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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Published on: January 21, 2020

RNAirport: a deep neural network-based database characterizing representative gene models in plants.

Sitao Zhu¹, Shu Yuan¹, Ruixia Niu¹

¹State Key Laboratory of Hybrid Rice, Institute for Advanced Studies (IAS), Wuhan University, Wuhan, Hubei 430072, China.

Journal of Genetics and Genomics = Yi Chuan Xue Bao

|March 22, 2024

Summary

Researchers developed methods to identify representative 5' leaders, crucial for understanding RNA regulation. This work annotates diverse 5' untranslated regions in plants, aiding translation efficiency studies.

Keywords:

5′-leader Deep learning RNA regulatory elements Synthetic biology Transcript isoforms Translational control uORF

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Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Published on: August 5, 2020

Area of Science:

Plant molecular biology
Genomics
Bioinformatics

Background:

The 5'-untranslated region (5'-UTR), now termed 5'-leader, exhibits significant isoform diversity.
Alternative splicing (aS) and alternative transcription start sites (aTSS) generate these diverse 5'-leaders.
Identifying a representative 5'-leader is essential for studying RNA regulatory elements that control translation efficiency.

Purpose of the Study:

To develop computational tools for annotating representative 5'-leaders in five plant species.
To enable the examination of RNA regulatory elements within these 5'-leaders.
To establish a foundation for future research, such as Ribo-Seq open-reading frame annotation.

Main Methods:

A ranking algorithm based on the Kruskal-Wallis test was employed to identify representative alternative splicing-mediated 5'-leaders.
A deep-learning model, 5'leaderP, was trained to predict representative 5'-ends based on cap-analysis gene expression data, learning aTSS-mediated patterns.
Experimental validation of the 5'leaderP model's predictions was performed in Arabidopsis and rice.

Main Results:

The study successfully identified representative 5'-leaders for five plant species using the developed algorithm and deep-learning model.
The 5'leaderP model demonstrated high accuracy in predicting 5'-ends, which was experimentally confirmed.
A resource, RNAirport (http://www.rnairport.com/leader5P/), was created to provide access to these annotated gene models and the 5'leaderP tool.

Conclusions:

The developed computational approaches effectively annotate representative 5'-leaders, addressing the challenge of isoform diversity.
This work provides valuable insights into plant 5'-leader diversity and its regulatory implications.
The findings pave the way for advanced functional genomics studies, including Ribo-Seq analysis, similar to ongoing human GENCODE projects.