Ultra-high throughput sequencing (HTS) is used to analyse the transcriptome or interactome at unprecedented depth on a genome-wide scale. These techniques yield short quence reads that are then mapped on a genome sequence to predict putatively transcribed or protein-interacting regions. We argue that factors such as background distribution, sequence errors, read length impact on the prediction capacity of sequence census experiments. Here we suggest a computational approach to measure these factors and analyse their influence on both transcri mic and epigenomic assays. We developped and tuned a bioinformatic pipeline to assess the expression level of known mRNAs and predict novel splicing variants based on the transcript sig ures (reads) obtained by Digital Gene Expression (DGE). However, almost 30% of the signatures map to non coding regions, suggesting the existence of unknown transcripts. To cross validate in ico those novel RNAs, we take advantage of RNA-seq, as well as other publicly available DGE data, and visualise all data in the genomic context.