Detection of splicing events and multiread locations from RNA-seq data based on a geometric-tail (GT) distribution of intron length

RNA sequencing (RNA-Seq) measures gene expression levels and permits splicing analysis. There are many aligners that provide ultra-fast mapping of millions of sequencing reads onto a reference genome. However, random assignment or removal of reads matching multiple positions along the reference genome could bias downstream analyses. Meanwhile, challenges remain in the alignment of reads spanning across splice junctions. Existing splicing-aware aligners that rely on the read-count method in identifying junction sites are inevitably affected by sequencing depths. We here proposed a new method that employs an empirical geometric-tail (GT) distribution of intron lengths to make a rational choice in multireads selection and junction sites detection.