Bridging the Annotation Gap in Non-model Plant Species
A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Molecular Biology".
Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 638
Special Issue Editors
Interests: Gene Regulation; Gene Network Inference; Machine Learning
Interests: Gene Annotation; Semantic similarity; Gene Annotation Network
Interests: functional genomics; plant systems biology; plant stress responses; plant-microbial homeostasis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear colleagues,
The field of plant biology, especially in its research conducted on non-model organisms, is facing a major limitation. While sequencing technologies allow for deciphering the genomic blueprint of almost any species at an unprecedented pace, the annotation of their genic content is severely lacking. Every plant genome project, whether for crops or trees, infers gene annotation electronically, and makes use of the only reliable, scientifically curated, and sufficiently comprehensive information we have in the plant world, that of the model organism Arabidopsis thaliana. While this is helpful, it is insufficient. Replicating the effort conducted in A. thaliana to validate in vivo novel gene functions is not an option for every (or any, for that matter) non-model plants being sequenced. But there are other avenues that have not yet received enough light. These, such as the use of gene network inference in combination with, for example, similar-to-annotate gene functions based on their co-expression neighbourhood have huge potential to bridge the annotation gap and benefit the plant community as a whole. The aim of this Special Issue is to shed light on any novel approaches that can help in that prospect.
Dr. Nicolas Delhomme
Dr. Aarón Ayllon-Benitez
Dr. Gabriella Sferra
Guest Editors
Manuscript Submission Information
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Keywords
- Gene annotation
- Non-model plant organisms
- Gene network inference
- Machine learning
