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byconaut

Deprecation of byconaut dependency for bycon Installations

Since the bycon v2.0 "Taito City" release, the byconaut project has been reduced to non-standard functionality. Importantly, "beyond Beacon services", installation support, example data and data import functions have been migrated into the bycon project itself. The byconaut project now mainly serves as a playground for temporary utilities and scripts making use of bycon functions for additional tasks.

Installation

byconaut depends on the bycon package which can be downloaded from its repository. Please see the repository and the corresponding documentation site.

While there is also a pip installation possible over pip3 install bycon this will not include the local configuration files necessary e.g. for processing the databases.

Create your own databases

Core Data

A basic setup for a Beacon compatible database - as supported by the bycon package - consists of the core data collections mirroring the Beacon default data model:

  • variants
  • analyses (which covers parameters from both Beacon analysis and run entity schemas)
  • biosamples
  • individuals

Databases are implemented in an existing MongoDB setup using utility applications contained in the importers directory by importing data from tab-delimited data files. In principle, only 2 import files are needed for inserting and updating of records: * a file for the non-variant metadata1 with specific header values, where as the absolute minimum id values for the different entities have to be provided * a file for genomic variants, again with specific headers but also containing the upstream ids for the corresponding analysis, biosample and individual

Examples

Minimal metadata file
individual_id   biosample_id    analysis_id
BRCA-patient-001 brca-001  brca-001-cnv
BRCA-patient-001 brca-001  brca-001-snv
BRCA-patient-002 brca-002  brca-002-cnv
Variant file

Further and optional procedures

  1. Create database and variants collection
  2. update the local bycon installation for your database information andlocal parameters
    • database name(s)
    • filter_definitions for parameter mapping
  3. Create metadata collections - analyses, biosamples and individuals
  4. Create statusmaps and CNV statistics for the analyses collection
    • only relevant for CNV database use cases
  5. Create the collations collection which uses filter_definitions and the corresponding values to aggregate information for query matching, term expansion ...
  6. Create frequencymaps for binned CNV data
    • relies on existence of statusmaps in analyses and collations
    • only needed for CNV data

Data maintenance scripts

Please see the helper apps documentation.

  1. Metadata in biomedical genomics is "everything but the sequence variation" ↩