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Convolutional neural network modelling: advancing identification of true mRNA cleavage sites

ABSTRACT: Convolutional neural network modelling: advancing identification of true mRNA cleavage sites

PROVIDER: PRJNA685865 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR13264434.fastq.gz	Fastqsanger.gz
	SRR13264435.fastq.gz	Fastqsanger.gz
	SRR13264436.fastq.gz	Fastqsanger.gz
	SRR13264437.fastq.gz	Fastqsanger.gz
	SRR13264438.fastq.gz	Fastqsanger.gz

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Convolutional neural network modelling: advancing identification of true mRNA cleavage sites

Project description:Degradome sequencing is commonly used to generate high-throughput information on mRNA cleavages by small RNAs. Here we developed an extension module based on a deep learning convolutional neural network (CNN) in a machine learning environment to discriminate false from true cleavage sites applied on datasets from potato (Solanum tuberosum, St) and the oomycete pathogen Phytophthora infestans (Pi). The core of the CNN module is a stochastic gradient descent optimizer with cyclical learning rate (CLR) which together with Bayesian optimization scored a validation accuracy of 100%. To verify the recognition of cleavage sites we applied the module on Arabidopsis thaliana microRNA cleavages. Our module managed to recognize all cleavages, confirming the reliability of the module. When applying this new model to evaluate our data, 7.3% of all cleavage windows represented true cleavages distributed as 214 sites in P. infestans and 444 sites in potato. The sRNA landscape of the potato-P. infestans interaction is complex with uneven sRNA production and cleavage regions. In total, 222 endogenous Pi-sRNAs, 565 endogenous St-sRNAs, 91 trans-acting Pi-sRNAs and 14 trans-acting St-sRNAs were discovered from our datasets. Groups of self-regulatory sRNAs, and sRNAs generated from effector sequences like RXLR and Crinklers suggest dual effector functions. In the potato genome, resistance genes were most targeted mainly as self-regulation but also as results of a trans-action event. Our new analytic model is freely accessible for anyone working on complex biological systems.

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Project description:Degradome sequencing is commonly used to generate high-throughput information on mRNA cleavage sites mediated by small RNAs (sRNA). In our datasets of potato (Solanum tuberosum, St) and Phytophthora infestans (Pi), initial predictions generated high numbers of cleavage site predictions, which highlighted the need of improved analytic tools. Here, we present an R package based on a deep learning convolutional neural network (CNN) in a machine learning environment to optimize discrimination of false from true cleavage sites. When applying smartPARE to our datasets on potato during the infection process by the late blight pathogen, 7.3% of all cleavage windows represented true cleavages distributed on 214 sites in P. infestans and 444 sites in potato. The sRNA landscape of the two organisms is complex with uneven sRNA production and cleavage regions widespread in the two genomes. Multiple targets and several cases of complex regulatory cascades, particularly in potato, was revealed. We conclude that our new analytic approach is useful for anyone working on complex biological systems and with the interest of identifying cleavage sites particularly inferred by sRNA classes beyond miRNAs.

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