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Genome-wide studies of gene expression started to move slowly from microarray technology to new generation sequencing platforms.Now,with RNA-seq,we are able to detect individual transcripts witha dynamic range.Standard libraries for RNA-seq do not preserve information about which strand was originally transcribed.In some cases,information can be inferred by computational analyses,by analyzing the open reading frame(ORF)information in protein coding genes,biases in coverage between 5and 3ends or splice-site orientation in eukaryotic genomes [1].Strand specific RNA-seq allows us to know directly from the source in which strand the RNA is being transcribed.This information improves our RNA-seq experiment,since we will be able to identify antisense transcripts [2],reverse-coding transcripts(coding on the opposite strand)and non-coding segments.We developed a software,called STELA(Strand Specific Expression Level Analysis)that takes advantage of the strand specific information,and looks for those coding regions being transcribed on the opposite strand.Using a neural network library [3],STELA is able to detect expression level signals that indicates if a region has initiated transcription or not,and using a decision algorithm,we are able to tell if every gene on the genome annotation is being coded on the opposite strand or not.Using expression coverage along the gene,STELA can also detect non-transcribed genes or genes with antisense transcripts.