The transcriptomics field has developed rapidly with the advent of next-generation sequencing technologies. RNA-seq has now displaced microarrays as the preferred method for gene expression profiling. The comprehensive nature of the data generated has been a boon in terms of transcript identification but analysis challenges remain. Key among these problems is the development of suitable expression metrics for expression level comparisons and methods for identification of differentially expressed genes (and exons). Several approaches have been developed but as yet no consensus exists on the best pipeline to use. De novo transcriptome approaches are increasingly viable for organisms lacking a sequenced genome. The reduction in starting RNA required has enabled the development of new applications such as single cell transcriptomics. The emerging picture of mammalian transcription is complex with further refinement expected with the integration of epigenomic data generated by projects such as ENCODE.
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