Abstract
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.
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© 2012 Springer-Verlag Berlin Heidelberg
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Li, Y., Li, HM., Burns, P., Borodovsky, M., Robinson, G.E., Ma, J. (2012). TrueSight: Self-training Algorithm for Splice Junction Detection Using RNA-seq. In: Chor, B. (eds) Research in Computational Molecular Biology. RECOMB 2012. Lecture Notes in Computer Science(), vol 7262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29627-7_14
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DOI: https://doi.org/10.1007/978-3-642-29627-7_14
Publisher Name: Springer, Berlin, Heidelberg
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