Rule-based integration of RNA-Seq analyses tools for identification of novel transcripts

Recent evidences suggest that a substantial amount of genome is transcribed more than that was anticipated, giving rise to a large number of unknown or novel transcripts. Identification of novel transcripts can provide key insights into understanding important cellular functions as well as molecular mechanisms underlying complex diseases like cancer. RNA-Seq has emerged as a powerful tool to detect novel transcripts, which previous profiling techniques failed to identify. A number of tools are available for enabling identification of novel transcripts at different levels. Read mappers such as TopHat, MapSplice, and SOAPsplice predict novel junctions, which are the indicators of novel transcripts. Cufflinks assembles novel transcripts based on alignment information and Oases performs de novo construction of transcripts. A common limitation of all these tools is prediction of sizable number of spurious or false positive (FP) novel transcripts. An approach that integrates information from all above sources and simultaneously scrutinizes FPs to correctly identify authentic novel transcripts of high confidence is proposed.