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Research Article
Jun He
Nanjing Normal University
Corresponding Author
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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.
Keywords
Functional annotation, protein sequences, creeping bentgrass, convolutional neural network
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The full text of this article is available to read as a PDF.
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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A new preprint design is coming!
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This preprint is under consideration at BMC Plant Biology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Jun He
Nanjing Normal University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 04 Mar, 2021
No community comments so far
Editorial decision: Major revision
On 24 Jan, 2022
Reviews received
Received 14 Apr, 2021
Reviewers agreed
On 07 Apr, 2021
Reviewers agreed
On 09 Mar, 2021
Reviewers invited
Invitations sent on 09 Mar, 2021
Editor assigned
On 01 Mar, 2021
Editor invited
On 25 Feb, 2021
Submission checks complete
On 25 Feb, 2021
First submitted
On 22 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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.
Figure 1
Figure 2
Figure 3
The full text of this article is available to read as a PDF.
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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