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Prediction of LncRNA Subcellular Localization with Deep Learning from Sequence Features.

Brian L Gudenas¹, Liangjiang Wang²

¹Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.

Scientific Reports

|November 8, 2018

Summary

Researchers developed DeepLncRNA, a deep learning tool, to predict the subcellular localization of long non-coding RNAs (lncRNAs) using their sequences. This method aids in understanding lncRNA function, as localization is key to their biological roles.

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Long non-coding RNAs (lncRNAs) play crucial roles in cellular processes but are often unannotated, hindering functional studies.
LncRNA functional annotation is challenging due to low sequence conservation and specific expression patterns.
Subcellular localization of lncRNAs provides vital functional insights, yet prediction methods are scarce.

Purpose of the Study:

To develop a novel computational method for predicting lncRNA subcellular localization directly from transcript sequences.
To address the gap in predictive tools for lncRNA localization, a key determinant of function.
To investigate the role of sequence features in lncRNA subcellular localization.

Main Methods:

Development of DeepLncRNA, a deep learning algorithm utilizing lncRNA transcript sequences.
Analysis of 93 strand-specific RNA-seq samples from nuclear and cytosolic fractions across multiple cell types.
Extraction of sequence-based features to train the DeepLncRNA prediction model.

Main Results:

DeepLncRNA achieved 72.4% accuracy, 83% sensitivity, and 62.4% specificity in predicting lncRNA subcellular localization.
The model obtained an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.787.
Primary sequence motifs were identified as significant drivers of lncRNA subcellular localization.

Conclusions:

DeepLncRNA offers a promising approach for predicting lncRNA subcellular localization from sequence data.
The findings highlight the importance of sequence motifs in determining where lncRNAs function within the cell.
This work facilitates the functional annotation of uncharacterized lncRNAs.