Towards a better prediction of subcellular location of long non-coding RNA

doi:10.1007/s11704-021-1015-3

Front. Comput. Sci.

2022, Vol. 16

Issue (5) : 165903 https://doi.org/10.1007/s11704-021-1015-3

RESEARCH ARTICLE

Towards a better prediction of subcellular location of long non-coding RNA

Zhao-Yue ZHANG, Zi-Jie SUN, Yu-He YANG, Hao LIN(

)

Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China

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Abstract

The spatial distribution pattern of long non-coding RNA (lncRNA) in cell is tightly related to their function. With the increment of publicly available subcellular location data, a number of computational methods have been developed for the recognition of the subcellular localization of lncRNA. Unfortunately, these computational methods suffer from the low discriminative power of redundant features or overfitting of oversampling. To address those issues and enhance the prediction performance, we present a support vector machine-based approach by incorporating mutual information algorithm and incremental feature selection strategy. As a result, the new predictor could achieve the overall accuracy of 91.60%. The highly automated web-tool is available at lin-group.cn/server/iLoc-LncRNA(2.0)/website. It will help to get the knowledge of lncRNA subcellular localization.

Keywords lncRNA subcellular localization support vector machine mutual information Web server

Corresponding Author(s): Hao LIN

Just Accepted Date: 12 April 2021 Issue Date: 24 December 2021

Cite this article:

Zhao-Yue ZHANG,Zi-Jie SUN,Yu-He YANG, et al. Towards a better prediction of subcellular location of long non-coding RNA[J]. Front. Comput. Sci., 2022, 16(5): 165903.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-021-1015-3
https://academic.hep.com.cn/fcs/EN/Y2022/V16/I5/165903

Fig.1 (a) The workflow diagram of developing the iLoc-lncRNA(2.0); (b) species composition in the benchmark dataset; (c) length distribution of lncRNA sequences and the number of samples in each subcellular location

Method	Feature dimension	Subcellular location	$S n %$	$S p %$	$P r e %$	$M C C$	$A U C$	$O A %$
iLoc-lncRNA(2.0) ^a)	1409	Nucleus	91.03	95.59	86.59	0.852	0.969	91.60
		Cytoplasm	94.37	89.96	94.59	0.842	0.969
		Ribosome	83.72	99.01	85.71	0.837	0.986
		Exosome	66.67	99.36	83.33	0.735	0.949
Locate-R ^b)	857	Nucleus	66.92	95.15	/	0.66	0.900	90.69
		Cytoplasm	84.74	89.1	/	0.725	0.930
		Ribosome	100	98.37	/	0.97	1.000
		Exosome	100	99.17	/	0.978	1.000
lncLocation	/	Nucleus	74.19	/	95.83	/	/	87.78
		Cytoplasm	100	/	85	/	/
		Ribosome	55.56	/	100	/	/
		Exosome	33.33	/	100	/	/
iLoc-lncRNA ^c)	4107	Nucleus	77.56	97.59	/	0.796	/	86.72
		Cytoplasm	99.06	67.68	/	0.742	/
		Ribosome	46.51	99.83	/	0.652	/
		Exosome	16.67	1	/	0.4	/

Tab.1 The performance of the SVM-based subcellular location prediction model

Fig.2 Incremental feature selection strategy accuracy curve for mRMR feature selection

Fig.3 ROC curves for the iLoc-lncRNA(2.0) for (a) nucleus, (b) cytosol, (c) ribosome, (d) exosome

Fig.4 Visualization of significant class-specific sequence motifs for (a) nucleus, (b) cytosol, (c) ribosome, (d) exosome by using DREME

Fig.5 The class-specific motifs distribution in four classes

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