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In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
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mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature

Prabina Kumar Meher1, Anil Rai2, Atmakuri Ramakrishna Rao3

  • 1ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India. prabina.meher@icar.gov.in.

BMC Bioinformatics
|June 25, 2021
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Summary

This study introduces a new computational tool to predict multiple messenger RNA (mRNA) locations within cells. The developed mLoc-mRNA server offers accurate predictions, improving upon existing methods for mRNA localization analysis.

Keywords:
BioinformaticsComputational biologyFeature selectionMachine learningSub-cellular localization

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Messenger RNA (mRNA) localization is vital for cellular growth, development, and spatio-temporal gene expression.
  • Traditional in situ hybridization methods for mRNA localization are costly and labor-intensive.
  • Existing computational tools often fail to predict multiple subcellular locations for a single mRNA.

Purpose of the Study:

  • To develop a high-end computational model for reliable and timely prediction of multiple mRNA subcellular localizations.
  • To address the limitations of current tools in predicting single mRNA locations.

Main Methods:

  • mRNA sequences from 9 different localizations were analyzed.
  • K-mer features (sizes 1-6) were used to transform sequences into numeric feature vectors (5460 features).
  • Elastic Net selected 1812 important features, and Random Forest was employed for prediction.

Main Results:

  • The model achieved high cross-validation accuracies for various localizations (e.g., 96.46% for mitochondrion, 97.42% for posterior).
  • Independent test set accuracies ranged from 65.33% to 94.26% across different localizations.
  • The developed approach demonstrated superior accuracy compared to existing mRNA localization prediction tools.

Conclusions:

  • A novel computational tool, mLoc-mRNA, has been developed for predicting multiple mRNA localizations.
  • An online prediction server is available at http://cabgrid.res.in:8080/mlocmrna/.
  • This tool is expected to complement existing methods for mRNA localization prediction.