Plant-LncPipe: a computational pipeline providing significant improvement in plant lncRNA identification

Abstract

Long non-coding RNAs (lncRNAs) play essential roles in various biological processes,such as chromatin remodeling,post-transcriptional regulation, and epigenetic modifications. Despite their critical functions in regulating plant growth, root development, and seed dormancy, the identification of plant lncRNAs remains a challenge due to the scarcity of specific and extensively tested identification methods. Most mainstream machine learning-based methods used for plant lncRNA identification were initially developed using human or other animal datasets, and their accuracy and effectiveness in predicting plant lncRNAs have not been fully evaluated or exploited. To overcome this limitation, we retrained several models, including CPAT, PLEK, and LncFinder, using plant datasets and compared their performance with mainstream lncRNA prediction tools such as CPC2, CNCI, RNAplonc, and LncADeep. Retraining these models significantly improved their performance, and two of the retrained models, LncFinder-plant and CPAT-plant, alongside their ensemble, emerged as the most suitable tools for plant lncRNA identification. This underscores the importance of model retraining in tackling the challenges associated with plant lncRNA identification. Finally, we developed a pipeline (Plant-LncPipe) that incorporates an ensemble of the two best-performing models and covers the entire data analysis process, including reads mapping, transcript assembly, lncRNA identification, classification, and origin, for the efficient identification of lncRNAs in plants. The pipeline, Plant-LncPipe, is available at: https://github.com/xuechantian/Plant-LncRNA-pipline.

Acknowledgements

We would like to acknowledge Dr Nathaniel Street from Umea Plant Science Centre (UPSC) for providing valuable language editing and insightful comments on this article. This research was supported by the National Key R&D Program of China (2022YFD2200103) and National Natural Science Foundation of China (32171816).

Author contributions

J.F.M. conceived and designed the study; X.C.T., Z.Y.C., S.N., T.L.S., X.M.Y., Y.T.B., Z.C.L., H.Y.M., K.H.J., and W.Z. prepared the data and performed related analysis; X.C.T. wrote the manuscript; J.F.M. edited and improved the manuscript; and all authors approved the final manuscript.

Data availability

Code and the data used for model construction can be found at: https://github.com/xuechantian/lncRNA-Retraining. The pipeline, Plant-LncPipe, is available at: https://github.com/xuechantian/Plant-LncRNA-pipline.

Conflict of interest

The authors declare no competing interests.

Supplementary data

Supplementary data are available at Horticulture Research online.

References

[1] Mercer TR, Dinger ME, Mattick JS. Long non-coding RNAs: insights into functions. Nat Rev Genet. 2009; 10:155-9

[2] Ponting CP, Oliver PL, Reik W. Evolution and functions of long noncoding RNAs. Cell. 2009; 136:629-41

[3] Wierzbicki AT, Blevins T, Swiezewski S. Long noncoding RNAs in plants. Annu Rev Plant Biol. 2021; 72:245-71

[4] Qin T, Zhao H, Cui P. et al. A nucleus-localized long non-coding RNA enhances drought and salt stress tolerance. Plant Physiol. 2017; 175:1321-36

[5] Derrien T, Johnson R, Bussotti G. et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Res. 2012; 22:1775-89

[6] Kang Y-J, Yang D-C, Kong L. et al. CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res. 2017; 45:W12-6

[7] Sun L, Luo H, Bu D. et al. Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts. Nucleic Acids Res. 2013; 41:e166-6

[8] Wang L, Park HJ, Dasari S. et al. CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Res. 2013; 41:e74-4

[9] Li A, Zhang J, Zhou Z. PLEK: a tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme. BMC Bioinformatics. 2014; 15:311

[10] Yang C, Yang L, Zhou M. et al. LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning. Bioinformatics. 2018; 34:3825-34

[11] Han S, Liang Y, Ma Q. et al. LncFinder: an integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property. Brief Bioinform. 2019; 20:2009-27

[12] Negri TC, Alves WAL, Bugatti PH. et al. Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants. Brief Bioinform. 2019; 20:682-9

[13] Singh U, Khemka N, Rajkumar MS. et al. PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea. Nucleic Acids Res. 2017; 45:e183

[14] Simopoulos CMA, Weretilnyk EA, Golding GB. Prediction of plant lncRNA by ensemble machine learning classifiers. BMC Genomics. 2018; 19:316

[15] Kern C, Wang Y, Chitwood J. et al. Genome-wide identification of tissue-specific long non-coding RNA in three farm animal species. BMC Genomics. 2018; 19:684

[16] Lv Y, Hu F, Zhou Y. et al. Maize transposable elements contribute to long non-coding RNAs that are regulatory hubs for abiotic stress response. BMC Genomics. 2019; 20:864

[17] Pedro DLF, Lorenzetti APR, Domingues DS. et al. PlaNC-TE: a comprehensive knowledgebase of non-coding RNAs and transposable elements in plants. Database. 2018; 2018:bay078

[18] Jha UC, Nayyar H, Jha R. et al. Long non-coding RNAs: emerging players regulating plant abiotic stress response and adaptation. BMC Plant Biol. 2020; 20:466

[19] Palos K, La Y, Railey CE. et al. Linking discoveries, mechanisms, and technologies to develop a clearer perspective on plant long noncoding RNAs. Plant Cell. 2023; 35:1762-86

[20] Paytuví Gallart A, Hermoso Pulido A, Martínez A. et al. GREENC: a Wiki-based database of plant lncRNAs. Nucleic Acids Res. 2016; 44:D1161-6

[21] Jin J, Lu P, Xu Y. et al. PLncDB V2.0: a comprehensive encyclopedia of plant long noncoding RNAs. Nucleic Acids Res. 2021; 49:D1489-95

[22] Goodstein DM, Shu S, Howson R. et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012; 40:D1178-86

[23] Szcześniak MW, Bryzghalov O, Ciomborowska-Basheer J. et al. CANTATAdb 2.0: expanding the collection of plant long noncoding RNAs. Methods Mol Biol. 2019; 1933:415-29

[24] Bolser D, Staines DM, Pritchard E. et al. Ensembl plants: integrating tools for visualizing, mining, and analyzing plant genomics data. Methods Mol Biol. 2016; 1374:115-40

[25] Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006; 22:1658-9

[26] Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015; 12:357-60

[27] Pertea M, Pertea GM, Antonescu CM. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015; 33:290-5

[28] Wucher V, Legeai F, Hédan B. et al. FEELnc: a tool for long non-coding RNA annotation and its application to the dog transcriptome. Nucleic Acids Res. 2017; 45:gkw1306-e57

[29] Uniprot Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 2018; 47:D506-15

[30] Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015; 12:59-60

[31] Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010; 26:841-2