Standardization of Comparative Transcriptomic in Analyses in Plants Using Experimental Metadata andOntology Terms
Creators: name: Cardoso, Arthur Shuzo Owtake
name: Riaño-Pachón, Diego Mauricio
name: dos Santos, Renato Augusto Corrêa
Publisher: name: Zenodo
Year: 2023
Subjects: RNA-Seq
Ontology Terms
Gene Expression Markers

description: Analyzing gene expression levels in plants with stress condition contexts can discern nuances in gene regulation that provide deep insights into different biological conditions. However, to extract precise meaning from the complex RNA-Seq data sets, ontology assignment is essential to standardize comparisons, enabling more robust and integrated analyses. Thus the goals of this work are: to assign ontology terms to RNA-Seq data based on different tissues and experimental conditions; to develop Python modules for the CoNekT Grasses platform (http://conekt.cena.usp.br) using ontology terms; and to identify gene expression markers associated with stress in Setaria viridis. We downloaded RNA-Seq metadata/datasets for S. viridis from NCBI and stored the results in a SQLite database (get_metadata.py). Read mapping rates and strand-specific libraries were assessed using Salmon v1.8.0. To assign Plant Ontology (PO) and Plant Experimental Condition Ontology (PECO) terms from NCBI metadata, we developed a Python script (lemmatization.py) using library 'spaCy'. We filtered part of speech from the lemmatized words to identify plant tissues/organs and stress conditions. Another script (bioportal.py) was created to retrieve terms from ontology libraries on the BioPortal website (PO and PECO). Integration of ontology annotations into the CoNekT Grasses platform involved back-end and front-end modifications. We expanded the platform's capabilities for sample annotation input. To identify gene expression markers under stress conditions, we analyzed an expression matrix involving abiotic stress studies. This matrix was normalized by Remove Unwanted Variation (RUVg). RUVg was used for batch effect correction, library preparation, and other nuisance effects; specificity calculations were performed using the TSPEX package. Genes with TAU > 0.85 were considered highly specific, and we compared results with and without RUVg correction. We obtained SQLite databases for S. viridis containing 51 datasets associated with a publication involving abiotic stress of luminosity, heat and caffeic acid exposure. Annotation of public RNA-Seq samples was implemented in the CoNekT Grasses platform. An exploratory analysis of stress datasets revealed sample heterogeneity and batch effects, with RUVg correction improving sample grouping. Comparing specificity results between matrices with and without RUVg correction showed differences in 8110 genes, while 1834 genes were consistent in both matrices. Using TSPEX, we identified highly specific genes associated with different stress conditions, including 65 light stress, 18 heat stress, and 206 caffeic acid stress. In conclusion, our specificity calculations yielded potential gene markers of stress-associated datasets. Future work should focus on functional annotation and validation of these genes for filtering stress-related datasets in CoNekTGrasses platform and building co-expression networks.