IRESpy: an XGBoost model for prediction of internal ribosome entry sites
Abstract Background 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 initiatio...
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| Format: | Article |
| Language: | English |
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BMC
2019-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-019-2999-7 |
| _version_ | 1819076737714094080 |
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| author | Junhui Wang Michael Gribskov |
| author_facet | Junhui Wang Michael Gribskov |
| author_sort | Junhui Wang |
| collection | DOAJ |
| description | Abstract Background 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. Results 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 contributions of model features are well explained by LIME and SHapley Additive exPlanations. The trained XGBoost model has been implemented as a bioinformatics tool for IRES prediction, IRESpy (https://irespy.shinyapps.io/IRESpy/), which has been applied to scan the human 5′ UTR and find novel IRES segments. Conclusions IRESpy is a fast, reliable, high-throughput IRES online prediction tool. It provides a publicly available tool for all IRES researchers, and can be used in other genomics applications such as gene annotation and analysis of differential gene expression. |
| first_indexed | 2024-12-21T18:46:04Z |
| format | Article |
| id | doaj.art-359ebe2fa2684347ae00cc95fbe5b20f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-21T18:46:04Z |
| publishDate | 2019-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-359ebe2fa2684347ae00cc95fbe5b20f2022-12-21T18:53:53ZengBMCBMC Bioinformatics1471-21052019-07-0120111510.1186/s12859-019-2999-7IRESpy: an XGBoost model for prediction of internal ribosome entry sitesJunhui Wang0Michael Gribskov1Biological Sciences Department, Purdue UniversityBiological Sciences Department, Purdue UniversityAbstract Background 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. Results 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 contributions of model features are well explained by LIME and SHapley Additive exPlanations. The trained XGBoost model has been implemented as a bioinformatics tool for IRES prediction, IRESpy (https://irespy.shinyapps.io/IRESpy/), which has been applied to scan the human 5′ UTR and find novel IRES segments. Conclusions IRESpy is a fast, reliable, high-throughput IRES online prediction tool. It provides a publicly available tool for all IRES researchers, and can be used in other genomics applications such as gene annotation and analysis of differential gene expression.http://link.springer.com/article/10.1186/s12859-019-2999-7Internal ribosome entry site (IRES)BioinformaticsMachine learningXGBoost |
| spellingShingle | Junhui Wang Michael Gribskov IRESpy: an XGBoost model for prediction of internal ribosome entry sites BMC Bioinformatics Internal ribosome entry site (IRES) Bioinformatics Machine learning XGBoost |
| title | IRESpy: an XGBoost model for prediction of internal ribosome entry sites |
| title_full | IRESpy: an XGBoost model for prediction of internal ribosome entry sites |
| title_fullStr | IRESpy: an XGBoost model for prediction of internal ribosome entry sites |
| title_full_unstemmed | IRESpy: an XGBoost model for prediction of internal ribosome entry sites |
| title_short | IRESpy: an XGBoost model for prediction of internal ribosome entry sites |
| title_sort | irespy an xgboost model for prediction of internal ribosome entry sites |
| topic | Internal ribosome entry site (IRES) Bioinformatics Machine learning XGBoost |
| url | http://link.springer.com/article/10.1186/s12859-019-2999-7 |
| work_keys_str_mv | AT junhuiwang irespyanxgboostmodelforpredictionofinternalribosomeentrysites AT michaelgribskov irespyanxgboostmodelforpredictionofinternalribosomeentrysites |