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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

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RNA-seq

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Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of 2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...

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DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on

Min Zeng¹, Yifan Wu¹, Chengqian Lu¹

¹Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan, 410083, China.

Briefings in Bioinformatics

|September 9, 2021

Summary

This summary is machine-generated.

DeepLncLoc accurately predicts long non-coding RNA (lncRNA) subcellular localization using a novel deep learning approach. This method preserves sequence order, improving upon traditional k-mer based techniques for enhanced biological function insights.

Keywords:

deep learning lncRNA subcellular localization prediction subsequence embedding

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Long non-coding RNAs (lncRNAs) are crucial RNA molecules influencing cellular processes.
lncRNA subcellular localization provides key insights into their biological functions.
Current prediction methods using k-mer features often neglect sequence order information.

Purpose of the Study:

To develop an advanced computational framework, DeepLncLoc, for predicting lncRNA subcellular localization.
To introduce a novel subsequence embedding method that retains lncRNA sequence order.
To improve the accuracy and effectiveness of lncRNA subcellular localization prediction.

Main Methods:

Developed DeepLncLoc, a deep learning framework for lncRNA subcellular localization.
Implemented a new subsequence embedding technique to capture sequence order.
Utilized a text convolutional neural network for feature learning and prediction.

Main Results:

DeepLncLoc demonstrated superior performance compared to traditional machine learning models and existing predictors.
The novel subsequence embedding method effectively retained essential sequence order information.
The framework achieved high accuracy in predicting lncRNA subcellular localization.

Conclusions:

DeepLncLoc offers a powerful and effective computational tool for predicting lncRNA subcellular localization.
The proposed subsequence embedding method represents a significant advancement for sequence-based prediction tasks.
The DeepLncLoc web server and source code are publicly available for research use.