Aln2tbl: building a mitochondrial features table from a assembly alignment in fasta format

Abstract

The sequencing, annotation and analysis of complete mitochondrial genomes is an important research tool in phylogeny and evolution. Starting with the primary sequence, genes/features are generally annotated automatically to obtain preliminary annotations in the form of a feature table. Further manual curation in a graphic alignment editor is nevertheless necessary to revise annotations. As such, the automatically generated feature table is invalidated and has to be modified manually before submission to data banks. We developed aln2tbl.py, a python script that recreates a feature table from a manually refined alignment of genes mapped on the mitochondrial genome in fasta format. The feature table is populated with notes and annotations specific to mitochondrial genomes. The table can be used to create a sqn file to be submitted directly to data banks. In summary, our scripts fills one gap in the available toolbox and, combined with other software, allows the automation of the entire process, from primary sequence to annotated genome submission, even if a manual curation step is conducted in a visual sequence editor.

Keywords:

• Mitochondrial genome
• gene annotation
• feature table
• Python
• GenBank submission

Acknowledgements

We thanks all IMEDEA, CSIC and UIB staff for their collaboration and support to achieve the research goals of our institutions, and to Dr. Claudio Cucini for testing the scripts. We are indebted to the three anonymous reviewers which greatly improved the manuscript, the script and its usage and description in GitHub (https://github.com/IMEDEA/mitogenomics).

Disclosure statement

Authors declare that there is no relevant financial or non-financial competing interests.

Data availability statement

The Python scripts aln2tbl.py, alongside its companion mitos2aln.py, documentation and examples, is freely available on GitHub (https://github.com/IMEDEA/mitogenomics) under the license GNU GPLv3.

Additional information

Funding

This work was supported by the Ministerio de Ciencia, Innovación y Universidades under Grant PID2019-107481GA-I00, and Ministerio de Economía y Competitividad under Grant CGL2016-76164-P.