Allocator: a graph neural network-based framework for mRNA subcellular localization prediction

Introduction

The asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins with-in human cells. This non-uniform distribution, a cornerstone of developmental biology, plays a pivotal role in numerous cellular processes. To advance our comprehension of gene regulatory networks, it is essential to develop computational tools for accurately identifying the subcellular localizations of mRNAs. Howev-er, considering multi-localization phenomena remains limited in existing approaches, with none consider-ing the influence of RNA’s secondary structure. In this study, we introduce ‘Allocator’, a deep learning-based model that seamlessly integrates both sequence-level and structure-level information, significantly enhancing the prediction of mRNA multi-localization. Allocator equips four efficient feature extractors, each designed to handle different inputs. Two are tailored for sequence-based inputs, incorporating MLP, and self-attention mechanisms. The other two are specialized in processing structure-based inputs, employing graph neural networks. Benchmarking results underscore Allocator’s superiority over state-of-the-art methods, showcasing its strength in revealing intricate localization associations. Furthermore, we have developed a user-friendly web server for Allocator, enhancing its accessibility. You can explore Allo-cator through our web server, available at http://allocator.unimelb-biotools.cloud.edu.au/.

Environment

• Anaconda
• python 3.7.13

Dependency

• torch 1.12.1
• torch-cluster 1.6.0
• torch-geometric 2.1.0.post1
• torch-scatter 2.0.9
• torch-sparse 0.6.15
• numpy 1.21.6
• biopython 1.81
• RNAfold
• LinearFold

Installation of RNAfold(Linux)

wget -q https://www.tbi.univie.ac.at/RNA/download/sourcecode/2_5_x/ViennaRNA-2.5.0.tar.gz

tar xfz ViennaRNA-2.5.0.tar.gz

cd /content/ViennaRNA-2.5.0

./configure

make

sudo make install

Installation of LinearFold(Linux)

git clone https://github.com/LinearFold/LinearFold.git

cd LinearFold

make

Usage

To get the information the user needs to enter for help, run: python Allocator.py --help or python Allocator.py -h

as follows:

python Allocator.py -h

usage: it's usage tip.

optional arguments:

-h, --help show this help message and exit

--input_path input fasta.

--output_path output path.

--device cpu or cuda

Examples:

Prediction:

python Allocator.py --input_path input.fasta --output_path results --device cpu