Convolutional neural network modelling: advancing identification of true mRNA cleavage sites
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ABSTRACT:
PROVIDER: PRJNA685865 | ENA |
REPOSITORIES: ENA
Dataset's files
| 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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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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