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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.
The technique helps...

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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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Detection and interpretation of metabolite-transcript coresponses using combined profiling data.

Henning Redestig1, Ivan G Costa

  • 1RIKEN Plant Science Center, Yokohama, Japan. henning@psc.riken.jp

Bioinformatics (Oxford, England)
|June 21, 2011
PubMed
Summary

We developed a novel method to predict gene-metabolite pathway relationships from transcriptomics and metabolomics data. This approach improves upon traditional correlation methods, especially in noisy, time-shifted biological systems.

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Last Updated: May 31, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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Published on: September 20, 2022

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis
11:25

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

  • Systems biology
  • Genomics
  • Metabolomics

Background:

  • Understanding gene expression and metabolite interplay is crucial for stress response physiology.
  • Parallel transcriptomics and metabolomics in time-series experiments offer systematic insights.
  • Publicly available datasets provide valuable resources for hypothesis generation.

Purpose of the Study:

  • To predict pathway comemberships between metabolites and genes using their coresponses to stress.
  • To develop a robust method for detecting gene-metabolite relationships despite noise and time delays.

Main Methods:

  • Utilized a hidden Markov model-based similarity measure, outperforming Pearson correlation in noisy, time-shifted data.
  • Proposed a supervised method integrating pathway information to create a consensus similarity statistic.
  • Applied the method to four combined profiling datasets.

Main Results:

  • Successfully predicted metabolite-gene comemberships for numerous KEGG pathways.
  • Demonstrated the effectiveness of the consensus statistic over single measures.

Conclusions:

  • The developed method enhances the detection of transcriptionally regulated pathways.
  • Facilitates the identification of novel metabolically related genes.
  • Opens new avenues for systems biology research by linking gene expression to metabolic function.