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. 2022 Nov 19;23(6):bbac467.
doi: 10.1093/bib/bbac467.

Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations

Yue Bi  1   2 , Fuyi Li  1   3   4 , Xudong Guo  3 , Zhikang Wang  1 , Tong Pan  1 , Yuming Guo  5 , Geoffrey I Webb  2 , Jianhua Yao  6 , Cangzhi Jia  7 , Jiangning Song  1   2
Affiliations

Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations

Yue Bi et al. Brief Bioinform. .

Abstract

Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.

Keywords: mRNA; machine learning; multi-class classification; multi-label prediction; sequence analysis; subcellular localization.

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Figures

Figure 1
Figure 1
The workflow of the methodology of Clarion.
Figure 2
Figure 2
Statistical distributions of mRNA entries in the benchmark dataset curated in this study. (A) The relative percentages of mRNAs with different labels in the benchmark dataset. (B) The distribution of the positive and negative samples in the nine subcellular compartments. (C) The distribution of mRNAs with different labels in the nine compartments.
Figure 3
Figure 3
(A) The example-based accuracies of seven different machine learning algorithms. (B) The performance comparison of the models trained with different weights in terms of Accexam, average precision, coverage, one-error, ranking loss and hamming loss. (C) The accuracies of the binary models in weighted series for predicting each mRNA subcellular localization.
Figure 4
Figure 4
Feature importance ranking based on the Shapley values. (A) Top 15 features for the nucleus, (B) top 15 features for the chromatin, (C) top 15 features for the ribosome and (D) top 15 features among all nine subcellular localizations.
See this image and copyright information in PMC

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