The plant transcriptome—from integrating observations to models

Transcriptomes as assessed by either microarrays or next-generation sequencing have produced a hitherto unprecedented data flood regarding transcript identity and levels in plant systems. Microarray data has been extensively used over the last 15 years or so and evaluation of the data thus produced has progressed well beyond early statistically quality evaluation and descriptive lists to a mature science whereby gene networks and cascades have been able to provide mechanistic insight. The development of sensitive quantitative PCR for lowly expressed genes such as transcription factors has additionally allowed another layer of complexity to be accessed and the modeling of transcription factor expression with that of target genes has met considerable success. Yet more recently, data emanating from RNAseq studies have greatly improved the coverage of transcript profiling. That said, this technology further compounded transcriptome analysis by making it possible to identify differentially spliced transcripts etc. In this research topic we would like to provide an “on the fly” portrait of the use of either microarray or RNAseq based datasets in contemporary Plant Systems Biology