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Related Concept Videos

Reporter Genes02:11

Reporter Genes

Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
Commonly used reporter...
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.
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Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
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Related Experiment Video

Updated: Jun 30, 2026

Competitive Genomic Screens of Barcoded Yeast Libraries
11:59

Competitive Genomic Screens of Barcoded Yeast Libraries

Published on: August 11, 2011

Mining for putative regulatory elements in the yeast genome using gene expression data.

J Vilo1, A Brazma, I Jonassen

  • 1European Bioinformatics Institute, EMBL Outstation, Hinxton, Cambridge, United Kingdom. vilo@ebi.ac.uk

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|September 8, 2000
PubMed
Summary
This summary is machine-generated.

We developed automated tools to discover regulatory DNA sequence patterns. This method identifies subtle signals in yeast gene expression data, with 48 of 62 discovered pattern groups matching known regulatory sites.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying regulatory signals in genome sequences is crucial for understanding gene regulation.
  • Existing methods may lack the sensitivity to detect subtle or novel regulatory elements.

Purpose of the Study:

  • To develop and validate an automated computational pipeline for discovering putative regulatory signals in genomic sequences.
  • To identify statistically significant sequence patterns associated with specific gene expression clusters.

Main Methods:

  • Clustering of gene expression data.
  • Rapid, exhaustive search for statistically significant sequence patterns in upstream gene regions.
  • Clustering of discovered patterns by mutual similarity to derive consensus patterns.
  • Validation against existing databases of known regulatory signals (SCPD).

Main Results:

  • Identified over 52,000 gene expression clusters in yeast (Saccharomyces cerevisiae).
  • Discovered nearly 1,500 significant sequence patterns.
  • Clustered patterns into 62 groups, deriving consensus patterns.
  • Found that 48 of 62 groups contained patterns matching known transcription factor binding sites in the SCPD database.

Conclusions:

  • The developed automated pipeline effectively discovers putative regulatory signals in genomic DNA.
  • The method successfully identified known and potentially novel regulatory elements in yeast.
  • This approach provides a powerful tool for genomic sequence analysis and regulatory network inference.