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Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins after protein biosynthesis, which orchestrates a variety of biological processes. Detecting PTM sites in proteome scale is one of the key steps to in-depth understanding their regulation mechanisms. In this study, we presented an integrated method based on eXtreme Gradient Boosting (XGBoost), called iRice-MS, to identify 2-hydroxyisobutyrylation, crotonylation, malonylation, ubiquitination, succinylation and acetylation in rice. For each PTM-specific model, we adopted eight feature encoding schemes, including sequence-based features, physicochemical property-based features and spatial mapping information-based features. The optimal feature set was identified from each encoding, and their respective models were established. Extensive experimental results show that iRice-MS always display excellent performance on 5-fold cross-validation and independent dataset test. In addition, our novel approach provides the superiority to other existing tools in terms of AUC value. Based on the proposed model, a web server named iRice-MS was established and is freely accessible at http://lin-group.cn/server/iRice-MS. [ABSTRACT FROM AUTHOR]

The protein Yin Yang 1 (YY1) could form dimers that facilitate the interaction between active enhancers and promoter-proximal elements. YY1-mediated enhancer–promoter interaction is the general feature of mammalian gene control. Recently, some computational methods have been developed to characterize the interactions between DNA elements by elucidating important features of chromatin folding; however, no computational methods have been developed for identifying the YY1-mediated chromatin loops. In this study, we developed a deep learning algorithm named DeepYY1 based on word2vec to determine whether a pair of YY1 motifs would form a loop. The proposed models showed a high prediction performance (AUCs |$\ge$| 0.93) on both training datasets and testing datasets in different cell types, demonstrating that DeepYY1 has an excellent performance in the identification of the YY1-mediated chromatin loops. Our study also suggested that sequences play an important role in the formation of YY1-mediated chromatin loops. Furthermore, we briefly discussed the distribution of the replication origin site in the loops. Finally, a user-friendly web server was established, and it can be freely accessed at http://lin-group.cn/server/DeepYY1. [ABSTRACT FROM AUTHOR]

As a newly discovered protein posttranslational modification, histone lysine crotonylation (Kcr) involved in cellular regulation and human diseases. Various proteomics technologies have been developed to detect Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and labor-intensive, which is difficult to widely popularize in large-scale species. Computational approaches are cost-effective and can be used in a high-throughput manner to generate relatively precise identification. In this study, we develop a deep learning-based method termed as Deep-Kcr for Kcr sites prediction by combining sequence-based features, physicochemical property-based features and numerical space-derived information with information gain feature selection. We investigate the performances of convolutional neural network (CNN) and five commonly used classifiers (long short-term memory network, random forest, LogitBoost, naive Bayes and logistic regression) using 10-fold cross-validation and independent set test. Results show that CNN could always display the best performance with high computational efficiency on large dataset. We also compare the Deep-Kcr with other existing tools to demonstrate the excellent predictive power and robustness of our method. Based on the proposed model, a webserver called Deep-Kcr was established and is freely accessible at http://lin-group.cn/server/Deep-Kcr. [ABSTRACT FROM AUTHOR]

The locations of the initiation of genomic DNA replication are defined as origins of replication sites (ORIs), which regulate the onset of DNA replication and play significant roles in the DNA replication process. The study of ORIs is essential for understanding the cell-division cycle and gene expression regulation. Accurate identification of ORIs will provide important clues for DNA replication research and drug development by developing computational methods. In this paper, the first integrated predictor named iORI-Euk was built to identify ORIs in multiple eukaryotes and multiple cell types. In the predictor, seven eukaryotic (Homo sapiens, Mus musculus , Drosophila melanogaster , Arabidopsis thaliana , Pichia pastoris , Schizosaccharomyces pombe and Kluyveromyces lactis) ORI data was collected from public database to construct benchmark datasets. Subsequently, three feature extraction strategies which are k -mer, binary encoding and combination of k -mer and binary were used to formulate DNA sequence samples. We also compared the different classification algorithms' performance. As a result, the best results were obtained by using support vector machine in 5-fold cross-validation test and independent dataset test. Based on the optimal model, an online web server called iORI-Euk (http://lin-group.cn/server/iORI-Euk/) was established for the novel ORI identification. [ABSTRACT FROM AUTHOR]

Meiotic recombination is one of the most important driving forces of biological evolution, which is initiated by double-strand DNA breaks. Recombination has important roles in genome diversity and evolution. This review firstly provides a comprehensive survey of the 15 computational methods developed for identifying recombination hotspots in Saccharomyces cerevisiae. These computational methods were discussed and compared in terms of underlying algorithms, extracted features, predictive capability and practical utility. Subsequently, a more objective benchmark data set was constructed to develop a new predictor iRSpot-Pse6NC2.0 (http://lin-group.cn/server/iRSpot-Pse6NC2.0). To further demonstrate the generalization ability of these methods, we compared iRSpot-Pse6NC2.0 with existing methods on the chromosome XVI of S. cerevisiae. The results of the independent data set test demonstrated that the new predictor is superior to existing tools in the identification of recombination hotspots. The iRSpot-Pse6NC2.0 will become an important tool for identifying recombination hotspot. [ABSTRACT FROM AUTHOR]

The pairwise interaction between transcription factors (TFs) plays an important role in enhancer-promoter loop formation. Although thousands of TFs in the human genome have been found, only a few TF pairs have been demonstrated to be related to loop formation. It is still a challenge to determine which TF pairs could be involved in the enhancer-promoter regulation network. This work describes a computational framework to identify TF pairs in enhancer-promoter regulation. By integrating different levels of data derived from Promoter Capture Hi-C, chromatin immunoprecipitation sequencing (ChIP-seq) of histone marks, RNA-seq, protein-protein interaction (PPI), and TF motif, we identified 361 significant TF pairs and constructed a TF interaction network. From the network, we found several hub-TFs, which may have important roles in the regulation of long-range interactions. Our studies extended TF pairs identified in other experimental and computational approaches. These findings will help the further study of long-range interactions between enhancers and promoters., Competing Interests: The authors declare no competing interests., (© 2020 The Author(s).)