Super-enhancers (SEs) are critical for the transcriptional regulation of gene expression. We developed the super-enhancer archive version 3.0 (SEA v. 3.0, http://sea.edbc.org) to extend SE research. SEA v. 3.0 provides the most comprehensive archive to date, consisting of 164 545 super-enhancers. Of these, 80 549 are newly identified from 266 cell types/tissues/diseases using an optimized computational strategy, and 52 have been experimentally confirmed with manually curated references. We now support super-enhancers in 11 species including 7 new species (zebrafish, chicken, chimp, rhesus, sheep, Xenopus tropicalis and stickleback). To facilitate super-enhancer functional analysis, we added several new regulatory datasets including 3 361 785 typical enhancers, chromatin interactions, SNPs, transcription factor binding sites and SpCas9 target sites. We also updated or developed new criteria query, genome visualization and analysis tools for the archive. This includes a tool based on Shannon Entropy to evaluate SE cell type specificity, a new genome browser that enables the visualization of SE spatial interactions based on Hi-C data, and an enhanced enrichment analysis interface that provides online enrichment analyses of SE related genes. SEA v. 3.0 provides a comprehensive database of all available SE information across multiple species, and will facilitate super-enhancer research, especially as related to development and disease.

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Abstract. Next-generation computational sciences feature large-scale workflows of many computing modules that must be deployed and executed in distributed network environments. With limited computing resources, it is often unavoidable to map multiple workflow modules to the same computer node with possible concurrent module execution, whose scheduling may significantly affect the workflow's end-to-end performance in the network. We formulate this on-node workflow scheduling problem as an optimization problem and prove it to be NPcomplete. We then conduct a deep investigation into workflow execution dynamics and propose a Critical Path-based Priority Scheduling (CPPS) algorithm to achieve Minimum End-to-end Delay (MED) under a given workflow mapping scheme. The performance superiority of the proposed CPPS algorithm is illustrated by extensive simulation results in comparison with a traditional fair-share (FS) scheduling policy and is further verified by proof-of-concept experiments based on a real-life scientific workflow for climate modeling deployed and executed in a testbed network.