Correlation-maximizing surrogate gene space for visual mining of gene expression patterns in developing barley endosperm tissue

Network theory inspired analysis of time-resolved expression data reveals key players guiding P. patens stem cell development

Transcription factors (TFs) often trigger developmental decisions, yet, their transcripts are often only moderately regulated and thus not easily detected by conventional statistics on expression data. Here we present a method that allows to determine such genes based on trajectory analysis of time-resolved transcriptome data. As a proof of principle, we have analysed apical stem cells of filamentous moss (P. patens) protonemata that develop from leaflets upon their detachment from the plant. By our novel correlation analysis of the post detachment transcriptome kinetics we predict five out of 1,058 TFs to be involved in the signaling leading to the establishment of pluripotency. Among the predicted regulators is the basic helix loop helix TF PpRSL1, which we show to be involved in the establishment of apical stem cells in P. patens. Our methodology is expected to aid analysis of key players of developmental decisions in complex plant and animal systems.

Seed-development programs: a systems biology-based comparison between dicots and monocots

Seeds develop differently in dicots and monocots, especially with respect to the major storage organs. High-resolution transcriptome data have provided the first insights into the molecular networks and pathway interactions that function during the development of individual seed compartments. Here, we review mainly recent data obtained by systems biology-based approaches, which have allowed researchers to construct and model complex metabolic networks and fluxes and identify key limiting steps in seed development. Comparative coexpression network analyses define evolutionarily conservative (FUS3/ABI3/LEC1) and divergent (LEC2) networks in dicots and monocots. Finally, we discuss the determination of seed size--an important yield-related characteristic--as mediated by a number of processes (maternal and epigenetic factors, fine-tuned regulation of cell death in distinct seed compartments, and endosperm growth) and underlying genes defined through mutant analyses. Altogether, systems approaches can make important contributions toward a more complete and holistic knowledge of seed biology and thus support strategies for knowledge-based molecular breeding.

New dimensionality reduction methods for the representation of high dimensional 'omics' data

'Omics' data have increased very rapidly in quantity and resolution, and are increasingly recognized as very valuable experimental observations in the systematic study of biological phenomena. The increase in availability, complexity and nonexpert interest in such data requires the urgent development of accurate and efficient dimensionality reduction and visualization techniques. To illustrate this need for new approaches we extensively discuss current methodology in terms of the limitations encountered. We then illustrate a recent example of how combinations of existing techniques can be used to overcome some of the present limitations, and discuss possible future directions for research in this important field of study.

Array platforms and bioinformatics tools for the analysis of plant transcriptome in response to abiotic stress

Current microarray technologies allow high-density in situ synthesis of oligonucleotides or ex situ spotting of target molecules (cDNA) for conducting genome-wide comparative gene expression profiling studies. The avalanche of available microarray gene expression data from model plant species covering cell-related, tissue-specific, and developmental events, as well as perturbations to a variety of environmental stimuli has triggered many activities regarding the development of adequate bioinformatics tools for the analysis of these complex data sets. In this chapter we summarize the technical issues of different microarray technologies, discuss the availability of bioinformatics tools, and present approaches to extract biologically meaningful knowledge. For case studies of abiotic stress transcriptome analysis we highlight the unprecedented opportunities provided by these high-throughput technologies to understand networks of regulatory and metabolic pathway responses of plant cells to the application of abiotic stress stimuli.