PlantLncBoost: key features for plant lncRNA identification and significant improvement in accuracy and generalization

Summary Long noncoding RNAs (lncRNAs) are critical regulators of numerous biological processes in plants. Nevertheless, their identification is challenging due to the low sequence conservation across various species. Existing computational methods for lncRNA identification often face difficulties in...

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Vydáno v:	The New phytologist Ročník 247; číslo 3; s. 1538 - 1549
Hlavní autoři:	Tian, Xue‐Chan, Nie, Shuai, Domingues, Douglas, Rossi Paschoal, Alexandre, Jiang, Li‐Bo, Mao, Jian‐Feng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Wiley Subscription Services, Inc 01.08.2025 John Wiley and Sons Inc
Témata:	Accuracy Algorithms Biological activity Computational Biology - methods Computer applications Conserved sequence feature selection Flowers & plants Fourier transform gradient boosting algorithms Identification long noncoding RNAs (lncRNAs) Methods model selection Non-coding RNA Open Reading Frames - genetics ORF coverage Performance assessment Plant species Plants Plants - genetics RNA, Long Noncoding - genetics RNA, Messenger - genetics RNA, Plant - genetics Software Wildlife conservation model selection long noncoding RNAs (lncRNAs) Fourier transform ORF coverage gradient boosting algorithms feature selection
ISSN:	0028-646X, 1469-8137, 1469-8137
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Abstract	Summary Long noncoding RNAs (lncRNAs) are critical regulators of numerous biological processes in plants. Nevertheless, their identification is challenging due to the low sequence conservation across various species. Existing computational methods for lncRNA identification often face difficulties in generalizing across diverse plant species, highlighting the need for more robust and versatile identification models. Here, we present PlantLncBoost, a novel computational tool designed to improve the generalization in plant lncRNA identification. By integrating advanced gradient boosting algorithms with comprehensive feature selection, our approach achieves both high accuracy and generalizability. We conducted an extensive analysis of 1662 features and identified three key features – ORF coverage, complex Fourier average, and atomic Fourier amplitude – that effectively distinguish lncRNAs from mRNAs. We assessed the performance of PlantLncBoost using comprehensive datasets from 20 plant species. The model exhibited exceptional performance, with an accuracy of 96.63%, a sensitivity of 98.42%, and a specificity of 94.93%, significantly outperforming existing tools. Further analysis revealed that the features we selected effectively capture the differences between lncRNAs and mRNAs across a variety of plant species. PlantLncBoost represents a significant advancement in plant lncRNA identification. It is freely accessible on GitHub (https://github.com/xuechantian/PlantLncBoost) and has been integrated into a comprehensive analysis pipeline, Plant‐LncRNA‐pipeline v.2 (https://github.com/xuechantian/Plant‐LncRNA‐pipeline‐v2).
AbstractList	Long noncoding RNAs (lncRNAs) are critical regulators of numerous biological processes in plants. Nevertheless, their identification is challenging due to the low sequence conservation across various species. Existing computational methods for lncRNA identification often face difficulties in generalizing across diverse plant species, highlighting the need for more robust and versatile identification models. Here, we present PlantLncBoost, a novel computational tool designed to improve the generalization in plant lncRNA identification. By integrating advanced gradient boosting algorithms with comprehensive feature selection, our approach achieves both high accuracy and generalizability. We conducted an extensive analysis of 1662 features and identified three key features – ORF coverage, complex Fourier average, and atomic Fourier amplitude – that effectively distinguish lncRNAs from mRNAs. We assessed the performance of PlantLncBoost using comprehensive datasets from 20 plant species. The model exhibited exceptional performance, with an accuracy of 96.63%, a sensitivity of 98.42%, and a specificity of 94.93%, significantly outperforming existing tools. Further analysis revealed that the features we selected effectively capture the differences between lncRNAs and mRNAs across a variety of plant species. PlantLncBoost represents a significant advancement in plant lncRNA identification. It is freely accessible on GitHub (https://github.com/xuechantian/PlantLncBoost) and has been integrated into a comprehensive analysis pipeline, Plant‐LncRNA‐pipeline v.2 (https://github.com/xuechantian/Plant‐LncRNA‐pipeline‐v2). Summary Long noncoding RNAs (lncRNAs) are critical regulators of numerous biological processes in plants. Nevertheless, their identification is challenging due to the low sequence conservation across various species. Existing computational methods for lncRNA identification often face difficulties in generalizing across diverse plant species, highlighting the need for more robust and versatile identification models. Here, we present PlantLncBoost, a novel computational tool designed to improve the generalization in plant lncRNA identification. By integrating advanced gradient boosting algorithms with comprehensive feature selection, our approach achieves both high accuracy and generalizability. We conducted an extensive analysis of 1662 features and identified three key features – ORF coverage, complex Fourier average, and atomic Fourier amplitude – that effectively distinguish lncRNAs from mRNAs. We assessed the performance of PlantLncBoost using comprehensive datasets from 20 plant species. The model exhibited exceptional performance, with an accuracy of 96.63%, a sensitivity of 98.42%, and a specificity of 94.93%, significantly outperforming existing tools. Further analysis revealed that the features we selected effectively capture the differences between lncRNAs and mRNAs across a variety of plant species. PlantLncBoost represents a significant advancement in plant lncRNA identification. It is freely accessible on GitHub (https://github.com/xuechantian/PlantLncBoost) and has been integrated into a comprehensive analysis pipeline, Plant‐LncRNA‐pipeline v.2 (https://github.com/xuechantian/Plant‐LncRNA‐pipeline‐v2). Long noncoding RNAs (lncRNAs) are critical regulators of numerous biological processes in plants. Nevertheless, their identification is challenging due to the low sequence conservation across various species. Existing computational methods for lncRNA identification often face difficulties in generalizing across diverse plant species, highlighting the need for more robust and versatile identification models. Here, we present PlantLncBoost, a novel computational tool designed to improve the generalization in plant lncRNA identification. By integrating advanced gradient boosting algorithms with comprehensive feature selection, our approach achieves both high accuracy and generalizability. We conducted an extensive analysis of 1662 features and identified three key features - ORF coverage, complex Fourier average, and atomic Fourier amplitude - that effectively distinguish lncRNAs from mRNAs. We assessed the performance of PlantLncBoost using comprehensive datasets from 20 plant species. The model exhibited exceptional performance, with an accuracy of 96.63%, a sensitivity of 98.42%, and a specificity of 94.93%, significantly outperforming existing tools. Further analysis revealed that the features we selected effectively capture the differences between lncRNAs and mRNAs across a variety of plant species. PlantLncBoost represents a significant advancement in plant lncRNA identification. It is freely accessible on GitHub (https://github.com/xuechantian/PlantLncBoost) and has been integrated into a comprehensive analysis pipeline, Plant-LncRNA-pipeline v.2 (https://github.com/xuechantian/Plant-LncRNA-pipeline-v2).Long noncoding RNAs (lncRNAs) are critical regulators of numerous biological processes in plants. Nevertheless, their identification is challenging due to the low sequence conservation across various species. Existing computational methods for lncRNA identification often face difficulties in generalizing across diverse plant species, highlighting the need for more robust and versatile identification models. Here, we present PlantLncBoost, a novel computational tool designed to improve the generalization in plant lncRNA identification. By integrating advanced gradient boosting algorithms with comprehensive feature selection, our approach achieves both high accuracy and generalizability. We conducted an extensive analysis of 1662 features and identified three key features - ORF coverage, complex Fourier average, and atomic Fourier amplitude - that effectively distinguish lncRNAs from mRNAs. We assessed the performance of PlantLncBoost using comprehensive datasets from 20 plant species. The model exhibited exceptional performance, with an accuracy of 96.63%, a sensitivity of 98.42%, and a specificity of 94.93%, significantly outperforming existing tools. Further analysis revealed that the features we selected effectively capture the differences between lncRNAs and mRNAs across a variety of plant species. PlantLncBoost represents a significant advancement in plant lncRNA identification. It is freely accessible on GitHub (https://github.com/xuechantian/PlantLncBoost) and has been integrated into a comprehensive analysis pipeline, Plant-LncRNA-pipeline v.2 (https://github.com/xuechantian/Plant-LncRNA-pipeline-v2).
Author	Domingues, Douglas Tian, Xue‐Chan Jiang, Li‐Bo Nie, Shuai Rossi Paschoal, Alexandre Mao, Jian‐Feng
AuthorAffiliation	2 State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology Beijing Forestry University Beijing 100083 China 7 Department of Plant Physiology Umeå Plant Science Centre (UPSC), Umeå University Umeå 90187 Sweden 1 School of Life Sciences and Medicine Shandong University of Technology Zibo Shandong 255000 China 4 Department of Genetics, “Luiz de Queiroz” College of Agriculture University of São Paulo 13418‐900 Piracicaba Sao Paulo Brazil 3 Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of Rice Science and Technology, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co‐construction by Ministry and Province) M
AuthorAffiliation_xml	– name: 5 Bioinformatics and Pattern Recognition Group (BIOINFO‐CP), Department of Computer Science Federal University of Technology – Paraná, UTFPR Campus Cornélio Procópio Cornélio Procópio 86300‐000 Brazil – name: 4 Department of Genetics, “Luiz de Queiroz” College of Agriculture University of São Paulo 13418‐900 Piracicaba Sao Paulo Brazil – name: 6 The Rosalind Franklin Institute OX110QX Didcot UK – name: 1 School of Life Sciences and Medicine Shandong University of Technology Zibo Shandong 255000 China – name: 3 Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of Rice Science and Technology, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co‐construction by Ministry and Province) Ministry of Agriculture and Rural Affairs Guangzhou 510640 China – name: 7 Department of Plant Physiology Umeå Plant Science Centre (UPSC), Umeå University Umeå 90187 Sweden – name: 2 State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology Beijing Forestry University Beijing 100083 China
Author_xml	– sequence: 1 givenname: Xue‐Chan orcidid: 0000-0001-9023-0114 surname: Tian fullname: Tian, Xue‐Chan organization: Beijing Forestry University – sequence: 2 givenname: Shuai orcidid: 0000-0002-4832-1271 surname: Nie fullname: Nie, Shuai organization: Ministry of Agriculture and Rural Affairs – sequence: 3 givenname: Douglas orcidid: 0000-0002-1290-0853 surname: Domingues fullname: Domingues, Douglas email: dougsd@usp.br organization: University of São Paulo – sequence: 4 givenname: Alexandre orcidid: 0000-0002-8887-0582 surname: Rossi Paschoal fullname: Rossi Paschoal, Alexandre organization: The Rosalind Franklin Institute – sequence: 5 givenname: Li‐Bo orcidid: 0000-0003-4703-9220 surname: Jiang fullname: Jiang, Li‐Bo email: libojiang@sdut.edu.cn organization: Shandong University of Technology – sequence: 6 givenname: Jian‐Feng orcidid: 0000-0001-9735-8516 surname: Mao fullname: Mao, Jian‐Feng email: jianfeng.mao@umu.se organization: Umeå Plant Science Centre (UPSC), Umeå University
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Snippet	Summary Long noncoding RNAs (lncRNAs) are critical regulators of numerous biological processes in plants. Nevertheless, their identification is challenging due... Long noncoding RNAs (lncRNAs) are critical regulators of numerous biological processes in plants. Nevertheless, their identification is challenging due to the...
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SubjectTerms	Accuracy Algorithms Biological activity Computational Biology - methods Computer applications Conserved sequence feature selection Flowers & plants Fourier transform gradient boosting algorithms Identification long noncoding RNAs (lncRNAs) Methods model selection Non-coding RNA Open Reading Frames - genetics ORF coverage Performance assessment Plant species Plants Plants - genetics RNA, Long Noncoding - genetics RNA, Messenger - genetics RNA, Plant - genetics Software Wildlife conservation
Title	PlantLncBoost: key features for plant lncRNA identification and significant improvement in accuracy and generalization
URI	https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fnph.70211 https://www.ncbi.nlm.nih.gov/pubmed/40432231 https://www.proquest.com/docview/3229053804 https://www.proquest.com/docview/3212783839 https://pubmed.ncbi.nlm.nih.gov/PMC12222927 https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-240920
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