Sebnif

Sebnif identifies high-quality novel long intergenic non-coding RNAs (lincRNAs) from transcriptome RNA-seq assemblies using ab initio assembly and a self-estimation based filtering strategy that applies transcript length, expression level, coding-potential criteria, and a single-exonic detection algorithm.

Key Features:

• Self-Estimation Based Novel lincRNA Filter (SeBNiF): An algorithm tailored to identify high-quality single-exonic lincRNAs from assembled transcripts.
• Ab initio assembly: Operates on transcripts derived from ab initio assembly of RNA-seq data to discover intergenic transcripts.
• Multi-criteria filtering: Applies stringent filters based on transcript length, expression level, and coding potential to reduce assembly artifacts.
• lincRNA focus: Targets long intergenic non-coding RNAs (lincRNAs) as regulatory RNA candidates, including single-exonic species.
• Empirical validation: Applied to Human Skeletal Muscle Cells (HSkMC) RNA-seq and produced a catalog of 917 high-confidence lincRNAs.

Scientific Applications:

• Novel lincRNA discovery: Identification of bona fide novel lincRNAs from transcriptome sequencing (RNA-seq) data.
• Single-exonic lincRNA characterization: Detection and cataloging of high-quality single-exonic lincRNAs often missed by other pipelines.
• Tissue-specific catalog generation: Construction of high-confidence, tissue-relevant lincRNA catalogs, exemplified by the HSkMC dataset.

Methodology:

Assembles transcripts de novo (ab initio) from RNA-seq, applies filtering by transcript length, expression level, and coding potential, and employs a self-estimation algorithm to detect high-quality single-exonic lincRNAs; applied to HSkMC RNA-seq to yield 917 lincRNAs.