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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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Related Experiment Video

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Microarray Analysis for Saccharomyces cerevisiae
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Published on: April 7, 2011

Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case.

José Luiz Rybarczyk-Filho1, Mauro A A Castro, Rodrigo J S Dalmolin

  • 1Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.

Nucleic Acids Research
|December 21, 2010
PubMed
Summary

This study introduces a novel computational method to analyze genome-wide expression data by ordering genes based on protein interaction probabilities. This approach creates experiment-independent functional modules, offering a new way to interpret cellular states and responses.

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09:12

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Saccharomyces cerevisiae Metabolic Labeling with 4-thiouracil and the Quantification of Newly Synthesized mRNA As a Proxy for RNA Polymerase II Activity

Published on: October 22, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Analyzing genome-wide expression data is challenging due to experiment-dependent gene clustering methods.
  • Traditional approaches rely on comparing expression levels to controls, leading to variable cluster definitions.
  • Existing methods are sensitive to cellular metabolic states, limiting cross-experiment comparability.

Purpose of the Study:

  • To develop a novel computational method for analyzing genome-wide expression data.
  • To create experiment-independent gene clustering based on protein-protein interaction probabilities.
  • To define stable functional modules associated with gene ontology terms.

Main Methods:

  • A computational method that orders genes on a line based on the probability of their products interacting.
  • Utilizes protein-protein association data from databases like STRING.
  • Applies the method to Saccharomyces cerevisiae genome expression data, projecting onto the ordered gene list.

Main Results:

  • Developed a genome organization independent of specific experimental conditions.
  • Identified functional modules linked to gene ontology terms.
  • Generated 'transcriptograms' by plotting transcription levels, which discriminate metabolic cellular states.

Conclusions:

  • The proposed method provides a robust framework for analyzing gene expression data.
  • Functional modules derived are stable across different experimental conditions.
  • The 'transcriptogram' visualization aids in understanding cell stimuli/responses and has potential diagnostic applications.