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Spontaneous mutations in fruit‐specific carotenoid biosynthetic genes of tomato (Solanum lycopersicum) have led to improved understanding of ripening‐associated carotenogenesis. Here, we confirm thatZDSis encoded by a single gene in tomato transcriptionally regulated by ripening transcription factors RIN, NOR and ethylene. Manipulation of ZDS was achieved through transgenic repression and heterologous over‐expression in tomato. CaMV 35S‐driven RNAi repression inhibited carotenoid biosynthesis in all aerial tissues examined resulting in elevated levels ofζ‐carotene isomers and upstream carotenoids, while downstreamall trans‐lycopene and subsequent photoprotective carotenes and xanthophylls were diminished. Consequently, immature fruit displayed photo‐bleaching consistent with reduced levels of the photoprotective carotenes and developmental phenotypes related to a reduction in the carotenoid‐derived phytohormone abscisic acid (ABA).ZDS‐repressed ripe fruit was devoid of the characteristic red carotenoid,all trans‐lycopene and displayed brilliant yellow pigmentation due to elevated 9,9′di‐cis‐ζ‐carotene. Over‐expression of theArabidopsis thaliana ZDS(AtZDS) gene bypassed endogenous co‐suppression and revealed ZDS as an additional bottleneck in ripening‐associated carotenogenesis of tomato. Quantitation of carotenoids in addition to multiple ripening parameters in ZDS‐altered lines and ABA‐deficient fruit‐specific carotenoid mutants was used to separate phenotypic consequences of ABA from other effects of ZDS manipulation and reveal a unique and dynamicζ‐carotene isomer profile in ripe fruit.

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non‐homology gene features. Among the eight supervised classification algorithms evaluated, random‐forest‐based prediction consistently provided the most accurate gene function prediction. Non‐homology‐based functional annotation provides complementary strengths to homology‐based annotation, with higher average performance in Biological Process GO terms, the domain where homology‐based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology‐based functional annotation is highest. GO prediction models trained with homology‐based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non‐homology‐based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology‐based functional annotations.