Identification and function annotation of long intervening noncoding RNAs.

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• RNA-sequencing (RNA-seq) enables rapid discovery of long intervening noncoding RNAs (lincRNAs).
• Existing bioinformatics tools like Tophat and Cufflinks are primarily for RNA-seq assembly, lacking advanced lincRNA analysis capabilities.
• There is a need for sophisticated computational methods for in-depth lincRNA identification and functional prediction.

Purpose of the Study:

• To present a computational protocol for novel lincRNA identification and function prediction.
• To provide accessible bioinformatics tools for researchers without extensive training.
• To facilitate deeper understanding of lincRNA roles in biological systems.

Main Methods:

• The protocol employs two open-access tools: CNCI (Coding-Non-Coding Index) and ncFANs (non-Coding Functional Association Networks).
• CNCI distinguishes noncoding from protein-coding transcripts and identifies novel lincRNAs.
• ncFANs constructs coexpression networks using gene expression profiles to predict functions of unannotated lincRNAs.

Main Results:

• The protocol successfully identifies novel lincRNAs and predicts their functions.
• CNCI effectively differentiates coding from noncoding transcripts.
• ncFANs enables functional inference of lincRNAs through integrated expression network analysis.

Conclusions:

• This protocol offers a streamlined approach for lincRNA discovery and functional annotation using RNA-seq data.
• The integrated use of CNCI and ncFANs provides a powerful platform for lincRNA research.
• The availability of these open-access tools democratizes advanced lincRNA analysis for the scientific community.