Ontology engineering

news and views© 2010 Nature America, Inc. All rights reserved.The authors provide a tool, Grid analysis oftime-series expression (GATE), which usesa correlation-based clustering algorithm forthe comparison and visualization of multilayeredtime-course data sets as interactiveimages or movies 6 . Using GATE, Lu et al. 1conducted temporal and dynamic analysesamong different regulatory layers with clusteringof genes that are regulated in a similarpattern, revealing connections betweenthe regulatory mechanisms underlying EScell fate changes. Their analysis reveals thetemporal order of regulatory network configurationsafter a single perturbation, thedownregulation of Nanog.The study by Lu et al. 1 does not plumbthe full complexity of cell fate determination.Rather, it should be viewed as a first step inwhat will have to be a series of attempts toincorporate multiple data sets into a comprehensiveunderstanding of gene regulation. Luet al. 1 introduced an artificial perturbationin ES cells by knocking down a single criticalfactor. Surely, during normal development,changes in multiple regulators may occursimultaneously. The necessity to oversimplifythe regulatory problem is evident inother ways. For example, different kinds ofhistone modifications involved in positiveor negative gene regulation that are likely tobe functionally relevant were not examined.Moreover, several forms of regulation werenot studied, including post-transcriptional/translational regulation by microRNAs 7 ,post-translational modification of proteins,and modulation of protein localization.A truly comprehensive accounting of thedynamics of fate changes will require considerationof these additional regulatorylayers.Tracking cell-state transitions by multiplehigh-throughput assays, as well as the integrationof such observations in a systematicfashion, is a monumental task. Ultimately,one would like to develop ways to use thekinds of large data sets analyzed by Lu etal. 1 to predict the specific outcomes in EScells of other regulatory perturbations.Given the heterogeneity in gene expressionamong cells 8,9 , it is likely that additionaltechnologies will be needed, such as quantitativemonitoring of gene expression andof epigenetic modifications at the single-celllevel. The development of readily accessibledatabases for storing large data sets fromvarious platforms, as well as user-friendlyanalysis and visualization tools, will also benecessary to facilitate the comprehensiveunderstanding of multilayered gene regulatorynetworks during dynamic cellularprocesses.COMPETING INTERESTS STATEMENTThe authors declare no competing financial interests.1. Lu, R. et al. Nature 462, 358–362 (2009).2. MacArthur, B.D., Ma’ayan, A. & Lemischka, I.R.Cold Spring Harb. Symp. Quant. Biol. 73, 211–215(2008).3. Mitsui, K. et al. Cell 113, 631–642 (2003).4. Chambers, I. et al. Cell 113, 643–655 (2003).5. de Sousa Abreu, R., Penalva, L.O., Marcotte, E.M. &Vogel, C. Mol. Biosyst. 5, 1512–1526 (2009).6. MacArthur, B.D., Lachmann, A., Lemischka, I.R. &Ma’ayan, A. Bioinformatics 26, 143–144 (2010).7. Marson, A. et al. Cell 134, 521–533 (2008).8. Singh, A.M., Hamazaki, T., Hankowski, K.E. & Terada,N. Stem Cells 25, 2534–2542 (2007).9. Chambers, I. et al. Nature 450, 1230–1234 (2007).nature biotechnology volume 28 number 2 february 2010 147