EMBL2checklists

EMBL2checklists converts EMBL or GenBank flat files of plant and fungal DNA barcoding sequences into marker-specific ENA submission checklists for deposition to the European Nucleotide Archive via the Webin system.


Key Features:

  • Automation and conversion: Converts sequences, annotations, and metadata into marker-specific ENA checklist formats in an automated process.
  • Input format support: Accepts EMBL and GenBank flat file formats as input.
  • Annotation and metadata integration: Integrates sequence annotations and sample metadata into the generated checklists.
  • Output format: Produces tab-delimited spreadsheets that conform to ENA checklist requirements and are ready for upload via Webin.
  • Implementation: Implemented as a Python package operable across platforms.
  • Post-processing: Generates editable checklist files for modification after conversion.

Scientific Applications:

  • Plant phylogenetics: Facilitates submission of plant DNA barcoding sequences to ENA to support phylogenetic and biodiversity studies.
  • Fungal metagenomics: Facilitates submission of fungal DNA barcoding sequences from metagenomic investigations to ENA for data sharing and downstream analyses.

Methodology:

Converts input files from EMBL or GenBank formats into tab-delimited spreadsheets that conform to ENA checklist requirements by extracting sequence annotations and metadata and producing marker-specific checklists ready for upload via the Webin submission system.

Topics

Details

License:
BSD-3-Clause
Maturity:
Mature
Cost:
Free of charge
Tool Type:
command-line tool, desktop application
Operating Systems:
Linux, Windows, Mac
Programming Languages:
Python
Added:
5/26/2019
Last Updated:
6/16/2020

Operations

Publications

Gruenstaeudl M, Hartmaring Y. EMBL2checklists: A Python package to facilitate the user-friendly submission of plant and fungal DNA barcoding sequences to ENA. PLOS ONE. 2019;14(1):e0210347. doi:10.1371/journal.pone.0210347. PMID:30629718. PMCID:PMC6328100.

PMID: 30629718
PMCID: PMC6328100
Funding: - Deutsche Forschungsgemeinschaft: 418670221