Introduction

RNase H (RNH) is a pivotal domain in retrovirus to cleave the DNA-RNA hybrid for continuing retroviral replication. The crucial role indicates that RNH is a promising drug target for therapeutic intervention. However, annotated RNHs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. In this work, a computational RNH model was proposed to annotate new putative RNHs (np-RNHs) in the retroviruses. It basically predicts RNH domains through recognizing their start and end sites separately with SVM method. The classification accuracy rates are 100%, 99.01% and 97.52% respectively corresponding to jack-knife, 10-fold cross-validation and 5-fold cross-validation test. Subsequently, this model discovered 14,033 np-RNHs after scanning sequences without RNH annotations. All these predicted np-RNHs and annotated RNHs were employed to analyze the length, hydrophobicity and evolutionary relationship of RNH domains. They are all related to retroviral genera, which validates the classification of retroviruses to a certain degree. In the end, a software tool was designed for the application of our prediction model. The software together with datasets involved in this paper can be available for free download at https://sourceforge.net/projects/rhtool/files/?source=navbar.

Publications

  1. A Computational Model for Predicting RNase H Domain of Retrovirus.
    Cite this
    Wu S, Zhang X, Han J, 2016-01-01 - PloS one

Credits

  1. Sijia Wu
    Developer

    School of Electronic and Information Engineering, Xi'an Jiaotong University, China

  2. Xinman Zhang
    Developer

    School of Electronic and Information Engineering, Xi'an Jiaotong University, China

  3. Jiuqiang Han
    Investigator

    School of Electronic and Information Engineering, Xi'an Jiaotong University, China

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Summary
AccessionBT001627
Tool TypeApplication
Category
PlatformsWindows
TechnologiesC++
User InterfaceTerminal Command Line
Download Count0
Country/RegionChina
Submitted ByJiuqiang Han