ICAnnoLncRNA: A Snakemake Pipeline for a Long Non-Coding-RNA Search and Annotation in Transcriptomic Sequences

Area of Science:

• Plant molecular biology
• Bioinformatics
• Genomics

Background:

• Long non-coding RNAs (lncRNAs) are crucial regulatory molecules in plants, but their identification and analysis across species remain challenging.
• Recent advancements in computational pipelines facilitate standardized and automated lncRNA data processing.
• Understanding lncRNA functions is essential for expanding knowledge in plant biology.

Purpose of the Study:

• To introduce ICAnnoLncRNA, an automated computational pipeline for the identification, classification, and annotation of plant lncRNAs.
• To provide a standardized and user-friendly tool for analyzing lncRNA candidates in assembled transcriptomic sequences.
• To facilitate deeper insights into lncRNA functions and characteristics in plants.

Main Methods:

• Development of the ICAnnoLncRNA pipeline integrating LncFinder software for lncRNA identification.
• Implementation of parameter adjustment using available genomic lncRNA annotation data.
• Inclusion of steps for candidate prediction, genome alignment, filtering, classification, database comparison, and structural/expression analysis.

Main Results:

• The ICAnnoLncRNA pipeline successfully automates the identification, classification, and annotation of plant lncRNAs.
• The pipeline effectively filters erroneous transcripts and transposable elements, ensuring data quality.
• Demonstrated application on 15 maize transcriptomic libraries assembled by Trinity and Hisat2/StringTie.

Conclusions:

• ICAnnoLncRNA provides a robust and automated solution for plant lncRNA research.
• The pipeline enhances the efficiency and accuracy of lncRNA discovery and characterization.
• This tool will accelerate the exploration of lncRNA roles in diverse plant species.