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Related Concept Videos

RNA-seq03:21

RNA-seq

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
RNA Structure01:23

RNA Structure

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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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RNA Structure01:19

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Ribosome Profiling02:24

Ribosome Profiling

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Stochastic sampling of the RNA structural alignment space.

Arif Ozgun Harmanci1, Gaurav Sharma, David H Mathews

  • 1Department of Electrical and Computer Engineering, University of Rochester, Hopeman 204, RC Box 270126, Rochester, NY 14627, USA.

Nucleic Acids Research
|May 12, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for predicting RNA secondary structures and alignments by jointly sampling structural alignment space. This joint sampling approach improves accuracy compared to single-sequence methods.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • RNA Structure Prediction

Background:

  • Predicting RNA secondary structures and alignments is crucial for understanding RNA function.
  • Existing methods often analyze sequences independently, limiting accuracy.
  • Homologous RNA sequences offer opportunities for comparative analysis to improve predictions.

Purpose of the Study:

  • To develop a novel method for predicting common secondary structures and alignments of two homologous RNA sequences.
  • To improve upon single-sequence sampling methods by utilizing joint sampling of the structural alignment space.

Main Methods:

  • Sampling the joint space of alignments and common secondary structures for homologous RNA sequences.
  • Utilizing a pseudo-Boltzmann distribution based on pseudo-free energy, combining thermodynamic and hidden Markov models.
  • Employing cluster analysis to evaluate the accuracy of predicted structures and alignments.

Main Results:

  • Joint sampling of the structural alignment space yields more accurate RNA secondary structure and alignment predictions than single-sequence methods.
  • Cluster analysis demonstrates that samples from joint sampling are more tightly clustered.
  • The 'best' centroid structure from joint sampling is, on average, more accurate than predictions from other methods.

Conclusions:

  • The proposed joint sampling method enhances the accuracy of predicting common RNA secondary structures and alignments for homologous sequences.
  • This approach offers a significant improvement over traditional single-sequence analysis.
  • Alternative candidate predictions can be identified through cluster analysis of the sampled space.