TY - JOUR
T1 - Linc2function
T2 - A Comprehensive Pipeline and Webserver for Long Non-Coding RNA (lncRNA) Identification and Functional Predictions Using Deep Learning Approaches
AU - Ramakrishnaiah, Yashpal
AU - Morris, Adam P.
AU - Dhaliwal, Jasbir
AU - Philip, Melcy
AU - Kuhlmann, Levin
AU - Tyagi, Sonika
N1 - Funding Information:
S.T. acknowledges the funding support from Monash University’s Australian Women in Research Acceleration Grant 2020 and National Health and Medical Research Council (NHMRC) (APP1126795).
Publisher Copyright:
© 2023 by the authors.
PY - 2023/9
Y1 - 2023/9
N2 - Long non-coding RNAs (lncRNAs), comprising a significant portion of the human transcriptome, serve as vital regulators of cellular processes and potential disease biomarkers. However, the function of most lncRNAs remains unknown, and furthermore, existing approaches have focused on gene-level investigation. Our work emphasizes the importance of transcript-level annotation to uncover the roles of specific transcript isoforms. We propose that understanding the mechanisms of lncRNA in pathological processes requires solving their structural motifs and interactomes. A complete lncRNA annotation first involves discriminating them from their coding counterparts and then predicting their functional motifs and target bio-molecules. Current in silico methods mainly perform primary-sequence-based discrimination using a reference model, limiting their comprehensiveness and generalizability. We demonstrate that integrating secondary structure and interactome information, in addition to using transcript sequence, enables a comprehensive functional annotation. Annotating lncRNA for newly sequenced species is challenging due to inconsistencies in functional annotations, specialized computational techniques, limited accessibility to source code, and the shortcomings of reference-based methods for cross-species predictions. To address these challenges, we developed a pipeline for identifying and annotating transcript sequences at the isoform level. We demonstrate the effectiveness of the pipeline by comprehensively annotating the lncRNA associated with two specific disease groups. The source code of our pipeline is available under the MIT licensefor local use by researchers to make new predictions using the pre-trained models or to re-train models on new sequence datasets. Non-technical users can access the pipeline through a web server setup.
AB - Long non-coding RNAs (lncRNAs), comprising a significant portion of the human transcriptome, serve as vital regulators of cellular processes and potential disease biomarkers. However, the function of most lncRNAs remains unknown, and furthermore, existing approaches have focused on gene-level investigation. Our work emphasizes the importance of transcript-level annotation to uncover the roles of specific transcript isoforms. We propose that understanding the mechanisms of lncRNA in pathological processes requires solving their structural motifs and interactomes. A complete lncRNA annotation first involves discriminating them from their coding counterparts and then predicting their functional motifs and target bio-molecules. Current in silico methods mainly perform primary-sequence-based discrimination using a reference model, limiting their comprehensiveness and generalizability. We demonstrate that integrating secondary structure and interactome information, in addition to using transcript sequence, enables a comprehensive functional annotation. Annotating lncRNA for newly sequenced species is challenging due to inconsistencies in functional annotations, specialized computational techniques, limited accessibility to source code, and the shortcomings of reference-based methods for cross-species predictions. To address these challenges, we developed a pipeline for identifying and annotating transcript sequences at the isoform level. We demonstrate the effectiveness of the pipeline by comprehensively annotating the lncRNA associated with two specific disease groups. The source code of our pipeline is available under the MIT licensefor local use by researchers to make new predictions using the pre-trained models or to re-train models on new sequence datasets. Non-technical users can access the pipeline through a web server setup.
KW - deep learning
KW - functional annotation
KW - lncRNA
KW - machine learning
KW - non-coding RNA
UR - http://www.scopus.com/inward/record.url?scp=85173595435&partnerID=8YFLogxK
U2 - 10.3390/epigenomes7030022
DO - 10.3390/epigenomes7030022
M3 - Article
C2 - 37754274
AN - SCOPUS:85173595435
SN - 2075-4655
VL - 7
JO - Epigenomes
JF - Epigenomes
IS - 3
M1 - 22
ER -