TransLnc: a comprehensive resource for translatable lncRNAs extends immunopeptidome

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Long non-coding RNAs (lncRNAs) were traditionally considered non-coding but are increasingly recognized for their roles in cellular processes and disease.
• Emerging evidence shows lncRNAs can be translated into functional peptides, expanding their biological significance.

Purpose of the Study:

• To introduce TransLnc, a comprehensive database of experimentally supported and predicted lncRNA-derived peptides across multiple species.
• To provide insights into the coding potential of lncRNAs and their implications for fundamental cellular processes and disease, particularly cancer immunotherapy.

Main Methods:

• Integration of six types of direct and indirect evidence to support the coding potential of lncRNAs.
• Prediction of lncRNA peptide characteristics, homology relationships, and tissue-specific expression patterns.
• Computationally predicted tumor neoantigens from lncRNA-encoded peptides, including binding affinities to MHC class I and II molecules.

Main Results:

• The TransLnc database contains 583,840 peptides encoded by 33,094 lncRNAs, with 65.28% having supporting evidence.
• Data is available for 34 tissues, highlighting the tissue-specific expression of lncRNAs.
• Identified 220,791 and 237,915 candidate neoantigens binding to MHC class I and II molecules, respectively.

Conclusions:

• TransLnc serves as a valuable resource for exploring the translational capacity of lncRNAs.
• The database extends the cancer immunopeptidome and offers novel insights for cancer immunotherapy development.
• Flexible tools within TransLnc facilitate the analysis of lncRNA expression and clinical relevance in cancers.