pISA-tree - a data management framework for life science research projects using a standardised directory tree

Abstract

We have developed pISA-tree, a straightforward and flexible data management solution for organisation of life science project-associated research data and metadata. It enables on-the-fly creation of enriched directory tree structure (project/Investigation/Study/Assay) via a series of sequential batch files in a standardised manner based on the ISA metadata framework. The system supports reproducible research and is in accordance with the Open Science initiative and FAIR principles. Compared with similar frameworks, it does not require any systems administration and maintenance as it can be run on a personal computer or network drive. It is complemented with two R packages, pisar and seekr, where the former facilitates integration of the pISA-tree datasets into bioinformatic pipelines and the latter enables synchronisation with the FAIRDOMHub public repository using the SEEK API. Source code and detailed documentation of pISA-tree and its supporting R packages are available from https://github.com/NIB-SI/pISA-tree.

Evidence-based unification of potato gene models with the UniTato collaborative genome browser

“…In conclusion, we believe that building upon existing gene models to improve and unify them in a community-wise manner is a reasonable and transparent way to improve potato gene model annotations. The repeated creation of new genome model versions, without interlinking, is not contributing to the FAIR data paradigm (Wilkinson et al, 2016;Petek et al, 2022) and thus hinders agricultural research, including precision agriculture and food safety (Cole et al, 2018). The requirements of periodic annotation curation and incorporating experimental data and novel findings into the annotation process are inherent also to other plant species (Yandell and Ence, 2012;Kersey, 2019).…”

DataSheet_1.pdf

“…In conclusion, we believe that building upon existing gene models to improve and unify them in a community-wise manner is a reasonable and transparent way to improve potato gene model annotations. The repeated creation of new genome model versions, without interlinking, is not contributing to the FAIR data paradigm (Wilkinson et al, 2016;Petek et al, 2022) and thus hinders agricultural research, including precision agriculture and food safety (Cole et al, 2018). The requirements of periodic annotation curation and incorporating experimental data and novel findings into the annotation process are inherent also to other plant species (Yandell and Ence, 2012;Kersey, 2019).…”