Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7534
Title: Detection of splicing events and multiread locations from RNA-seq data based on a geometric-tail (GT) distribution of intron length
Authors: Lou, Shao-Ke 
Li, Jing-Woei 
Qin, Hao 
Yim, Aldrin K 
Lo, Leung-Yau 
Ni, Bing 
Prof. LEUNG Kwong Sak 
Tsui, Stephen K. 
Chan, Ting-Fung 
Issue Date: 2011
Source: BMC Bioinformatics, 2011, Vol. 12, Issue SUPPL.527, Article number S2
Journal: BMC Bioinformatics 
Abstract: Background: 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.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7534
ISSN: 14712105
DOI: 10.1186/1471-2105-12-S5-S2
Appears in Collections:Applied Data Science - Publication

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