Lou, Shao-KeShao-KeLouLi, Jing-WoeiJing-WoeiLiQin, HaoHaoQinYim, Aldrin KAldrin KYimLo, Leung-YauLeung-YauLoNi, BingBingNiProf. LEUNG Kwong SakTsui, Stephen K.Stephen K.TsuiChan, Ting-FungTing-FungChan2023-03-232023-03-232011BMC Bioinformatics, 2011, Vol. 12, Issue SUPPL.527, Article number S214712105http://hdl.handle.net/20.500.11861/7534Background: 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.Results: 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.Conclusions: 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. © 2011 Lou et al; licensee BioMed Central Ltd.enSplice SiteReference GenomeSplice EventSplice JunctionIntron LengthPeer Reviewed Journal Article10.1186/1471-2105-12-S5-S2