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Evaluating Plant Gene Models Using Machine Learning
Gene models are regions of the genome that can be transcribed into RNA and translated to proteins, or belong to a class of non-coding RNA genes. The prediction of gene models is a complex process that can be unreliable, leading to false positive annotations. To help support the calling of confident...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9230120/ https://www.ncbi.nlm.nih.gov/pubmed/35736770 http://dx.doi.org/10.3390/plants11121619 |
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author | Upadhyaya, Shriprabha R. Bayer, Philipp E. Tay Fernandez, Cassandria G. Petereit, Jakob Batley, Jacqueline Bennamoun, Mohammed Boussaid, Farid Edwards, David |
author_facet | Upadhyaya, Shriprabha R. Bayer, Philipp E. Tay Fernandez, Cassandria G. Petereit, Jakob Batley, Jacqueline Bennamoun, Mohammed Boussaid, Farid Edwards, David |
author_sort | Upadhyaya, Shriprabha R. |
collection | PubMed |
description | Gene models are regions of the genome that can be transcribed into RNA and translated to proteins, or belong to a class of non-coding RNA genes. The prediction of gene models is a complex process that can be unreliable, leading to false positive annotations. To help support the calling of confident conserved gene models and minimize false positives arising during gene model prediction we have developed Truegene, a machine learning approach to classify potential low confidence gene models using 14 gene and 41 protein-based characteristics. Amino acid and nucleotide sequence-based features were calculated for conserved (high confidence) and non-conserved (low confidence) annotated genes from the published Pisum sativum Cameor genome. These features were used to train eXtreme Gradient Boost (XGBoost) classifier models to predict whether a gene model is likely to be real. The optimized models demonstrated a prediction accuracy ranging from 87% to 90% and an F-1 score of 0.91–0.94. We used SHapley Additive exPlanations (SHAP) and feature importance plots to identify the features that contribute to the model predictions, and we show that protein and gene-based features can be used to build accurate models for gene prediction that have applications in supporting future gene annotation processes. |
format | Online Article Text |
id | pubmed-9230120 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92301202022-06-25 Evaluating Plant Gene Models Using Machine Learning Upadhyaya, Shriprabha R. Bayer, Philipp E. Tay Fernandez, Cassandria G. Petereit, Jakob Batley, Jacqueline Bennamoun, Mohammed Boussaid, Farid Edwards, David Plants (Basel) Communication Gene models are regions of the genome that can be transcribed into RNA and translated to proteins, or belong to a class of non-coding RNA genes. The prediction of gene models is a complex process that can be unreliable, leading to false positive annotations. To help support the calling of confident conserved gene models and minimize false positives arising during gene model prediction we have developed Truegene, a machine learning approach to classify potential low confidence gene models using 14 gene and 41 protein-based characteristics. Amino acid and nucleotide sequence-based features were calculated for conserved (high confidence) and non-conserved (low confidence) annotated genes from the published Pisum sativum Cameor genome. These features were used to train eXtreme Gradient Boost (XGBoost) classifier models to predict whether a gene model is likely to be real. The optimized models demonstrated a prediction accuracy ranging from 87% to 90% and an F-1 score of 0.91–0.94. We used SHapley Additive exPlanations (SHAP) and feature importance plots to identify the features that contribute to the model predictions, and we show that protein and gene-based features can be used to build accurate models for gene prediction that have applications in supporting future gene annotation processes. MDPI 2022-06-20 /pmc/articles/PMC9230120/ /pubmed/35736770 http://dx.doi.org/10.3390/plants11121619 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Upadhyaya, Shriprabha R. Bayer, Philipp E. Tay Fernandez, Cassandria G. Petereit, Jakob Batley, Jacqueline Bennamoun, Mohammed Boussaid, Farid Edwards, David Evaluating Plant Gene Models Using Machine Learning |
title | Evaluating Plant Gene Models Using Machine Learning |
title_full | Evaluating Plant Gene Models Using Machine Learning |
title_fullStr | Evaluating Plant Gene Models Using Machine Learning |
title_full_unstemmed | Evaluating Plant Gene Models Using Machine Learning |
title_short | Evaluating Plant Gene Models Using Machine Learning |
title_sort | evaluating plant gene models using machine learning |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9230120/ https://www.ncbi.nlm.nih.gov/pubmed/35736770 http://dx.doi.org/10.3390/plants11121619 |
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