CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines
The function of long non-coding RNA (lncRNA) is largely determined by its specific location within a cell. Previous methods have used noisy datasets, including mRNA transcripts in tools intended for lncRNAs, and excluded lncRNAs lacking significant differential localization between the cytoplasm and...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Bioinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1585794/full |
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| author | Shubham Choudhury Naman Kumar Mehta Gajendra P. S. Raghava |
| author_facet | Shubham Choudhury Naman Kumar Mehta Gajendra P. S. Raghava |
| author_sort | Shubham Choudhury |
| collection | DOAJ |
| description | The function of long non-coding RNA (lncRNA) is largely determined by its specific location within a cell. Previous methods have used noisy datasets, including mRNA transcripts in tools intended for lncRNAs, and excluded lncRNAs lacking significant differential localization between the cytoplasm and nucleus. In order to overcome these shortcomings, a method has been developed for predicting cytoplasm-associated lncRNAs in 15 human cell-lines, identifying which lncRNAs are more abundant in the cytoplasm compared to the nucleus. All models in this study were trained using five-fold cross validation and tested on an validation dataset. Initially, we developed machine and deep learning based models using traditional features like composition and correlation. Using composition and correlation based features, machine learning algorithms achieved an average AUC of 0.7049 and 0.7089, respectively for 15 cell-lines. Secondly, we developed machine based models developed using embedding features obtained from the large language model DNABERT-2. The average AUC for all the cell-lines achieved by this approach was 0.665. Subsequently, we also fine-tuned DNABERT-2 on our training dataset and evaluated the fine-tuned DNABERT-2 model on the validation dataset. The fine-tuned DNABERT-2 model achieved an average AUC of 0.6336. Correlation-based features combined with ML algorithms outperform LLM-based models, in the case of predicting differential lncRNA localization. These cell-line specific models as well as web-based service are available to the public from our web server (https://webs.iiitd.edu.in/raghava/cytolncpred/). |
| format | Article |
| id | doaj-art-b13fb26954af4abdb4308e99324c1c2b |
| institution | Kabale University |
| issn | 2673-7647 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj-art-b13fb26954af4abdb4308e99324c1c2b2025-08-20T03:48:23ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472025-05-01510.3389/fbinf.2025.15857941585794CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-linesShubham ChoudhuryNaman Kumar MehtaGajendra P. S. RaghavaThe function of long non-coding RNA (lncRNA) is largely determined by its specific location within a cell. Previous methods have used noisy datasets, including mRNA transcripts in tools intended for lncRNAs, and excluded lncRNAs lacking significant differential localization between the cytoplasm and nucleus. In order to overcome these shortcomings, a method has been developed for predicting cytoplasm-associated lncRNAs in 15 human cell-lines, identifying which lncRNAs are more abundant in the cytoplasm compared to the nucleus. All models in this study were trained using five-fold cross validation and tested on an validation dataset. Initially, we developed machine and deep learning based models using traditional features like composition and correlation. Using composition and correlation based features, machine learning algorithms achieved an average AUC of 0.7049 and 0.7089, respectively for 15 cell-lines. Secondly, we developed machine based models developed using embedding features obtained from the large language model DNABERT-2. The average AUC for all the cell-lines achieved by this approach was 0.665. Subsequently, we also fine-tuned DNABERT-2 on our training dataset and evaluated the fine-tuned DNABERT-2 model on the validation dataset. The fine-tuned DNABERT-2 model achieved an average AUC of 0.6336. Correlation-based features combined with ML algorithms outperform LLM-based models, in the case of predicting differential lncRNA localization. These cell-line specific models as well as web-based service are available to the public from our web server (https://webs.iiitd.edu.in/raghava/cytolncpred/).https://www.frontiersin.org/articles/10.3389/fbinf.2025.1585794/fulllncRNAcytoplasm localizationmachine learningDNABert-2cell-line specific localization |
| spellingShingle | Shubham Choudhury Naman Kumar Mehta Gajendra P. S. Raghava CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines Frontiers in Bioinformatics lncRNA cytoplasm localization machine learning DNABert-2 cell-line specific localization |
| title | CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines |
| title_full | CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines |
| title_fullStr | CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines |
| title_full_unstemmed | CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines |
| title_short | CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines |
| title_sort | cytolncpred a computational method for predicting cytoplasm associated long non coding rnas in 15 cell lines |
| topic | lncRNA cytoplasm localization machine learning DNABert-2 cell-line specific localization |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1585794/full |
| work_keys_str_mv | AT shubhamchoudhury cytolncpredacomputationalmethodforpredictingcytoplasmassociatedlongnoncodingrnasin15celllines AT namankumarmehta cytolncpredacomputationalmethodforpredictingcytoplasmassociatedlongnoncodingrnasin15celllines AT gajendrapsraghava cytolncpredacomputationalmethodforpredictingcytoplasmassociatedlongnoncodingrnasin15celllines |