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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...
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Related Experiment Video

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A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells
06:02

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells

Published on: October 28, 2025

Improved bolstering error estimation for gene ranking.

Kiet N T Huynh1, John H Phan, Tan M Vo

  • 1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
Summary
This summary is machine-generated.

We evaluated gene expression analysis metrics for disease research. Modified bolstering offers the best accuracy and efficiency, especially for small sample sizes, outperforming cross-validation and bootstrap methods.

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A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying differentially expressed genes in diseased tissues is crucial for understanding disease mechanisms.
  • The performance of gene expression analysis methods heavily relies on the chosen evaluation metric.
  • Small sample sizes in real-world datasets often lead to classifier data-overfitting.

Purpose of the Study:

  • To evaluate the performance of various error estimation algorithms for gene expression analysis.
  • To investigate the impact of sample size on metric performance and classifier overfitting.
  • To identify the most accurate and computationally efficient evaluation metric for disease gene identification.

Main Methods:

  • Examined cross-validation, bootstrap, resubstitution, and resubstitution with bolstering error estimation algorithms.
  • Utilized support vector machine, Fisher's discriminant, and signed distance function classifiers.
  • Generated synthetic datasets based on real data to control for sample size and overfitting.

Main Results:

  • Resubstitution with bolstering initially showed the best performance, particularly in computational efficiency.
  • Classical bolstering exhibited bias in high-dimensional data.
  • Modified bolstering demonstrated superior estimation accuracy and computational efficiency across various sample sizes.

Conclusions:

  • Modified bolstering is the optimal metric for evaluating gene expression analysis methods, balancing accuracy and efficiency.
  • Increasing sample size helps reduce estimation bias in bolstering methods.
  • The findings provide guidance for selecting robust evaluation metrics in genomic studies of disease.