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The plant transcriptome – from integrating observations to models

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Transcriptomes as assessed by either microarrays or next-generation sequencing have produced a hirtherto 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 they produced has ...

Transcriptomes as assessed by either microarrays or next-generation sequencing have produced a hirtherto 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 they produced has progressed well beyond 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 on one hand greatly improved the coverage of transcript profiling but on the other further compounded transcriptome analysis by adding the further complexity of it being facile to differentiate 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. In particular, we would like to cover (i) gene network models, (ii) novel ways of making these data accessible
(iii) RNA Seq data processing
(iv) mechanistic models

as well as providing a prospective view as to anticipated future developments in this rapidly expanding field. Because of the diversity of the field, we welcome besides ‘Original Research’ and ‘Mini Reviews’ also ‘Hypothesis and Theory’, ‘Perspectives’, ‘Opinion’ and ‘Methods‘ papers. Manuscripts featuring computational elements would be strongly preferred but are by no means obligatory.

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