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

Research paper by Shao-Ke SK Lou, Jing-Woei JW Li, Hao H Qin, Aldrin Kay-Yuen AK Yim, Leung-Yau LY Lo, Bing B Ni, Kwong-Sak KS Leung, Stephen Kwok-Wing SK Tsui, Ting-Fung TF Chan

Indexed on: 26 Oct '11Published on: 26 Oct '11Published in: BMC Bioinformatics



Abstract

RNA sequencing (RNA-seq) measures gene expression levels and permits splicing analysis. Many existing aligners are capable of mapping millions of sequencing reads onto a reference genome. For reads that can be mapped to multiple positions along the reference genome (multireads), these aligners may either randomly assign them to a location, or discard them altogether. Either way 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.The distance between aligned positions of paired-end (PE) reads or two parts of a spliced read is dependent on the experiment protocol and gene structures. 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 splice-sites detection, according to the aligned distances from PE and sliced reads.GT models that combine sequence similarity from alignment, and together with the probability of length distribution, could accurately determine the location of both multireads and spliced reads.