  Background: Creeping bentgrass (Agrostis soionifera) is a perennial grass of
Gramineae, belonging to cold season turfgrass, but has shallow adventitious
roots, poor disease-resistance. Little is known about the ISR mechanism of
turfgrass and the signal transduction involved in disease-resistance induction,
especially the function of a large number of disease-resistance related
proteins are urgent to be explored. Results: In this work, the protein
sequences of creeping bentgrass were measured and annotated by a functional
prediction model based on convolutional neural network. Creeping bentgrass
seedlings were grown with BDO treatment, and the ISR response was induced by
infecting Rhizoctonia solani. We preformed the transcriptome analysis by
Illumina Sequencing and high-quality unigenes were obtained. A minority of
assembled unigenes were functionally annotated according to the database
alignment while a large part of the obtained amino acid sequences was left
non-annotated. To treat the non-annotated sequences, a prediction model was
established by training the data set from GO families in three domains to
acquire good performance, especially the higher false positive control rate.
With such model, we analyzed the non-annotated protein sequences of creeping
bentgrass transcriptome, and annotated the disease-resistance response and
signal transduction related proteins. Conclusions: The results provide good
candidates of the proteins with certain functions. With the results in this
work, the waste of transcriptome sequencing data of creeping bentgrass can be
avoided, and research time and labor for the analysis of ISR characteristics of
creeping bentgrass will be saved in further research. It also provides
reference for the sequence analysis of turfgrass disease-resistance research.
