Design powerful predictor for mRNA subcellular location prediction in Homo sapiens.
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IF: 13.994
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Cited by: 82
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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

APEX-Seq
Omics
smFISH
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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