System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Epublish 5 citations

PubMed: 38326777 DOI: 10.1186/s12864-024-10077-9

Unified mRNA Subcellular Localization Predictor based on machine learning techniques.

BMC Genomics 25(1):151

Authors: Saleh Musleh, Muhammad Arif, Nehad M Alajez, Tanvir Alam

Affiliations

  • College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
  • Translational Cancer and Immunity Center (TCIC), Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University, Doha, Qatar.
  • College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar.
  • College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Abstract

BACKGROUND

The mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost.

METHODS

In this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an in silico strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER).

RESULTS

The proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection.

AVAILABILITY

We have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP .

MeSH

Mitochondria, Rna, Messenger, Machine Learning, Cytoplasm, Learning

Cited by