exonlevel

Exonlevel, in genomics and transcriptomics, refers to analysis performed at the level of individual exons within genes rather than at the gene or full transcript level. Exon-level analysis quantifies reads that map to annotated exons in RNA sequencing data to assess exon inclusion or exclusion across conditions, capturing changes in splicing patterns or exon usage that may not be evident from gene-level expression alone.

Methodology typically involves aligning RNA-seq reads to a reference genome or transcriptome and counting reads that overlap annotated exons. Exon counts are then normalized for sequencing depth and other factors. Statistical methods, such as those implemented in DEXSeq or JunctionSeq, model exon counts within each gene to test for differential exon usage while accounting for overall gene expression. These approaches can indicate differential splicing events, including exon skipping or inclusion, and may be complemented by transcript-level analyses to interpret shifts in isoform composition.

Applications of exon-level analysis include discovery and characterization of alternative splicing events across developmental stages, tissues, disease states, or treatment conditions. It helps identify exons whose usage changes independently of total gene expression, providing insights into regulatory mechanisms and potential functional consequences of splicing.

Limitations and considerations include reliance on high-quality exon annotations, as overlapping or nested exons can complicate read assignment. Short reads and limited sequencing depth can reduce sensitivity, and biases such as GC content or multi-mapping reads require careful handling. Exon-level results are often integrated with transcript- and gene-level findings to produce a coherent view of gene regulation and splicing changes.

See also: alternative splicing, differential exon usage, RNA-seq, DEXSeq, JunctionSeq.