HybridSucc:A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction

As an important protein acylation modification,lysine succinylation(Ksucc)is involved in diverse biological processes,and participates in human tumorigenesis.Here,we collected 26,243 non-redundant known Ksucc sites from 13 species as the benchmark data set,combined 10 types of informative features,and implemented a hybrid-learning architecture by integrating deep-learning and conventional machine-learning algorithms into a single framework.We constructed a new tool named HybridSucc,which achieved area under curve(AUC)values of 0.885 and 0.952 for general and human-specific prediction of Ksucc sites,respectively.In comparison,the accuracy of HybridSucc was 17.84%-50.62%better than that of other existing tools.Using HybridSucc,we conducted a proteome-wide prediction and prioritized 370 cancer mutations that change Ksucc states of 218 important proteins,including PKM2,SHMT2,and IDH2.We not only developed a high-profile tool for predicting Ksucc sites,but also generated useful candidates for further experimental consideration.The online service of HybridSucc can be freely accessed for academic research at http://hybridsucc.biocuckoo.org/.

WocEA:The visualization of functional enrichment results in word clouds

The integration, analysis and visualization of the big omics data are critical for addressing a broad spectrum of biological questions. One of the most frequently conducted procedures is enrichment analysis, which statistically tests whether individual functional an- notations of Gent Ontology （GO） or Kyoto Encyclopedia of Genes and Genomes （KEGG） are significantly over-or under-represented in an ＂interesting＂ gene or protein list against the reference set （Tavazoie et al., 1999）.