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Highlights d A large number of TFs and miRNAs are critical for EMT to occur d EMT involves a temporal hierarchy of transcriptional networks d Reciprocal networks between TFs and between TFs and miRNAs regulates EMT d These analyses serve as a resource for exploring gene regulation during EMT SUMMARYEpithelial-mesenchymal transition (EMT) enables cells to gain migratory and invasive features underlined by major transcriptional and epigenetic reprogramming. However, most studies have focused on the endpoints of the EMT process, and the epistatic hierarchy of the transcriptional networks underlying EMT has remained elusive. We have used a siRNAbased, functional high-content microscopy screen to identify 46 (co)transcription factors ((co)TFs) and 13 miRNAs critically required for EMT in normal murine mammary gland (NMuMG) cells. We compared dynamic gene expression during EMT kinetics and used functional perturbation of critical (co)TFs and miRNAs to identify groups and networks of EMT genes. Computational analysis as well as functional validation experiments revealed interaction networks between TFs and miRNAs and delineated the hierarchical and functional interactions of multiple EMT regulatory networks in NMuMG cells.

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.

Motivation In order to infer a cell signalling network, we generally need interventional data from perturbation experiments. If the perturbation experiments are time-resolved, then signal progression through the network can be inferred. However, such designs are infeasible for large signalling networks, where it is more common to have steady-state perturbation data on the one hand, and a non-interventional time series on the other. Such was the design in a recent experiment investigating the coordination of epithelial–mesenchymal transition (EMT) in murine mammary gland cells. We aimed to infer the underlying signalling network of transcription factors and microRNAs coordinating EMT, as well as the signal progression during EMT. Results In the context of nested effects models, we developed a method for integrating perturbation data with a non-interventional time series. We applied the model to RNA sequencing data obtained from an EMT experiment. Part of the network inferred from RNA interference was validated experimentally using luciferase reporter assays. Our model extension is formulated as an integer linear programme, which can be solved efficiently using heuristic algorithms. This extension allowed us to infer the signal progression through the network during an EMT time course, and thereby assess when each regulator is necessary for EMT to advance. Availability and implementation R package at https://github.com/cbg-ethz/timeseriesNEM. The RNA sequencing data and microscopy images can be explored through a Shiny app at https://emt.bsse.ethz.ch. Supplementary information Supplementary data are available at Bioinformatics online.