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Recent high-throughput omics techniques have produced a large amount of biological data. Visualization of big omics data is essential to answer a wide range of biological problems. As a concise but comprehensive strategy, a heatmap can analyze and visualize high-dimensional and heterogeneous biomolecular expression data in an attractive artwork. In 2014, we developed a stand-alone software package, Heat map Illustrator (HemI 1.0), which implemented three clustering methods and seven distance metrics for heatmap illustration. Here, we significantly improved 1.0 and released the online service of HemI 2.0, in which 7 clustering methods and 22 types of distance metrics were implemented. In HemI 2.0, the clustering results and publication-quality heatmaps can be exported directly. For an in-depth analysis of the data, we further added an option of enrichment analysis for 12 model organisms, with 15 types of functional annotations. The enrichment results can be visualized in five idioms, including bubble chart, bar graph, coxcomb chart, pie chart and word cloud. We anticipate that HemI 2.0 can be a helpful web server for visualization of biomolecular expression data, as well as the additional enrichment analysis. HemI 2.0 is freely available for all users at: https://hemi.biocuckoo.org/.

Protein phosphorylation, catalyzed by protein kinases (PKs), is one of the most important post-translational modifications (PTMs), and involved in regulating almost all of biological processes. Here, we report an updated server, Group-based Prediction System (GPS) 6.0, for prediction of PK-specific phosphorylation sites (p-sites) in eukaryotes. First, we pre-trained a general model using penalized logistic regression (PLR), deep neural network (DNN), and Light Gradient Boosting Machine (LightGMB) on 490 762 non-redundant p-sites in 71 407 proteins. Then, transfer learning was conducted to obtain 577 PK-specific predictors at the group, family and single PK levels, using a well-curated data set of 30 043 known site-specific kinase-substrate relations in 7041 proteins. Together with the evolutionary information, GPS 6.0 could hierarchically predict PK-specific p-sites for 44046 PKs in 185 species. Besides the basic statistics, we also offered the knowledge from 22 public resources to annotate the prediction results, including the experimental evidence, physical interactions, sequence logos, and p-sites in sequences and 3D structures. The GPS 6.0 server is freely available at https://gps.biocuckoo.cn. We believe that GPS 6.0 could be a highly useful service for further analysis of phosphorylation.

Here, we reported the compendium of protein lysine modifications (CPLM 4.0, http://cplm.biocuckoo.cn/), a data resource for various post-translational modifications (PTMs) specifically occurred at the side-chain amino group of lysine residues in proteins. From the literature and public databases, we collected 450 378 protein lysine modification (PLM) events, and combined them with the existing data of our previously developed protein lysine modification database (PLMD 3.0). In total, CPLM 4.0 contained 592 606 experimentally identified modification events on 463 156 unique lysine residues of 105 673 proteins for up to 29 types of PLMs across 219 species. Furthermore, we carefully annotated the data using the knowledge from 102 additional resources that covered 13 aspects, including variation and mutation, disease-associated information, protein-protein interaction, protein functional annotation, DNA & RNA element, protein structure, chemical-target relation, mRNA expression, protein expression/proteomics, subcellular localization, biological pathway annotation, functional domain annotation, and physicochemical property. Compared to PLMD 3.0 and other existing resources, CPLM 4.0 achieved a >2-fold increase in collection of PLM events, with a data volume of ∼45GB. We anticipate that CPLM 4.0 can serve as a more useful database for further study of PLMs.