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RNA-seq03:21

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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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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Published on: December 9, 2022

Evaluating mixture models for building RNA knowledge-based potentials.

Adelene Y L Sim1, Olivier Schwander, Michael Levitt

  • 1Department of Applied Physics, Stanford University, Stanford, CA 94305-4090, USA. adelene@stanford.edu

Journal of Bioinformatics and Computational Biology
|July 20, 2012
PubMed
Summary
This summary is machine-generated.

We developed a new method to predict RNA structure by creating smoother knowledge-based potentials. This approach overcomes limitations of traditional methods, improving RNA structure modeling and function understanding.

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RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • Ribonucleic acid (RNA) molecules are crucial for biological processes and require specific folded structures to function.
  • Understanding RNA structure is key to understanding RNA function.
  • Knowledge-based potentials derived from experimental structures aid in predicting biomolecular structures like RNA.

Purpose of the Study:

  • To address the limitations of ruggedness in knowledge-based potentials for RNA structure prediction.
  • To compare different mixture models for building knowledge-based potentials, specifically Kernel Density Estimation, Expectation Minimization, and Dirichlet Process.
  • To develop a smoother, more effective knowledge-based potential for RNA structure modeling.

Main Methods:

  • Compared knowledge-based potentials derived from inter-atomic distances in RNA structures.
  • Utilized various mixture models: Kernel Density Estimation, Expectation Minimization, and Dirichlet Process.
  • Evaluated the efficacy of potentials in selecting native-like RNA models from structural decoys.

Main Results:

  • A smooth knowledge-based potential generated using the Dirichlet Process demonstrated success in identifying native-like RNA structures.
  • The Dirichlet Process potential showed comparable efficacy to spline-fitting methods applied to binned distance histograms.
  • The developed potential exhibited reduced ruggedness compared to traditional methods.

Conclusions:

  • The Dirichlet Process offers a robust method for creating smoother knowledge-based potentials for RNA structure prediction.
  • This less rugged potential enhances the applicability of knowledge-based potentials in diverse structural modeling scenarios.
  • The findings contribute to a better understanding of RNA structure and function through improved computational modeling.