Beacon 2 RI tools v2.0

This repository contains the new Beacon ri tools v2.0, a software created with the main goal of generating BFF data from .csv or .vcf (and probably more types of datafiles in the future). This is based on the first beacon ri tools, a previous and different version that you can find here: Beacon ri tools v1. The new features for beacon v2.0 are:

• Code Language is written in Python 3.11
• The output gain is schemas that suit the very last version of Beacon v2 specifications, and ready to be deployed in a beacon v2 API compliant.
• This version raises exceptions that serve as a guide for users to know how to fill data correctly into the datasheets, so the final datafiles are correct and compliant with specifications.
• All the possible combinations of docs that are compliant with specifications can be generated, for example, writing a variation either in LegacyVariation, MolecularVariation or SystemicVariation.

Data conversion process

The main goal of Beacon ri tools v2.0 is to obtain a BFF (json following Beacon v2 official specifications) file that can be injected to a beacon v2 mongoDB database. To obtain a beacon v2 with its mongodb and see how to inject these BFF files, you can check it out and download yours for free at the official repo of Beacon v2 ri api. To get this json file, you can either convert your data from a .vcf file or from a .csv file. Please, see instruction manual to follow the right steps to do the data conversion. At the end, you will end completing one of the possible conversion processes that is shown in the next diagram:

Installation guide with docker

First of all, clone or download the repository to your computer:

git clone https://github.com/EGA-archive/beacon2-ri-tools-v2.git

To light up the container with beacon ri tools v2, execute the next command inside the root folder:

docker-compose up -d --build

Once the container is up and running you can start using beacon ri tools v2, congratulations!

Instruction manual

Setting configuration and csv file

To start using beacon ri tools v2, you have to edit the configuration file conf.py that you will find inside conf. Inside this file you will find the next information:

#### Input and Output files config parameters ####
csv_folder = './csv/examples/'
output_docs_folder='./output_docs/'

#### VCF Conversion config parameters ####
allele_frequency=1 # introduce float number, leave 1 if you want to convert all the variants
reference_genome='GRCh38' # Choose one between NCBI36, GRCh37, GRCh38
datasetId='coadread_tcga_pan_can_atlas_2018'
case_level_data=False
num_rows=7000000

Please, remember to make the datasetId match the id for your datasets.csv file.

Generic config parameters

The csv_folder variable sets where are all the .csv files the tool will work with. All the .csv files must follow a specific header structure. You can find an example here templates. Note that any header with different column names from the ones that appear inside the files of this folder will not be read by the beacon ri tools v2. The output_docs_folder sets the folder where your final .json files will be saved once execution of beacon tools finishes. This folder should always be located within 'output_docs', and the only part of the path that can be altered is the subdirectory of 'output_docs'.

VCF conversion config parameters

The reference_genome is the reference genome the tool will use to map the position of the chromosomes. Make sure to select the same version as the one used to generate your data. The allele_frequency let's you set a threshold for the allele frequency of the variants you want to convert from the vcf file. The datasetId needs to match the id of your datasets.csv or datasets.json file. This will add a datasetId field in every record to match the record with the dataset it belongs to. The case_level_data is a boolean parameter (True or False) which will relate your variants to the samples they belong to. In case you set this to true, please, read as well the case level data paragraph below. The num_rows are the aproximate calculation you expect for the total of variants in each vcf there are. Make sure this is greater than the total variants expected. It was automatically calculated before but it was very slow sometimes to calculate all the variants number in a VCF.

Case Level Data conversion

If you are converting with the paramater case_level_data to True, this will add data into two collections: targets and caseLevelData. If you need to export the variants to insert them in another mongoDB, you will need to export these two collections as well, by executing the next commands:

docker exec ri-tools-mongo mongoexport --jsonArray --uri "mongodb://root:example@127.0.0.1:27017/beacon?authSource=admin" --collection caseLevelData > caseLevelData.json

docker exec ri-tools-mongo mongoexport --jsonArray --uri "mongodb://root:example@127.0.0.1:27017/beacon?authSource=admin" --collection targets > targets.json

Converting data from .vcf.gz file

To convert data from .vcf.gz to .json you will need to copy all the .vcf.gz files you want to convert inside the files_to_read folder.

docker exec -it ri-tools python genomicVariations_vcf.py

After that, if needed, export your documents from mongoDB to a .json file using one of these two possible commands. First option will delete "_id" entries generated by mongoDB:

docker exec ri-tools-mongo mongoexport --jsonArray --uri "mongodb://root:example@127.0.0.1:27017/beacon?authSource=admin" --collection genomicVariations | sed '/"_id":/s/"_id":[^,]*,//g' > genomicVariations.json

The second option, our recommended one, will keep the "_id" entries generated by mongoDB:

docker exec ri-tools-mongo mongoexport --jsonArray --uri "mongodb://root:example@127.0.0.1:27017/beacon?authSource=admin" --collection genomicVariations > genomicVariations.json

This will generate the final .json file which is in Beacon Friendly Format (BFF). Bear in mind that this time, the file will be saved in the directory you are located, so if you want to save it in the output_docs folder, add it in the path of the mongoexport.

Creating the .csv file (if metadata or not having a vcf file for genomicVariations)

If you want to convert metadata into BFF or fill a genomicVariations csv to convert it to json (BFF), you will have to create a .csv file writing the records to the correct column, the name of the column in the header indicates the field of the schema that this data will be placed in. Every new row will be appended to the final output file as a new and independent document. Fill in the csv file, following the next rules:

• If you want to write data that needs to be appended in the same document, please write data separated with |, for example if you need to write an id, e.g. HG00001|HG00002 then respect this order for their correlatives in the same document, as for the label of this id, e.g. labelforHG00001|labelforHG00002.
• As the info field for each collection is very generic and can be filled with different data, you will need to fill the column data directly with json type data. For copies and subjects for genomicVariations, json data is also needed.
• Please, respect the column structure of the files inside templates, as the script will only read the columns with the "correctly spelled" headers.
• Note that you don't have to write inside all the columns, as some of them are optionals and others are incompatible among them, as they are part of different options of the Beacon specification (an exception will raise in case a column is misfilled). We have filled an example of a .csv for each collection ready to be converted to BFF with the CINECA dataset. Please, take a look at it if you wish here.

Getting .json final documents

Before getting the .json final documents, please make sure your conf.py, that you will find inside conf, is reading the right .csv document(s) and execute the next bash script from the root folder in your terminal. All .csv files contained in the specified csv_folder will be transformed into .json:

docker exec -it ri-tools python convert_csvTObff.py

The final generated .json files, which are Beacon Friendly Format, will be in the output_docs folder with the name of the collection followed by .json extension, e.g. genomicVariations.json.

These BFF jsons will be used to populate a mongoDB for beacon usage. To know how to import in a Beacon v2, please do as described in Beacon v2 ri api.

Version notes

• Other file names and distribution of folder and files is not supported.

Acknowledgements

Thanks to all the EGA archive team, and specially:

• Jordi Rambla, for guiding, supporting, helping and making possible the development of this tool.