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Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition.

Prabina Kumar Meher1, Tanmaya Kumar Sahu1, Shachi Gahoi1

  • 1ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.

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|April 23, 2019
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Summary

This study comprehensively evaluated sequence encoding schemes for splice site prediction using machine learning. The paired-nucleotide frequency difference (FDTF) encoding with Support Vector Machines (SVM) demonstrated optimal accuracy for predicting gene structure.

Keywords:
Gene predictionIntron densityMarkov modelSequence encodingSupervised learning

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate splice site identification is crucial for predicting gene structure.
  • Machine learning approaches (MLAs) outperform rule-based methods but require numerical input via sequence encoding.
  • Evaluating diverse encoding schemes is essential for optimizing MLA performance in splice site prediction.

Purpose of the Study:

  • To comprehensively evaluate eight sequence encoding schemes for predicting donor and acceptor splice sites.
  • To compare the performance of five supervised learning methods (ANN, Bagging, Boosting, RF, SVM) across different encoding schemes and species.
  • To identify the optimal combination of encoding scheme and machine learning method for accurate splice site prediction.

Main Methods:

  • Evaluated eight sequence encoding schemes (Bayes kernel, DS, DM, FDDM, FDTF, MM1, MM1+MM2, MM2).
  • Utilized five supervised learning methods: Artificial Neural Network (ANN), Bagging, Boosting, Random Forest (RF), and Support Vector Machine (SVM).
  • Tested and validated performance across multiple species, including A. thaliana, C. elegans, D. melanogaster, H. sapiens, Ciona intestinalis, Dictyostelium discoideum, Phaeodactylum tricornutum, and Trypanosoma brucei.

Main Results:

  • The paired-nucleotide frequency difference (FDTF) encoding scheme achieved the highest accuracy, followed by MM2 or FDDM.
  • Support Vector Machine (SVM) and Random Forest (RF) demonstrated superior performance among the tested machine learning methods.
  • The SVM-FDTF combination proved to be the optimal approach for splice site prediction, with higher accuracy observed in species with lower intron density.

Conclusions:

  • The FDTF encoding scheme combined with SVM offers a highly accurate method for splice site prediction.
  • This study provides a comprehensive benchmark for sequence encoding strategies in splice site identification.
  • An R-package, EncDNA, has been developed to facilitate the application of these encoding schemes in genomic analysis.