IRESpy: an XGBoost model for prediction of internal ribosome entry sites

BMC Bioinformatics
Junhui Wang, Michael Gribskov

Abstract

Internal ribosome entry sites (IRES) are segments of mRNA found in untranslated regions that can recruit the ribosome and initiate translation independently of the 5' cap-dependent translation initiation mechanism. IRES usually function when 5' cap-dependent translation initiation has been blocked or repressed. They have been widely found to play important roles in viral infections and cellular processes. However, a limited number of confirmed IRES have been reported due to the requirement for highly labor intensive, slow, and low efficiency laboratory experiments. Bioinformatics tools have been developed, but there is no reliable online tool. This paper systematically examines the features that can distinguish IRES from non-IRES sequences. Sequence features such as kmer words, structural features such as QMFE, and sequence/structure hybrid features are evaluated as possible discriminators. They are incorporated into an IRES classifier based on XGBoost. The XGBoost model performs better than previous classifiers, with higher accuracy and much shorter computational time. The number of features in the model has been greatly reduced, compared to previous predictors, by including global kmer and structural features. The contributio...Continue Reading

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Citations

May 26, 2020·Clinical & Translational Oncology : Official Publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·Y ShiJ Xu
Nov 25, 2020·Molecular Therapy. Nucleic Acids·Yue BiJiangning Song
Jul 10, 2021·Frontiers in Cell and Developmental Biology·Qi XiaoXiaoyan Zhu

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Methods Mentioned

BETA
antisense oligonucleotides
RNA-seq
FACS
RNAS

Software Mentioned

LIME
Shiny
RNAfold
Viral IRES Prediction System ( VIPS )
IRESpy
UNAfold
VIPS
shiny app
IRESfinder
RNA Align

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