Cytokines are critical for intercellular communication in human health and disease, but the investigation of cytokine signaling activity has remained challenging due to the short half-lives of cytokines and the complexity/redundancy of cytokine functions. To address these challenges, we developed the Cytokine Signaling Analyzer (CytoSig; https://cytosig.ccr.cancer.gov/), providing both a database of target genes modulated by cytokines and a predictive model of cytokine signaling cascades from transcriptomic profiles. We collected 20,591 transcriptome profiles for human cytokine, chemokine and growth factor responses. This atlas of transcriptional patterns induced by cytokines enabled the reliable prediction of signaling activities in distinct cell populations in infectious diseases, chronic inflammation and cancer using bulk and single-cell transcriptomic data. CytoSig revealed previously unidentified roles of many cytokines, such as BMP6 as an anti-inflammatory factor, and identified candidate therapeutic targets in human inflammatory diseases, such as CXCL8 for severe coronavirus disease 2019. NATurE METhoDSWe validated CytoSig by showing that it can reliably predict cytokine target activities in both human clinical studies and our in vivo experiments. Further, CytoSig identified CXCL8 signaling as a potential COVID-19 therapeutic target that may alleviate adverse inflammation without undermining protective immunity. ResultsThe Framework for Data Curation on public repositories. We hypothesized that the large number of cytokine treatment datasets available publicly could serve as a knowledge base to model signaling activities in diverse biological contexts. However, two hurdles must be overcome to transform this body of data into a useful model. First, the experimental design behind each published dataset is unique, requiring labor-intensive expert interpretation of the metadata and standardization of the data into a format suitable for automated analysis. Second, one must identify and exclude experiments that involve cell models, stimuli, doses or time intervals that are not physiologically relevant. More broadly, such challenges exist for many other biological topics that could be addressed by data aggregation. To overcome these hurdles, we established the FDC, which couples large-scale automatic data processing with natural language processing functions to assist expert annotation of experimental design (Methods and Fig. 1a).The FDC automatically extracts RNA-sequencing (RNA-seq) data from the Sequence Read Archive (SRA) 12 and the European Nucleotide Archive (ENA) 13 , along with automatically extracting MicroArray data from the Gene Expression Omnibus (GEO) 14 and ArrayExpress (AE) 15 . For metadata annotation, the FDC interacts with curators in iterative cycles. If the metadata structure and experimental designs differ drastically across studies, as was the case for cytokine-response data, the initial cycle of curation relies heavily on human expertise. However, based on the initial curations, the curator...

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Internal symmetry of a protein structure is the pseudo-symmetry that a single protein chain sometimes exhibits. This is in contrast to the symmetry with which monomers are arranged in many multimeric protein complexes. SymD is a program that detects proteins with internal symmetry. It proved to be useful for analyzing protein structure, function and modeling. This web-based interactive tool was developed by implementing the SymD algorithm. To the best of our knowledge, SymD webserver is the first tool of its kind with which users can easily study the symmetry of the protein they are interested in by uploading the structure or retrieving it from databases. It uses the Galaxy platform to take advantage of its extensibility and displays the symmetry properties, the symmetry axis and the sequence alignment of the structures before and after the symmetry transformation via an interactive graphical visualization environment in any modern web browser. An Example Run video displays the workflow to help users navigate. SymD webserver is publicly available at http://symd.nci.nih.gov.