're all compressed, but still they will be as big as your original VCF. On top of that,
genomicVariationsVcf.json.gz file can be huge. In summary, please allocate up to 10x times the space of your original VCF. Feel free to erase the temporary VCF files
beacon_XXXXXXX/vcf/*vcf.gz once the job is completed.
Now that you have the 7 JSON files it's time to go to the STEP 3.
The objective of this step is to load (a.k.a. ingest) the 7 JSON files into MongoDB. Once loaded in MongoDB, they are named collections.
For doing this we will use again
beacon script, but this time in mode mongodb.
Let's assume that we have the 6 files from STEP 1 in the directory
my_bff_dir and the file from STEP 2 at
We will add these values to a new parameters file:
--- bff: metadatadir: my_bff_dir # You can change the name of the JSON files runs: runs.json cohorts: cohorts.json biosamples: biosamples.json individuals: individuals.json analyses: analyses.json datasets: datasets.json # Note that genomicVariationsVcf is not affected by <metadatadir> genomicVariationsVcf: beacon_XXXXXXX/vcf/genomicVariationsVcf.json.gz
Finally, you execute this command
./beacon mongodb -p param_file | | | exe mode paramaters file.
An alternative to using
mongodb, in case you already have it installed, is using
mongoimport, which does not need a parameters file and uses the following commands:
mongoimport --jsonArray --uri "mongodb://root:firstname.lastname@example.org:27017/beacon?authSource=admin" --file analyses.json --collection analyses mongoimport --jsonArray --uri "mongodb://root:email@example.com:27017/beacon?authSource=admin" --file biosamples.json --collection biosamples mongoimport --jsonArray --uri "mongodb://root:firstname.lastname@example.org:27017/beacon?authSource=admin" --file cohorts.json --collection cohorts mongoimport --jsonArray --uri "mongodb://root:email@example.com:27017/beacon?authSource=admin" --file datasets.json --collection datasets mongoimport --jsonArray --uri "mongodb://root:firstname.lastname@example.org:27017/beacon?authSource=admin" --file individuals.json --collection individuals mongoimport --jsonArray --uri "mongodb://root:email@example.com:27017/beacon?authSource=admin" --file runs.json --collection runs mongoimport --jsonArray --uri "mongodb://root:firstname.lastname@example.org:27017/beacon?authSource=admin" --file genomicVariationsVcf.json --collection genomicVariations
If everything goes well, all your data should be loaded into an instance of MongoDB.
Congratulations! You can now go to the STEP 4.
To exit this container you just need to type "exit".
You can create the necessary indexes running the following Python script:
docker exec beacon python beacon/reindex.py
Fetch the ontologies and extract the filtering terms
This step consists of analyzing all the collections of the Mongo database for first extracting the ontology OBO files and then filling the filtering terms endpoint with the information of the data loaded in the database.
You can automatically fetch the ontologies and extract the filtering terms running the following script:
docker exec beacon python beacon/db/extract_filtering_terms.py
Get descendant and semantic similarity terms
If you have the ontologies loaded and the filtering terms extracted, you can automatically get their descendant and semantic similarity terms running the following script:
docker exec beacon python beacon/db/get_descendants.py
Start making queries with the API.
Note about MongoDB
As with any other database, it is possible to perform queries directly to MongoDB. In our case, the database is named beacon and contains the ingested collections. For doing so, you will need to use one of the many UI (we have included Mongo Express), the
mongosh or use any of the MongoDB drivers that exist for most programming languages. As an example, we have included an utility
bff-api that enables you to make simple queries to one collection at a time (see instructions here). For a more comprehensive description check MongoDB literature.