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Ribosome Profiling

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.
The technique helps...

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Related Experiment Video

Updated: May 11, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Fuzzy kernel evidence Random Forest for identifying pseudouridine sites.

Mingshuai Chen^1,2, Mingai Sun³, Xi Su⁴

¹Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China.

Briefings in Bioinformatics

|April 15, 2024

Summary

This summary is machine-generated.

Identifying pseudouridine sites in RNA is challenging. A new computational method, PseU-FKeERF, accurately predicts these sites using fuzzy kernel evidence Random Forest, aiding future disease control and drug development.

Keywords:

RNA sequences evidence Random Forest fuzzy feature set kernel method pseudouridine sites

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Last Updated: May 11, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Published on: July 14, 2015

Identifying Amino Acid Overproducers Using Rare-Codon-Rich Markers

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Published on: June 24, 2019

Profiling Ubiquitin and Ubiquitin-like Dependent Post-translational Modifications and Identification of Significant Alterations

Profiling Ubiquitin and Ubiquitin-like Dependent Post-translational Modifications and Identification of Significant Alterations

Published on: November 7, 2019

Area of Science:

Molecular Biology
Bioinformatics

Background:

Pseudouridine is a crucial RNA modification found in prokaryotes and eukaryotes.
Experimental identification of pseudouridine sites is time-consuming and resource-intensive.
Accurate computational methods are needed for pseudouridine site identification from RNA sequencing data.

Purpose of the Study:

To develop a novel computational method for accurate pseudouridine site identification.
To address the challenges of experimental pseudouridine site detection.

Main Methods:

Proposed a new method called PseU-FKeERF, utilizing a fuzzy kernel evidence Random Forest (FKeERF) algorithm.
Integrated four RNA feature coding schemes for enhanced feature representation.
Employed fuzzy logic to expand feature space and kernel methods for interpretable prediction.

Main Results:

PseU-FKeERF demonstrated high accuracy in identifying pseudouridine sites.
The method outperformed several existing state-of-the-art techniques in both cross-validation and independent tests.
Achieved superior predictive performance compared to current methods.

Conclusions:

PseU-FKeERF offers an accurate and efficient computational approach for pseudouridine site identification.
This advancement can support future research in disease control and drug development.