Deep annotation of long noncoding RNAs by assembling RNA-seq and small RNA-seq data

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Long noncoding RNAs (lncRNAs) play vital roles in biological processes.
• Systematic characterization of lncRNAs is challenging due to low expression levels.

Purpose of the Study:

• To develop and validate a computational pipeline for comprehensive transcript annotation.
• To identify novel lncRNAs and understand their functions, particularly those associated with endogenous retroviruses.

Main Methods:

• Development of a novel computational pipeline: RNA-seq and small RNA-seq combined strategy (RSCS).
• Application of RSCS across diverse cellular contexts for transcript annotation.
• Validation of identified transcripts through analysis of structure, base composition, sequence complexity, and regulatory motifs.

Main Results:

• Identification of thousands of high-confidence potential novel transcripts using the RSCS.
• Verification of transcript integrity, including full-length status.
• Discovery of numerous endogenous retrovirus-associated lncRNAs.
• Identification of a novel endogenous retrovirus-lncRNA critical for Yap1 expression and early embryogenesis.

Conclusions:

• The RSCS pipeline generates a more complete and precise transcriptome annotation.
• The findings significantly expand the known transcriptome annotation for mammals.
• The study highlights the importance of lncRNAs, including novel retroviral-associated ones, in fundamental biological processes like embryogenesis.