Recent high-throughput techniques have generated a flood of biological data in all aspects. The transformation and visualization of multi-dimensional and numerical gene or protein expression data in a single heatmap can provide a concise but comprehensive presentation of molecular dynamics under different conditions. In this work, we developed an easy-to-use tool named HemI (Heat map Illustrator), which can visualize either gene or protein expression data in heatmaps. Additionally, the heatmaps can be recolored, rescaled or rotated in a customized manner. In addition, HemI provides multiple clustering strategies for analyzing the data. Publication-quality figures can be exported directly. We propose that HemI can be a useful toolkit for conveniently visualizing and manipulating heatmaps. The stand-alone packages of HemI were implemented in Java and can be accessed at http://hemi.biocuckoo.org/down.php.

In eukaryotes, protein phosphorylation is specifically catalyzed by numerous protein kinases (PKs), faithfully orchestrates various biological processes, and reversibly determines cellular dynamics and plasticity. Here we report an updated algorithm of Group-based Prediction System (GPS) 5.0 to improve the performance for predicting kinase-specific phosphorylation sites (p-sites). Two novel methods, position weight determination (PWD) and scoring matrix optimization (SMO), were developed. Compared with other existing tools, GPS 5.0 exhibits a highly competitive accuracy. Besides serine/threonine or tyrosine kinases, GPS 5.0 also supports the prediction of dual-specificity kinase-specific p-sites. In the classical module of GPS 5.0, 617 individual predictors were constructed for predicting p-sites of 479 human PKs. To extend the application of GPS 5.0, a species-specific module was implemented to predict kinase-specific p-sites for 44,795 PKs in 161 eukaryotes. The online service and local packages of GPS 5.0 are freely available for academic research at http://gps.biocuckoo.cn.

Various posttranslational modifications (PTMs) participate in nearly all aspects of biological processes by regulating protein functions, and aberrant states of PTMs are frequently implicated in human diseases. Therefore, an integral resource of PTM–disease associations (PDAs) would be a great help for both academic research and clinical use. In this work, we reported PTMD, a well-curated database containing PTMs that are associated with human diseases. We manually collected 1950 known PDAs in 749 proteins for 23 types of PTMs and 275 types of diseases from the literature. Database analyses show that phosphorylation has the largest number of disease associations, whereas neurologic diseases have the largest number of PTM associations. We classified all known PDAs into six classes according to the PTM status in diseases and demonstrated that the upregulation and presence of PTM events account for a predominant proportion of disease-associated PTM events. By reconstructing a disease–gene network, we observed that breast cancers have the largest number of associated PTMs and AKT1 has the largest number of PTMs connected to diseases. Finally, the PTMD database was developed with detailed annotations and can be a useful resource for further analyzing the relations between PTMs and human diseases. PTMD is freely accessible at http://ptmd.biocuckoo.org.

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.

Protein acetylation catalyzed by specific histone acetyltransferases (HATs) is an essential post-translational modification (PTM) and involved in the regulation a broad spectrum of biological processes in eukaryotes. Although several ten thousands of acetylation sites have been experimentally identified, the upstream HATs for most of the sites are unclear. Thus, the identification of HAT-specific acetylation sites is fundamental for understanding the regulatory mechanisms of protein acetylation. In this work, we first collected 702 known HAT-specific acetylation sites of 205 proteins from the literature and public data resources, and a motif-based analysis demonstrated that different types of HATs exhibit similar but considerably distinct sequence preferences for substrate recognition. Using 544 human HAT-specific sites for training, we constructed a highly useful tool of GPS-PAIL for the prediction of HAT-specific sites for up to seven HATs, including CREBBP, EP300, HAT1, KAT2A, KAT2B, KAT5 and KAT8. The prediction accuracy of GPS-PAIL was critically evaluated, with a satisfying performance. Using GPS-PAIL, we also performed a large-scale prediction of potential HATs for known acetylation sites identified from high-throughput experiments in nine eukaryotes. Both online service and local packages were implemented, and GPS-PAIL is freely available at: http://pail.biocuckoo.org.