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Ribosome Profiling02:24

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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...
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Related Experiment Video

Updated: Jun 20, 2025

An In Vitro Dormancy Model of Estrogen-sensitive Breast Cancer in the Bone Marrow: A Tool for Molecular Mechanism Studies and Hypothesis Generation
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Modeling the Depth of Cellular Dormancy from RNA-Sequencing Data.

Michelle Yuchen Wei1, Guang Yao2,3

  • 1Department of Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA.

Methods in Molecular Biology (Clifton, N.J.)
|July 22, 2024
PubMed
Summary

This study introduces QDSWorkflow, an R package for analyzing RNA sequencing data. It helps researchers understand cellular dormancy depth in both bulk and single-cell samples.

Keywords:
DormancyQuiescence depth scoreR packageRNA-sequencing

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • High-throughput transcriptome RNA sequencing (RNA-seq) is crucial for studying dynamic biological processes.
  • Understanding cellular dormancy is vital for various biological and medical research areas.

Purpose of the Study:

  • To present a novel computational framework for characterizing heterogeneous cellular dormancy depth.
  • To enable the analysis of dormancy using RNA-sequencing data from both bulk and single-cell samples.

Main Methods:

  • Development of an R package named QDSWorkflow.
  • Implementation of a computational framework within QDSWorkflow.
  • Application to analyze RNA-sequencing data.

Main Results:

  • The QDSWorkflow package provides a method to characterize cellular dormancy depth.
  • The framework is applicable to heterogeneous cellular populations.
  • Successful application to RNA-sequencing data from bulk and single-cell samples.

Conclusions:

  • QDSWorkflow offers a valuable tool for researchers studying cellular dormancy.
  • The framework facilitates a deeper understanding of dynamic biological processes through RNA-seq analysis.