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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
RNA-seq03:21

RNA-seq

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 microarray-based...
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: Jun 15, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Reducing the algorithmic variability in transcriptome-based inference.

Salih Tuna1, Mahesan Niranjan

  • 1School of Electronics and Computer Science, University of Southampton, Southampton, UK.

Bioinformatics (Oxford, England)
|March 10, 2010
PubMed
Summary
This summary is machine-generated.

Binary gene expression data simplifies microarray analysis by reducing variability from algorithm choices. This approach enhances the accuracy of inferring biological insights from gene expression studies.

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Cost-Efficient Transcriptomic-Based Drug Screening
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Last Updated: Jun 15, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

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Published on: March 7, 2018

Cost-Efficient Transcriptomic-Based Drug Screening
06:40

Cost-Efficient Transcriptomic-Based Drug Screening

Published on: February 23, 2024

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput microarray analysis involves complex preprocessing with numerous algorithmic choices.
  • Lack of standardized guidance leads to variability in results across different analytical pipelines.
  • Gene expression data preprocessing is critical for reliable downstream biological interpretation.

Purpose of the Study:

  • To investigate the impact of algorithmic choices in microarray preprocessing.
  • To evaluate binary representation of gene expression data as a method to reduce preprocessing variability.
  • To assess if binary representation improves the quality of inference from microarray studies.

Main Methods:

  • Utilizing binary representations of gene expression data, focusing on expression presence/absence.
  • Comparing the effect of different preprocessing algorithms on microarray data analysis.
  • Applying Tanimoto kernel with support vector machines to binary transcriptome data for phenotype classification.

Main Results:

  • Binary representation significantly reduces result variability stemming from algorithmic choices in preprocessing.
  • The use of binary transcriptome data with a Tanimoto kernel for support vector machines improves phenotype classification performance.
  • This approach mitigates the impact of algorithm selection on the reliability of microarray study outcomes.

Conclusions:

  • Binary representation of gene expression data offers a robust strategy to standardize microarray analysis.
  • This method enhances the reliability and accuracy of biological inference from high-throughput gene expression studies.
  • Binary data combined with appropriate machine learning kernels provides a powerful tool for phenotype classification.