Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.

Here, we presented an integrative database named DrLLPS (http://llps.biocuckoo.cn/) for proteins involved in liquid–liquid phase separation (LLPS), which is a ubiquitous and crucial mechanism for spatiotemporal organization of various biochemical reactions, by creating membraneless organelles (MLOs) in eukaryotic cells. From the literature, we manually collected 150 scaffold proteins that are drivers of LLPS, 987 regulators that contribute in modulating LLPS, and 8148 potential client proteins that might be dispensable for the formation of MLOs, which were then categorized into 40 biomolecular condensates. We searched potential orthologs of these known proteins, and in total DrLLPS contained 437 887 known and potential LLPS-associated proteins in 164 eukaryotes. Furthermore, we carefully annotated LLPS-associated proteins in eight model organisms, by using the knowledge integrated from 110 widely used resources that covered 16 aspects, including protein disordered regions, domain annotations, post-translational modifications (PTMs), genetic variations, cancer mutations, molecular interactions, disease-associated information, drug-target relations, physicochemical property, protein functional annotations, protein expressions/proteomics, protein 3D structures, subcellular localizations, mRNA expressions, DNA & RNA elements, and DNA methylations. We anticipate DrLLPS can serve as a helpful resource for further analysis of LLPS.

The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.

As an important reversible lipid modification, S-palmitoylation mainly occurs at specific cysteine residues in proteins, participates in regulating various biological processes and is associated with human diseases. Besides experimental assays, computational prediction of S-palmitoylation sites can efficiently generate helpful candidates for further experimental consideration. Here, we reviewed the current progress in the development of S-palmitoylation site predictors, as well as training data sets, informative features and algorithms used in these tools. Then, we compiled a benchmark data set containing 3098 known S-palmitoylation sites identified from small- or large-scale experiments, and developed a new method named data quality discrimination (DQD) to distinguish data quality weights (DQWs) between the two types of the sites. Besides DQD and our previous methods, we encoded sequence similarity values into images, constructed a deep learning framework of convolutional neural networks (CNNs) and developed a novel algorithm of graphic presentation system (GPS) 6.0. We further integrated nine additional types of sequence-based and structural features, implemented parallel CNNs (pCNNs) and designed a new predictor called GPS-Palm. Compared with other existing tools, GPS-Palm showed a >31.3% improvement of the area under the curve (AUC) value (0.855 versus 0.651) for general prediction of S-palmitoylation sites. We also produced two species-specific predictors, with corresponding AUC values of 0.900 and 0.897 for predicting human- and mouse-specific sites, respectively. GPS-Palm is free for academic research at http://gpspalm.biocuckoo.cn/.

Here, we described the updated database iUUCD 2.0 (http://iuucd.biocuckoo.org/) for ubiquitin-activating enzymes (E1s), ubiquitin-conjugating enzymes (E2s), ubiquitin-protein ligases (E3s), deubiquitinating enzymes (DUBs), ubiquitin/ubiquitin-like binding domains (UBDs) and ubiquitin-like domains (ULDs), which act as key regulators in modulating ubiquitin and ubiquitin-like (UB/UBL) conjugations. In total, iUUCD 2.0 contained 136 512 UB/UBL regulators, including 1230 E1s, 5636 E2s, 93 343 E3s, 9548 DUBs, 30 173 UBDs and 11 099 ULDs in 148 eukaryotic species. In particular, we provided rich annotations for regulators of eight model organisms, especially in humans, by compiling and integrating the knowledge from nearly 70 widely used public databases that cover cancer mutations, single nucleotide polymorphisms (SNPs), mRNA expression, DNA and RNA elements, protein–protein interactions, protein 3D structures, disease-associated information, drug-target relations, post-translational modifications, DNA methylation and protein expression/proteomics. Compared with our previously developed UUCD 1.0 (∼0.41 GB), iUUCD 2.0 has a size of ∼32.1 GB of data with a >75-fold increase in data volume. We anticipate that iUUCD 2.0 can be a more useful resource for further study of UB/UBL conjugations.