Introduction

Until now, the subcellular localization of RNA has become a pivot mechanism for cell polarization, which occurs in single-celled organisms, animal and plant tissues and developing embryos of animals. mRNA transcript localization has been confirmed to be able to spatially localize gene expression and transcriptional protein. About 80% of transcripts are asymmetrically distributed in human cells, and mis-location of transcripts can also lead to disease such as spinal muscular atrophy, Alzheimer's disease and cancer. In recent years, subcellular localization algorithms based on machine learning have made extraordinary progress.

SubLocEP

SubLocEP can recognize individual subcellular locations, accurately.The supported file formats are : Fasta

SubLocEP

Method

The modeling process of SucLocEP is as follow. (1) Collect training dataset, independent dataset 1 and independent dataset 2. (2) Extract crucial features based on sequence-based feature and physicochemical properties. (3) Feature analysis and model ensemble construction. (4) performance evaluation and web generate.

Reference

1. Garg, A., et al., mRNALoc: a novel machine-learning based in-silico tool to predict mRNA subcellular localization. Nucleic Acids Research, 2020.
2. Garg, A., et al., mRNALoc: a novel machine-learning based in-silico tool to predict mRNA subcellular localization. Nucleic Acids Research, 2020.

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all rights reserved@ 2020 | Quan Zou, Ph.D. & Professor
Last modified date:3/8/2020