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AthRiboNC: Unveiling the Translational Potential of Arabidopsis ncRNAs
by Shen Yi | Chen Ming | Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China | Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
Abstract ID: 710
Event: BGRS-abstracts
Sections: [Sym 12] Section “Systems theory, big biological data analysis, ontologies and artificial intelligence”
Our database AthRiboNC addresses the underexplored area of non-coding RNA (ncRNA)-encoded peptides in plants. These regulatory molecules have recently been implicated in encoding active small peptides, critical for gene regulation yet they lack comprehensive characterization and representation in databases. To bridge this gap, the authors established AthRiboNC, a specialized database providing evidence of ncRNA-encoded peptides specific to the model plant Arabidopsis thaliana.

 

To populate AthRiboNC, ribosome profiling data (Ribo-seq) from 226 samples across various growth conditions and treatments was amassed from public repositories and mapped to the Arabidopsis genome. Sophisticated bioinformatics tools quantified expression and identified small open reading frames (sORFs) with coding potential within the ncRNAs. Further analysis employing co-expression and functional network strategies delineated the context and implications of the ncRNAs' translational products.

 

The AthRiboNC database goes beyond mere cataloging of ncRNAs; it elucidates associations with phenotypic traits and offers navigation through hub genes and functionally enriched co-expression modules. Additionally, the inbuilt BLAST tool aligns users' queries against the sORFs, granting researchers a streamlined platform for comparative and functional genomics.