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Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Riboswitches01:56

Riboswitches

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Ligand Binding Sites

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Related Experiment Video

Updated: May 27, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

Using binding profiles to predict binding sites of target RNAs.

Unyanee Poolsap1, Yuki Kato, Kengo Sato

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. unyanee@is.naist.jp

Journal of Bioinformatics and Computational Biology
|November 16, 2011
PubMed
Summary

bistaRNA accurately predicts multiple binding sites for RNA-RNA interactions, reducing computational cost. This method aids in discovering new targets for antisense RNA regulation.

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Last Updated: May 27, 2026

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
11:34

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Published on: August 9, 2019

PAR-CliP - A Method to Identify Transcriptome-wide the Binding Sites of RNA Binding Proteins
12:24

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Published on: July 2, 2010

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

Area of Science:

  • Computational biology
  • Molecular biology
  • Bioinformatics

Background:

  • RNA-RNA interactions are crucial for understanding small non-coding RNA functions.
  • Predicting these interactions computationally is essential for biological research.
  • Existing methods for analyzing interacting RNA secondary structures have limitations.

Purpose of the Study:

  • To develop a novel computational method, bistaRNA, for predicting multiple binding sites of target RNAs interacting with regulatory antisense RNAs.
  • To reduce the computational cost associated with RNA-RNA interaction prediction.
  • To identify novel RNA targets for specific antisense RNAs, offering insights into regulatory mechanisms.

Main Methods:

  • bistaRNA utilizes binding profiles to score hybridized structures, optimizing computational efficiency.
  • The method incorporates an ensemble of equilibrium interacting structures.
  • Dynamic programming is employed to maximize expected accuracy in predicting binding sites.

Main Results:

  • Experimental validation demonstrates bistaRNA's high accuracy in predicting RNA-RNA interaction sites.
  • bistaRNA exhibits significantly faster computation times compared to existing methods.
  • The study successfully identified potential new targets for antisense RNAs, validating the method's utility.

Conclusions:

  • bistaRNA is an accurate and efficient tool for predicting multiple RNA-RNA binding sites.
  • The method provides valuable insights into antisense RNA regulatory networks.
  • bistaRNA facilitates the discovery of novel RNA targets and enhances understanding of gene regulation.