TY - GEN
T1 - TrueSight
T2 - 16th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2012
AU - Li, Yang
AU - Li, Hong Mei
AU - Burns, Paul
AU - Borodovsky, Mark
AU - Robinson, Gene E.
AU - Ma, Jian
PY - 2012
Y1 - 2012
N2 - RNA-seq has proven to be a powerful technique for transcriptome profiling based on next-generation sequencing (NGS) technologies. However, due to the limited read length of NGS data, it is extremely challenging to accurately map RNA-seq reads to splice junctions, which is critically important for the analysis of alternative splicing and isoform construction. Several tools have been developed to find splice junctions by RNA-seq de novo, without the aid of gene annotations [1-3]. However, the sensitivity and specificity of these tools need to be improved. In this paper, we describe a novel method, called TrueSight, that combines information from (i) RNA-seq read mapping quality and (ii) coding potential from the reference genome sequences into a unified model that utilizes semi-supervised learning to precisely identify splice junctions.
AB - RNA-seq has proven to be a powerful technique for transcriptome profiling based on next-generation sequencing (NGS) technologies. However, due to the limited read length of NGS data, it is extremely challenging to accurately map RNA-seq reads to splice junctions, which is critically important for the analysis of alternative splicing and isoform construction. Several tools have been developed to find splice junctions by RNA-seq de novo, without the aid of gene annotations [1-3]. However, the sensitivity and specificity of these tools need to be improved. In this paper, we describe a novel method, called TrueSight, that combines information from (i) RNA-seq read mapping quality and (ii) coding potential from the reference genome sequences into a unified model that utilizes semi-supervised learning to precisely identify splice junctions.
UR - https://www.scopus.com/pages/publications/84860825821
UR - https://www.scopus.com/inward/citedby.url?scp=84860825821&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-29627-7_14
DO - 10.1007/978-3-642-29627-7_14
M3 - Conference contribution
AN - SCOPUS:84860825821
SN - 9783642296260
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 163
EP - 164
BT - Research in Computational Molecular Biology - 16th Annual International Conference, RECOMB 2012, Proceedings
Y2 - 21 April 2012 through 24 April 2012
ER -