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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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

Updated: May 17, 2026

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform
09:13

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform

Published on: January 13, 2016

Imputing gene expression from selectively reduced probe sets.

Yoni Donner1, Ting Feng, Christophe Benoist

  • 1Department of Computer Science, Stanford University, Stanford, California, USA.

Nature Methods
|October 16, 2012
PubMed
Summary
This summary is machine-generated.

Selecting a small subset of gene probes and imputing missing values significantly reduces the cost of gene expression profiling. Our probe selection for imputation (PSI) algorithms accurately reconstructs missing data across diverse applications.

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A High Throughput in situ Hybridization Method to Characterize mRNA Expression Patterns in the Fetal Mouse Lower Urogenital Tract

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Last Updated: May 17, 2026

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform
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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Measuring complete gene expression profiles across numerous experiments is prohibitively expensive.
  • High-throughput gene expression analysis is crucial for understanding biological systems.

Purpose of the Study:

  • To develop and validate a cost-effective approach for gene expression profiling using a reduced probe subset.
  • To introduce novel algorithms for simultaneous probe selection and missing value imputation.

Main Methods:

  • Developed 'probe selection for imputation' (PSI) algorithms.
  • Utilized preliminary full expression profiles to select optimal probe subsets.
  • Implemented imputation techniques to reconstruct missing gene expression data.

Main Results:

  • PSI algorithms successfully reconstructed missing gene expression values in diverse applications.
  • Evaluated performance using multiple metrics of biological importance.
  • Analyzed PSI method performance under varying experimental conditions.

Conclusions:

  • The proposed PSI approach offers a cost-effective alternative for large-scale gene expression studies.
  • Guidelines for optimal method selection and imputation accuracy estimation are provided.
  • Successfully applied the PSI approach to a large-scale immune system variation study.