Journal of Bioinformatics and Computational Biology

The graph of sequences represents the genetic variations of pan-genome concisely and space-efficiently than multiple linear reference genome. In order to accelerate aligning reads to the graph, an index of graph-based reference genomes is used to obtain candidate locations. However, the potential combinatorial explosion of nodes on the sequence graph leads to increasing the index space and maximum memory usage of alignment process considerably, especially for large-scale datasets. For this, existing methods typically attempt to prune complex regions, or extend the length of seeds, which sacrifices the recall of alignment algorithm despite reducing space usage slightly. We present the Sparse-index of Graph (SIG) and alignment algorithm SIG-Aligner, capable of indexing and aligning at the lower memory cost. SIG builds the non-overlapping minimizers index inside nodes of sequence graph and SIG-Aligner filters out most of the false positive matches by the method based on the pigeonhole principle. Compared to Giraffe, the results of computational experiments show that SIG achieves a significant reduction in index memory space ranging from 50% to 75% for the human pan-genome graphs, while still preserving superior or comparable accuracy of alignment and the faster alignment time.

Circular RNAs (circRNAs) are endogenous non-coding RNAs with a covalently closed loop structure. They have many biological functions, mainly regulatory ones. They have been proven to modulate protein-coding genes in the human genome. CircRNAs are linked to various diseases like Alzheimer’s disease, diabetes, atherosclerosis, Parkinson’s disease and cancer. Identifying the associations between circular RNAs and diseases is essential for disease diagnosis, prevention, and treatment. The proposed model, based on the variational autoencoder and genetic algorithm circular RNA disease association (VAGA-CDA), predicts novel circRNA-disease associations. First, the experimentally verified circRNA-disease associations are augmented with the synthetic minority oversampling technique (SMOTE) and regenerated using a variational autoencoder, and feature selection is applied to these vectors by a genetic algorithm (GA). The variational autoencoder effectively extracts features from the augmented samples. The optimized feature selection of the genetic algorithm effectively carried out dimensionality reduction. The sophisticated feature vectors extracted are then given to a Random Forest classifier to predict new circRNA-disease associations. The proposed model yields an AUC value of 0.9644 and 0.9628 under 5-fold and 10-fold cross-validations, respectively. The results of the case studies indicate the robustness of the proposed model.

Sorting signals are crucial for the anchoring of proteins to the cell surface in archaea and bacteria. These proteins often feature distinct motifs at their C-terminus, cleaved by sortase or sortase-like enzymes. Gram-positive bacteria exhibit the LPXTGX consensus motif, cleaved by sortases, while Gram-negative bacteria employ exosortases recognizing motifs like PEP. Archaea utilize exosortase homologs known as archaeosortases for signal anchoring. Traditionally identification of such C-terminal sorting signals was performed with profile Hidden Markov Models (pHMMs). The Cell-Wall PREDiction (CW-PRED) method introduced for the first time a custom-made class HMM for proteins in Gram-positive bacteria that contain a cell wall sorting signal which begins with an LPXTG motif, followed by a hydrophobic domain and a tail of positively charged residues. Here we present a new and updated version of CW-PRED for predicting C-terminal sorting signals in Archaea, Gram-positive, and Gram-negative bacteria. We used a large training set and several model enhancements that improve motif identification in order to achieve better discrimination between C-terminal signals and other proteins. Cross-validation demonstrates CW-PRED’s superiority in sensitivity and specificity compared to other methods. Application of the method in reference proteomes reveals a large number of potential surface proteins not previously identified. The method is available for academic use at http://195.251.108.230/apps.compgen.org/CW-PRED/ and as standalone software.

Polypharmacy, the use of drug combinations, is an effective approach for treating complex diseases, but it increases the risk of adverse effects. To predict novel polypharmacy side effects based on known ones, many computational methods have been proposed. However, most of them generate deterministic low-dimensional embeddings when modeling the latent space of drugs, which cannot effectively capture potential side effect associations between drugs. In this study, we present SIPSE, a novel approach for predicting polypharmacy side effects. SIPSE integrates single-drug side effect information and drug-target protein data to construct novel drug feature vectors. Leveraging a semi-implicit graph variational auto-encoder, SIPSE models known polypharmacy side effects and generates flexible latent distributions for drug nodes. SIPSE infers the current node distribution by combining the distributions of neighboring nodes with embedding noise. By sampling node embeddings from these distributions, SIPSE effectively predicts polypharmacy side effects between drugs. One key innovation of SIPSE is its incorporation of uncertainty propagation through noise embedding and neighborhood sharing, enhancing its graph analysis capabilities. Extensive experiments on a benchmark dataset of polypharmacy side effects demonstrated that SIPSE significantly outperformed five state-of-the-art methods in predicting polypharmacy side effects.

Cancer subtyping refers to categorizing a particular cancer type into distinct subtypes or subgroups based on a range of molecular characteristics, clinical manifestations, histological features, and other relevant factors. The identification of cancer subtypes can significantly enhance precision in clinical practice and facilitate personalized diagnosis and treatment strategies. Recent advancements in the field have witnessed the emergence of numerous network fusion methods aimed at identifying cancer subtypes. The majority of these fusion algorithms, however, solely rely on the fusion network of a single core matrix for the identification of cancer subtypes and fail to comprehensively capture similarity. To tackle this issue, in this study, we propose a novel cancer subtype recognition method, referred to as PCA-constrained multi-core matrix fusion network (PCA-MM-FN). The PCA-MM-FN algorithm initially employs three distinct methods to obtain three core matrices. Subsequently, the obtained core matrices are projected into a shared subspace using principal component analysis, followed by a weighted network fusion. Lastly, spectral clustering is conducted on the fused network. The results obtained from conducting experiments on the mRNA expression, DNA methylation, and miRNA expression of five TCGA datasets and three multi-omics benchmark datasets demonstrate that the proposed PCA-MM-FN approach exhibits superior accuracy in identifying cancer subtypes compared to the existing methods.

The Qingfei Paidu decoction (QFPDD) is a widely acclaimed therapeutic formula employed nationwide for the clinical management of coronavirus disease 2019 (COVID-19). QFPDD exerts a synergistic therapeutic effect, characterized by its multi-component, multi-target, and multi-pathway action. However, the intricate interactions among the ingredients and targets within QFPDD and their systematic effects in multiple tissues remain undetermined. To address this, we qualitatively characterized the chemical components of QFPDD. We integrated multi-tissue transcriptomic analysis with GraphDTA, a deep learning model, to screen for potential compound–target interactions of QFPDD in multiple tissues. We predicted 13 key active compounds, 127 potential targets and 27 pathways associated with QFPDD across six different tissues. Notably, oleanolic acid-AXL exhibited leading affinity in the heart, blood, and liver. Molecular docking and molecular dynamics simulation confirmed their strong binding affinity. The robust interaction between oleanolic acid and the AXL receptor suggests that AXL is a promising target for developing clinical intervention strategies. Through the construction of a multi-tissue compound–target interaction network, our study further elucidated the mechanisms through which QFPDD effectively combats COVID-19 in multiple tissues. Our work also establishes a framework for future investigations into the systemic effects of other Traditional Chinese Medicine (TCM) formulas in disease treatment.