This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2010
Detection of splicing events and multiread locations from RNA-seq data based on a geometric-tail (GT) distribution of intron length
1 School of Life Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR
2 Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR
3 School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR
4 Hong Kong Bioinformatics Center, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR
BMC Bioinformatics 2011, 12(Suppl 5):S2 doi:10.1186/1471-2105-12-S5-S2Published: 27 July 2011
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.