A data-driven genome annotation approach for cassava

Genome annotation files play a critical role in dictating the quality of downstream analyses by providing essential predictions for gene positions and structures. These files are pivotal in decoding the complex information encoded within DNA sequences. Here, we generated experimental data resolving RNA 5'- and 3'-ends as well as full-length RNAs for cassava TME12 sticklings in ambient temperature and cold. We used these data to generate genome annotation files using the TranscriptomeReconstructoR (TR) tool. A careful comparison to high-quality genome annotations suggests that our new TR genome annotations identified additional genes, resolved the transcript boundaries more accurately and identified additional RNA isoforms. We enhanced existing cassava genome annotation files with the information from TR that maintained the different transcript models as RNA isoforms. The resultant merged annotation was subsequently utilized for comprehensive analysis. To examine the effects of genome annotation files on gene expression studies, we compared the detection of differentially expressed genes during cold using the same RNA-seq data but alternative genome annotation files. We found that our merged genome annotation that included cold-specific TR gene models identified about twice as many cold-induced genes. These data indicate that environmentally induced genes may be missing in off-the-shelf genome annotation files. In conclusion, TR offers the opportunity to enhance crop genome annotations with implications for the discovery of differentially expressed candidate genes during plant-environment interactions.