Carbohydrate-active enzyme (CAZymes) are not only the most important enzymes for bioenergy and agricultural industries, but also very important for human health, in that human gut microbiota encode hundreds of CAZyme genes in their genomes for degrading various dietary and host carbohydrates. We have built an online database dbCAN-seq (http://cys.bios.niu.edu/dbCAN_seq) to provide pre-computed CAZyme sequence and annotation data for 5,349 bacterial genomes. Compared to the other CAZyme resources, dbCAN-seq has the following new features: (i) a convenient download page to allow batch download of all the sequence and annotation data; (ii) an annotation page for every CAZyme to provide the most comprehensive annotation data; (iii) a metadata page to organize the bacterial genomes according to species metadata such as disease, habitat, oxygen requirement, temperature, metabolism; (iv) a very fast tool to identify physically linked CAZyme gene clusters (CGCs) and (v) a powerful search function to allow fast and efficient data query. With these unique utilities, dbCAN-seq will become a valuable web resource for CAZyme research, with a focus complementary to dbCAN (automated CAZyme annotation server) and CAZy (CAZyme family classification and reference database).

Gene expression profiling has been widely used to characterize cell status to reflect the health of the body, to diagnose genetic diseases, etc. In recent years, although the cost of genome-wide expression profiling is gradually decreasing, the cost of collecting expression profiles for thousands of genes is still very high. Considering gene expressions are usually highly correlated in humans, the expression values of the remaining target genes can be predicted by analyzing the values of 943 landmark genes. Hence, we designed an algorithm for predicting gene expression values based on XGBoost, which integrates multiple tree models and has stronger interpretability. We tested the performance of XGBoost model on the GEO dataset and RNA-seq dataset and compared the result with other existing models. Experiments showed that the XGBoost model achieved a significantly lower overall error than the existing D-GEX algorithm, linear regression, and KNN methods. In conclusion, the XGBoost algorithm outperforms existing models and will be a significant contribution to the toolbox for gene expression value prediction.

Background: Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately.Results: We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach.Conclusion: Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNA-disease associations. The source code and data are available athttps://github.com/zhanglabNKU/VGAELDA.

Background: Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results: In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions: Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN.

In biomedical networks, molecular associations are important to understand biological processes and functions. Many computational methods, such as link prediction methods based on graph neural networks (GNNs), have been successfully applied in discovering molecular relationships with biological significance. However, it remains a challenge to explore a method that relies on representation learning of links for accurately predicting molecular associations. In this paper, we present a novel GNN based on link representation (LR-GNN) to identify potential molecular associations. LR-GNN applies a graph convolutional network (GCN)-encoder to obtain node embedding. To represent associations between molecules, we design a propagation rule that captures the node embedding of each GCN-encoder layer to construct the LR. Furthermore, the LRs of all layers are fused in output by a designed layer-wise fusing rule, which enables LR-GNN to output more accurate results. Experiments on four biomedical network data, including lncRNA-disease association, miRNA-disease association, protein–protein interaction and drug–drug interaction, show that LR-GNN outperforms state-of-the-art methods and achieves robust performance. Case studies are also presented on two datasets to verify the ability to predict unknown associations. Finally, we validate the effectiveness of the LR by visualization.

Anti-CRISPR (Acr) proteins encoded by (pro)phages/(pro)viruses have a great potential to enable a more controllable genome editing. However, genome mining new Acr proteins is challenging due to the lack of a conserved functional domain and the low sequence similarity among experimentally characterized Acr proteins. We introduce here AcrFinder, a web server (http://bcb.unl.edu/AcrFinder) that combines three well-accepted ideas used by previous experimental studies to pre-screen genomic data for Acr candidates. These ideas include homology search, guilt-by-association (GBA), and CRISPR-Cas self-targeting spacers. Compared to existing bioinformatics tools, AcrFinder has the following unique functions: (i) it is the first online server specifically mining genomes for Acr-Aca operons; (ii) it provides a most comprehensive Acr and Aca (Acr-associated regulator) database (populated by GBA-based Acr and Aca datasets); (iii) it combines homology-based, GBA-based, and self-targeting approaches in one software package; and (iv) it provides a user-friendly web interface to take both nucleotide and protein sequence files as inputs, and output a result page with graphic representation of the genomic contexts of Acr-Aca operons. The leave-one-out cross-validation on experimentally characterized Acr-Aca operons showed that AcrFinder had a 100% recall. AcrFinder will be a valuable web resource to help experimental microbiologists discover new Anti-CRISPRs.