lncEvo: automated identification and conservation study of long noncoding RNAs

Area of Science:

• Genomics
• Computational Biology
• Evolutionary Biology

Background:

• Long noncoding RNAs (lncRNAs) are transcripts >200 nt that do not encode proteins.
• While many lncRNAs may be nonfunctional, hundreds have critical roles in gene regulation, cellular processes, and human diseases.
• Studying lncRNA evolution is crucial for understanding their biology, but traditional methods fail due to lack of sequence conservation.

Purpose of the Study:

• To develop a computational tool for studying the evolution of long noncoding RNAs (lncRNAs).
• To enable comparative analysis of lncRNA transcriptomes between species.
• To facilitate the prediction and conservation analysis of lncRNAs.

Main Methods:

• Developed lncEvo, a Nextflow-based computational pipeline.
• The pipeline integrates transcriptome assembly from RNA-Seq data.
• Includes modules for lncRNA prediction and genome-wide conservation studies, including ortholog searching.

Main Results:

• lncEvo provides a comprehensive workflow for lncRNA analysis.
• Users can utilize individual modules for transcriptome assembly or lncRNA prediction.
• The pipeline facilitates genome-wide comparison of lncRNA transcriptomes between species.

Conclusions:

• lncEvo is a versatile, all-in-one tool for lncRNA evolution studies.
• It offers customizable trade-offs between speed, sensitivity, and ease of use.
• The pipeline is freely available under the MIT license for academic and non-academic use.