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Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

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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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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Updated: Jan 1, 2026

RIBO-seq in Bacteria: a Sample Collection and Library Preparation Protocol for NGS Sequencing
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DeepShape: estimating isoform-level ribosome abundance and distribution with Ribo-seq data.

Hongfei Cui1,2, Hailin Hu3, Jianyang Zeng4

  • 1Institute for Artificial Intelligence and Department of Computer Science and Technology, Tsinghua University, Beijing, China.

BMC Bioinformatics
|December 22, 2019
PubMed
Summary

DeepShape, a novel deep learning method, accurately estimates ribosome abundance and profiles from Ribo-seq data without RNA-seq. This computational tool enhances ribosome profiling analysis for translation studies.

Keywords:
Multiple alignmentRibo-seqTranscript-levelTranslation

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Area of Science:

  • Computational biology
  • Molecular biology
  • Genomics

Background:

  • Ribosome profiling (Ribo-seq) is crucial for understanding translation.
  • Mapping Ribo-seq reads to transcripts and assigning multi-mapped reads to isoforms are key challenges.
  • Current methods for handling multi-mapped reads are suboptimal, leading to potential inaccuracies.

Purpose of the Study:

  • To develop an RNA-seq-free computational method for accurate ribosome profiling.
  • To estimate isoform-specific ribosome abundance and ribosome density profiles.
  • To analyze translational regulation in cancer cells.

Main Methods:

  • Developed DeepShape, a deep learning model for Ribo-seq data analysis.
  • DeepShape estimates ribosome abundance and profiles without requiring RNA-seq data.
  • Introduced the Codon Residence Index (CRI) to analyze ribosome speed at the codon level.

Main Results:

  • DeepShape demonstrated superior accuracy in simulations compared to existing methods.
  • Applied DeepShape to Ribo-seq data from PC3 human prostate cancer cells.
  • Identified distinct translational regulation patterns for different isoforms of invasion/metastasis genes.
  • Observed PP242 treatment-specific regulation of codon translation.

Conclusions:

  • DeepShape is a powerful and accurate tool for Ribo-seq data analysis.
  • The method facilitates a deeper understanding of translational regulation.
  • DeepShape advances the analysis of ribosome profiling data.