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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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RNA Secondary Structure Prediction Using High-throughput SHAPE
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A semi-supervised learning approach for RNA secondary structure prediction.

Haruka Yonemoto1, Kiyoshi Asai2, Michiaki Hamada3

  • 1Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8562, Japan.

Computational Biology and Chemistry
|March 10, 2015
PubMed
Summary

This study introduces a new semi-supervised learning method for RNA secondary structure prediction. By using both known and unknown RNA structures, this approach enhances prediction accuracy, especially when experimental data is scarce.

Keywords:
Parameter learningRNA secondary structureSemi-supervised learning

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • RNA secondary structure prediction is crucial in RNA bioinformatics.
  • Current probabilistic models rely on experimentally determined structures for training.
  • Experimental determination of RNA structures is challenging, limiting available training data.

Purpose of the Study:

  • To develop a novel semi-supervised learning approach for RNA secondary structure prediction.
  • To improve the accuracy of RNA secondary structure prediction models by incorporating unlabeled data.
  • To address the limitations posed by the scarcity of experimentally determined RNA structures.

Main Methods:

  • Implemented a hybrid model combining generative (stochastic context-free grammars) and discriminative (conditional random fields) approaches.
  • Utilized both RNA sequences with known secondary structures and those with unknown structures for model training.
  • Applied techniques successfully used in natural language processing to RNA structure prediction.

Main Results:

  • Incorporating RNA sequences with unknown secondary structures into the training process significantly improved prediction accuracy.
  • Demonstrated the effectiveness of the semi-supervised learning approach in enhancing RNA secondary structure prediction.
  • Achieved improved accuracy compared to models trained solely on experimentally determined structures.

Conclusions:

  • The developed semi-supervised learning method is the first of its kind for RNA secondary structure prediction.
  • This technique offers a valuable solution for improving prediction accuracy when reliable experimental structures are limited.
  • The hybrid model effectively leverages unlabeled data, advancing the field of RNA bioinformatics.