Design powerful predictor for mRNA subcellular location prediction in Homo sapiens.

PMID:31994694

IF: 13.994

Cited by: 82

Abstract

Messenger RNAs (mRNAs) shoulder special responsibilities that transmit genetic code from DNA to discrete locations in the cytoplasm. The locating process of mRNA might provide spatial and temporal regulation of mRNA and protein functions. The situ hybridization and quantitative transcriptomics analysis could provide detail information about mRNA subcellular localization; however, they are time consuming and expensive. It is highly desired to develop computational tools for timely and effectively predicting mRNA subcellular location. In this work, by using binomial distribution and one-way analysis of variance, the optimal nonamer composition was obtained to represent mRNA sequences. Subsequently, a predictor based on support vector machine was developed to identify the mRNA subcellular localization. In 5-fold cross-validation, results showed that the accuracy is 90.12% for Homo sapiens (H. sapiens). The predictor may provide a reference for the study of mRNA localization mechanisms and mRNA translocation strategies. An online web server was established based on our models, which is available at http://lin-group.cn/server/iLoc-mRNA/.

Keywords

Omics

smFISH

APEX-Seq

feature selection

mRNA

statistical analysis

subcellular location

web server

MeSH terms

Computational Biology

Humans

RNA Transport

RNA, Messenger

Sequence Analysis, RNA

Software

Authors

Zhang, Zhao-Yue

Yang, Yu-He

Ding, Hui

Wang, Dong

Chen, Wei

Lin, Hao

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