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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 Splicing01:32

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
RNA Splicing01:32

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
Pre-mRNA Processing: RNA Splicing01:32

Pre-mRNA Processing: RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...

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Related Experiment Video

Updated: May 15, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

Analyzing the performance of deep learning splice prediction algorithms.

Nathan Fortier1, Gabe Rudy1, Andreas Scherer1

  • 1Research and Development, Golden Helix, Inc., Bozeman, Montana, United States of America.

Plos One
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Open-source SpliceAI tools (CI-SpliceAI and OpenSpliceAI) show similar performance to the original for predicting splice-altering variants. However, performance varies for deep intronic variants, and standard thresholds miss many pathogenic cases.

Related Experiment Videos

Last Updated: May 15, 2026

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
09:58

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Published on: December 9, 2016

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • SpliceAI is a leading tool for predicting splice-altering variants.
  • Restrictive licensing limits clinical adoption of SpliceAI.
  • Open-source implementations exist but require independent benchmarking.

Purpose of the Study:

  • To independently benchmark open-source SpliceAI implementations (CI-SpliceAI, OpenSpliceAI) against the original.
  • To evaluate performance across diverse variant datasets, including deep intronic variants.
  • To compare deep learning models against legacy splice-prediction tools.

Main Methods:

  • Compared original SpliceAI with CI-SpliceAI and OpenSpliceAI across six diverse datasets.
  • Evaluated performance using balanced accuracy and correlation analysis.
  • Assessed deep learning models against a legacy ensemble and four individual tools.

Main Results:

  • Deep learning algorithms consistently outperformed the legacy ensemble.
  • All three deep learning algorithms showed similar performance on large datasets with canonical splice site variants.
  • Original SpliceAI performed best on deep intronic variants; optimal thresholds differ significantly from standard recommendations.
  • Open-source implementations largely reproduced original SpliceAI behavior but diverged on deep intronic variants.

Conclusions:

  • Open-source SpliceAI reimplementations successfully reproduce original algorithm performance across multiple contexts.
  • Both open-source tools consistently outperform traditional splice prediction methods.
  • Performance on deep intronic variants differs, and standard score thresholds are inadequate for this class.