Skip to main content

Map MaveDB scoresets to VRS objects

Project description

dcd-map: Map MaveDB data to computable and interoperable variant objects

image image image Actions status DOI

This library implements a novel method for mapping MaveDB scoreset data to GA4GH Variation Representation Specification (VRS) objects, enhancing interoperability for genomic medicine applications. See Arbesfeld et. al. (2023) for a preprint edition of the mapping manuscript, or download the resulting mappings directly.

Prerequisites

  • Universal Transcript Archive (UTA): see README for setup instructions. Users with access to Docker on their local devices can use the available Docker image; otherwise, start a relatively recent (version 14+) PostgreSQL instance and add data from the available database dump.
  • SeqRepo: see README for setup instructions. The SeqRepo data directory must be writeable; see specific instructions here for more.
  • Gene Normalizer: see documentation for data setup instructions.
  • blat: Must be available on the local PATH and executable by the user. Otherwise, its location can be set manually with the BLAT_BIN_PATH env var. See the UCSC Genome Browser FAQ for download instructions.

Installation

Install from PyPI:

python3 -m pip install dcd-mapping

Usage

Use the dcd-map command with a scoreset URN, eg

$ dcd-map urn:mavedb:00000083-c-1

Output is saved in the format <URN>_mapping_results_<ISO datetime>.json in the directory specified by the environment variable MAVEDB_STORAGE_DIR, or ~/.local/share/dcd-mapping by default.

Use dcd-map --help to see other available options.

Notebooks

Notebooks for manuscript data analysis and figure generation are provided within notebooks/analysis. See notebooks/analysis/README.md for more information.

Development

Clone the repo

git clone https://github.com/ave-dcd/dcd_mapping
cd dcd_mapping

Create and activate a virtual environment

python3 -m virtualenv venv
source venv/bin/activate

Install as editable and with developer dependencies

python3 -m pip install -e '.[dev,tests]'

Add pre-commit hooks

pre-commit install

Run tests with pytest

pytest

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dcd_mapping-0.1.3.tar.gz (3.2 MB view details)

Uploaded Source

Built Distribution

dcd_mapping-0.1.3-py3-none-any.whl (38.7 kB view details)

Uploaded Python 3

File details

Details for the file dcd_mapping-0.1.3.tar.gz.

File metadata

  • Download URL: dcd_mapping-0.1.3.tar.gz
  • Upload date:
  • Size: 3.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.0 CPython/3.12.4

File hashes

Hashes for dcd_mapping-0.1.3.tar.gz
Algorithm Hash digest
SHA256 843df3e66630691a1827a98bda47db00a99b9e26c5f50bc5a8084393e2b13bc9
MD5 98fcc41400e1114a82aac148ae98387b
BLAKE2b-256 28e3aa16e1ba46bf70b58a515c9777fb2bd0f1dc66dafc8c14b66e0c632f2779

See more details on using hashes here.

File details

Details for the file dcd_mapping-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: dcd_mapping-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 38.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.0 CPython/3.12.4

File hashes

Hashes for dcd_mapping-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 9c7a3c49e39b1341fbd55f07294da74004a418ac6a710aebe06a4d564d81fa97
MD5 f9bdb489e1a216541070d668608d1889
BLAKE2b-256 78c75b3ca4d4e240d7e129eb5afffb56e23f62294099d366af842e72398c557d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page