ALBERT

Description: Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.

Description: Circular RNAs (circRNAs) are widely expressed in highly diverged eukaryotes. Although circRNAs have been known for many years, their function remains unclear. Interaction with RNA-binding protein (RBP) to influence post-transcriptional regulation is considered to be an important pathway for circRNA function, such as acting as an oncogenic RBP sponge to inhibit cancer. In this study, we design a deep learning framework, CRPBsites, to predict the binding sites of RBPs on circRNAs. In this model, the sequences of variable-length binding sites are transformed into embedding vectors by word2vec model. Bidirectional LSTM is used to encode the embedding vectors of binding sites, and then they are fed into another LSTM decoder for decoding and classification tasks. To train and test the model, we construct four datasets that contain sequences of variable-length binding sites on circRNAs, and each set corresponds to an RBP, which is overexpressed in bladder cancer tissues. Experimental results on four datasets and comparison with other existing models show that CRPBsites has superior performance. Afterwards, we found that there were highly similar binding motifs in the four binding site datasets. Finally, we applied well-trained CRPBsites to identify the binding sites of IGF2BP1 on circCDYL, and the results proved the effectiveness of this method. In conclusion, CRPBsites is an effective prediction model for circRNA-RBP interaction site identification. We hope that CRPBsites can provide valuable guidance for experimental studies on the influence of circRNA on post-transcriptional regulation.

Description: Intratumoral heterogeneity is a well-documented feature of human cancers and is associated with outcome and treatment resistance. However, a heterogeneous tumor transcriptome contributes an unknown level of variability to analyses of differentially expressed genes (DEGs) that may contribute to phenotypes of interest, including treatment response. Although current clinical practice and the vast majority of research studies use a single sample from each patient, decreasing costs of sequencing technologies and computing power have made repeated-measures analyses increasingly economical. Repeatedly sampling the same tumor increases the statistical power of DEG analysis, which is indispensable toward downstream analysis and also increases one’s understanding of within-tumor variance, which may affect conclusions. Here, we compared five different methods for analyzing gene expression profiles derived from repeated sampling of human prostate tumors in two separate cohorts of patients. We also benchmarked the sensitivity of generalized linear models to linear mixed models for identifying DEGs contributing to relevant prostate cancer pathways based on a ground-truth model.

Description: Efforts to elucidate protein–DNA interactions at the molecular level rely in part on accurate predictions of DNA-binding residues in protein sequences. While there are over a dozen computational predictors of the DNA-binding residues, they are DNA-type agnostic and significantly cross-predict residues that interact with other ligands as DNA binding. We leverage a custom-designed machine learning architecture to introduce DNAgenie, first-of-its-kind predictor of residues that interact with A-DNA, B-DNA and single-stranded DNA. DNAgenie uses a comprehensive physiochemical profile extracted from an input protein sequence and implements a two-step refinement process to provide accurate predictions and to minimize the cross-predictions. Comparative tests on an independent test dataset demonstrate that DNAgenie outperforms the current methods that we adapt to predict residue-level interactions with the three DNA types. Further analysis finds that the use of the second (refinement) step leads to a substantial reduction in the cross predictions. Empirical tests show that DNAgenie’s outputs that are converted to coarse-grained protein-level predictions compare favorably against recent tools that predict which DNA-binding proteins interact with double-stranded versus single-stranded DNAs. Moreover, predictions from the sequences of the whole human proteome reveal that the results produced by DNAgenie substantially overlap with the known DNA-binding proteins while also including promising leads for several hundred previously unknown putative DNA binders. These results suggest that DNAgenie is a valuable tool for the sequence-based characterization of protein functions. The DNAgenie’s webserver is available at http://biomine.cs.vcu.edu/servers/DNAgenie/.

Description: Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Description: Over the past decade, genome-wide assays for chromatin interactions in single cells have enabled the study of individual nuclei at unprecedented resolution and throughput. Current chromosome conformation capture techniques survey contacts for up to tens of thousands of individual cells, improving our understanding of genome function in 3D. However, these methods recover a small fraction of all contacts in single cells, requiring specialised processing of sparse interactome data. In this review, we highlight recent advances in methods for the interpretation of single-cell genomic contacts. After discussing the strengths and limitations of these methods, we outline frontiers for future development in this rapidly moving field.

Description: Good knowledge of a peptide’s tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational protocol that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5–40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9Å from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and PepLook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.

Description: Deep generative models have been an upsurge in the deep learning community since they were proposed. These models are designed for generating new synthetic data including images, videos and texts by fitting the data approximate distributions. In the last few years, deep generative models have shown superior performance in drug discovery especially de novo molecular design. In this study, deep generative models are reviewed to witness the recent advances of de novo molecular design for drug discovery. In addition, we divide those models into two categories based on molecular representations in silico. Then these two classical types of models are reported in detail and discussed about both pros and cons. We also indicate the current challenges in deep generative models for de novo molecular design. De novo molecular design automatically is promising but a long road to be explored.

Description: DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.

Description: Antimicrobial resistance (AMR) poses a threat to global public health. To mitigate the impacts of AMR, it is important to identify the molecular mechanisms of AMR and thereby determine optimal therapy as early as possible. Conventional machine learning-based drug-resistance analyses assume genetic variations to be homogeneous, thus not distinguishing between coding and intergenic sequences. In this study, we represent genetic data from Mycobacterium tuberculosis as a graph, and then adopt a deep graph learning method—heterogeneous graph attention network (‘HGAT–AMR’)—to predict anti-tuberculosis (TB) drug resistance. The HGAT–AMR model is able to accommodate incomplete phenotypic profiles, as well as provide ‘attention scores’ of genes and single nucleotide polymorphisms (SNPs) both at a population level and for individual samples. These scores encode the inputs, which the model is ‘paying attention to’ in making its drug resistance predictions. The results show that the proposed model generated the best area under the receiver operating characteristic (AUROC) for isoniazid and rifampicin (98.53 and 99.10%), the best sensitivity for three first-line drugs (94.91% for isoniazid, 96.60% for ethambutol and 90.63% for pyrazinamide), and maintained performance when the data were associated with incomplete phenotypes (i.e. for those isolates for which phenotypic data for some drugs were missing). We also demonstrate that the model successfully identifies genes and SNPs associated with drug resistance, mitigating the impact of resistance profile while considering particular drug resistance, which is consistent with domain knowledge.

Description: Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson’s r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody’s structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.

Description: Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools.

Description: Despite The Central Dogma states the destiny of gene as ‘DNA makes RNA and RNA makes protein’, the nucleic acids not only store and transmit genetic information but also, surprisingly, join in intracellular vital movement as a regulator of gene expression. Bioinformatics has contributed to knowledge for a series of emerging novel nucleic acids molecules. For typical cases, microRNA (miRNA), long noncoding RNA (lncRNA) and circular RNA (circRNA) exert crucial role in regulating vital biological processes, especially in malignant diseases. Due to extraordinarily heterogeneity among all malignancies, hepatocellular carcinoma (HCC) has emerged enormous limitation in diagnosis and therapy. Mechanistic, diagnostic and therapeutic nucleic acids for HCC emerging in past score years have been systematically reviewed. Particularly, we have organized recent advances on nucleic acids of HCC into three facets: (i) summarizing diverse nucleic acids and their modification (miRNA, lncRNA, circRNA, circulating tumor DNA and DNA methylation) acting as potential biomarkers in HCC diagnosis; (ii) concluding different patterns of three key noncoding RNAs (miRNA, lncRNA and circRNA) in gene regulation and (iii) outlining the progress of these novel nucleic acids for HCC diagnosis and therapy in clinical trials, and discuss their possibility for clinical applications. All in all, this review takes a detailed look at the advances of novel nucleic acids from potential of biomarkers and elaboration of mechanism to early clinical application in past 20 years.

Description: Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities.

Description: Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

Description: Type III secretion systems (T3SS) can be found in many pathogenic bacteria, such as Dysentery bacillus, Salmonella typhimurium, Vibrio cholera and pathogenic Escherichia coli. The routes of infection of these bacteria include the T3SS transferring a large number of type III secreted effectors (T3SE) into host cells, thereby blocking or adjusting the communication channels of the host cells. Therefore, the accurate identification of T3SEs is the precondition for the further study of pathogenic bacteria. In this article, a new T3SEs ensemble predictor was developed, which can accurately distinguish T3SEs from any unknown protein. In the course of the experiment, methods and models are strictly trained and tested. Compared with other methods, EP3 demonstrates better performance, including the absence of overfitting, strong robustness and powerful predictive ability. EP3 (an ensemble predictor that accurately identifies T3SEs) is designed to simplify the user’s (especially nonprofessional users) access to T3SEs for further investigation, which will have a significant impact on understanding the progression of pathogenic bacterial infections. Based on the integrated model that we proposed, a web server had been established to distinguish T3SEs from non-T3SEs, where have EP3_1 and EP3_2. The users can choose the model according to the species of the samples to be tested. Our related tools and data can be accessed through the link http://lab.malab.cn/∼lijing/EP3.html.

Description: Deciphering microRNA (miRNA) targets is important for understanding the function of miRNAs as well as miRNA-based diagnostics and therapeutics. Given the highly cell-specific nature of miRNA regulation, recent computational approaches typically exploit expression data to identify the most physiologically relevant target messenger RNAs (mRNAs). Although effective, those methods usually require a large sample size to infer miRNA–mRNA interactions, thus limiting their applications in personalized medicine. In this study, we developed a novel miRNA target prediction algorithm called miRACLe (miRNA Analysis by a Contact modeL). It integrates sequence characteristics and RNA expression profiles into a random contact model, and determines the target preferences by relative probability of effective contacts in an individual-specific manner. Evaluation by a variety of measures shows that fitting TargetScan, a frequently used prediction tool, into the framework of miRACLe can improve its predictive power with a significant margin and consistently outperform other state-of-the-art methods in prediction accuracy, regulatory potential and biological relevance. Notably, the superiority of miRACLe is robust to various biological contexts, types of expression data and validation datasets, and the computation process is fast and efficient. Additionally, we show that the model can be readily applied to other sequence-based algorithms to improve their predictive power, such as DIANA-microT-CDS, miRanda-mirSVR and MirTarget4. MiRACLe is publicly available at https://github.com/PANWANG2014/miRACLe.

Description: With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

Description: The fast accumulation of biological data calls for their integration, analysis and exploitation through more systematic approaches. The generation of novel, relevant hypotheses from this enormous quantity of data remains challenging. Logical models have long been used to answer a variety of questions regarding the dynamical behaviours of regulatory networks. As the number of published logical models increases, there is a pressing need for systematic model annotation, referencing and curation in community-supported and standardised formats. This article summarises the key topics and future directions of a meeting entitled ‘Annotation and curation of computational models in biology’, organised as part of the 2019 [BC]2 conference. The purpose of the meeting was to develop and drive forward a plan towards the standardised annotation of logical models, review and connect various ongoing projects of experts from different communities involved in the modelling and annotation of molecular biological entities, interactions, pathways and models. This article defines a roadmap towards the annotation and curation of logical models, including milestones for best practices and minimum standard requirements.

Description: Circular RNAs (circRNAs) are a unique class of RNA molecule identified more than 40 years ago which are produced by a covalent linkage via back-splicing of linear RNA. Recent advances in sequencing technologies and bioinformatics tools have led directly to an ever-expanding field of types and biological functions of circRNAs. In parallel with technological developments, practical applications of circRNAs have arisen including their utilization as biomarkers of human disease. Currently, circRNA-associated bioinformatics tools can support projects including circRNA annotation, circRNA identification and network analysis of competing endogenous RNA (ceRNA). In this review, we collected about 100 circRNA-associated bioinformatics tools and summarized their current attributes and capabilities. We also performed network analysis and text mining on circRNA tool publications in order to reveal trends in their ongoing development.

Description: Motivation MircroRNAs (miRNAs) regulate target genes and are responsible for lethal diseases such as cancers. Accurately recognizing and identifying miRNA and gene pairs could be helpful in deciphering the mechanism by which miRNA affects and regulates the development of cancers. Embedding methods and deep learning methods have shown their excellent performance in traditional classification tasks in many scenarios. But not so many attempts have adapted and merged these two methods into miRNA–gene relationship prediction. Hence, we proposed a novel computational framework. We first generated representational features for miRNAs and genes using both sequence and geometrical information and then leveraged a deep learning method for the associations’ prediction. Results We used long short-term memory (LSTM) to predict potential relationships and proved that our method outperformed other state-of-the-art methods. Results showed that our framework SG-LSTM got an area under curve of 0.94 and was superior to other methods. In the case study, we predicted the top 10 miRNA–gene relationships and recommended the top 10 potential genes for hsa-miR-335-5p for SG-LSTM-core. We also tested our model using a larger dataset, from which 14 668 698 miRNA–gene pairs were predicted. The top 10 unknown pairs were also listed. Availability Our work can be download in https://github.com/Xshelton/SG_LSTM Contact luojiawei@hnu.edu.cn Supplementary information Supplementary data are available at Briefings in Bioinformatics online.

Description: 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.

Description: How to accurately estimate protein–ligand binding affinity remains a key challenge in computer-aided drug design (CADD). In many cases, it has been shown that the binding affinities predicted by classical scoring functions (SFs) cannot correlate well with experimentally measured biological activities. In the past few years, machine learning (ML)-based SFs have gradually emerged as potential alternatives and outperformed classical SFs in a series of studies. In this study, to better recognize the potential of classical SFs, we have conducted a comparative assessment of 25 commonly used SFs. Accordingly, the scoring power was systematically estimated by using the state-of-the-art ML methods that replaced the original multiple linear regression method to refit individual energy terms. The results show that the newly-developed ML-based SFs consistently performed better than classical ones. In particular, gradient boosting decision tree (GBDT) and random forest (RF) achieved the best predictions in most cases. The newly-developed ML-based SFs were also tested on another benchmark modified from PDBbind v2007, and the impacts of structural and sequence similarities were evaluated. The results indicated that the superiority of the ML-based SFs could be fully guaranteed when sufficient similar targets were contained in the training set. Moreover, the effect of the combinations of features from multiple SFs was explored, and the results indicated that combining NNscore2.0 with one to four other classical SFs could yield the best scoring power. However, it was not applicable to derive a generic target-specific SF or SF combination.

Description: Enhancer-promoter interactions (EPIs) play an important role in transcriptional regulation. Recently, machine learning-based methods have been widely used in the genome-scale identification of EPIs due to their promising predictive performance. In this paper, we propose a novel method, termed EPI-DLMH, for predicting EPIs with the use of DNA sequences only. EPI-DLMH consists of three major steps. First, a two-layer convolutional neural network is used to learn local features, and an bidirectional gated recurrent unit network is used to capture long-range dependencies on the sequences of promoters and enhancers. Second, an attention mechanism is used for focusing on relatively important features. Finally, a matching heuristic mechanism is introduced for the exploration of the interaction between enhancers and promoters. We use benchmark datasets in evaluating and comparing the proposed method with existing methods. Comparative results show that our model is superior to currently existing models in multiple cell lines. Specifically, we found that the matching heuristic mechanism introduced into the proposed model mainly contributes to the improvement of performance in terms of overall accuracy. Additionally, compared with existing models, our model is more efficient with regard to computational speed.

Description: Epigenome-wide mediation analysis aims to identify DNA methylation CpG sites that mediate the causal effects of genetic/environmental exposures on health outcomes. However, DNA methylations in the peripheral blood tissues are usually measured at the bulk level based on a heterogeneous population of white blood cells. Using the bulk level DNA methylation data in mediation analysis might cause confounding bias and reduce study power. Therefore, it is crucial to get fine-grained results by detecting mediation CpG sites in a cell-type-specific way. However, there is a lack of methods and software to achieve this goal. We propose a novel method (Mediation In a Cell-type-Specific fashion, MICS) to identify cell-type-specific mediation effects in genome-wide epigenetic studies using only the bulk-level DNA methylation data. MICS follows the standard mediation analysis paradigm and consists of three key steps. In step1, we assess the exposure-mediator association for each cell type; in step 2, we assess the mediator-outcome association for each cell type; in step 3, we combine the cell-type-specific exposure-mediator and mediator-outcome associations using a multiple testing procedure named MultiMed [Sampson JN, Boca SM, Moore SC, et al. FWER and FDR control when testing multiple mediators. Bioinformatics 2018;34:2418–24] to identify significant CpGs with cell-type-specific mediation effects. We conduct simulation studies to demonstrate that our method has correct FDR control. We also apply the MICS procedure to the Normative Aging Study and identify nine DNA methylation CpG sites in the lymphocytes that might mediate the effect of cigarette smoking on the lung function.

Description: Motivation Identifying microRNAs that are associated with different diseases as biomarkers is a problem of great medical significance. Existing computational methods for uncovering such microRNA-diseases associations (MDAs) are mostly developed under the assumption that similar microRNAs tend to associate with similar diseases. Since such an assumption is not always valid, these methods may not always be applicable to all kinds of MDAs. Considering that the relationship between long noncoding RNA (lncRNA) and different diseases and the co-regulation relationships between the biological functions of lncRNA and microRNA have been established, we propose here a multiview multitask method to make use of the known lncRNA–microRNA interaction to predict MDAs on a large scale. The investigation is performed in the absence of complete information of microRNAs and any similarity measurement for it and to the best knowledge, the work represents the first ever attempt to discover MDAs based on lncRNA–microRNA interactions. Results In this paper, we propose to develop a deep learning model called MVMTMDA that can create a multiview representation of microRNAs. The model is trained based on an end-to-end multitasking approach to machine learning so that, based on it, missing data in the side information can be determined automatically. Experimental results show that the proposed model yields an average area under ROC curve of 0.8410+/−0.018, 0.8512+/−0.012 and 0.8521+/−0.008 when k is set to 2, 5 and 10, respectively. In addition, we also propose here a statistical approach to predicting lncRNA-disease associations based on these associations and the MDA discovered using MVMTMDA. Availability Python code and the datasets used in our studies are made available at https://github.com/yahuang1991polyu/MVMTMDA/.

Description: Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drugs and their side effects or classifying drug–drug interaction. Motivated by technological advancements and the availability of big data, we provide a review on the detection and classification of side effects using deep learning approaches. It is shown that the effective integration of heterogeneous, multidimensional drug data sources, together with the innovative deployment of deep learning approaches, helps reduce or prevent the occurrence of adverse drug reactions (ADRs). Deep learning approaches can also be exploited to find replacements for drugs which have side effects or help to diversify the utilization of drugs through drug repurposing.

Description: Motivation The emergence of abundant biological networks, which benefit from the development of advanced high-throughput techniques, contributes to describing and modeling complex internal interactions among biological entities such as genes and proteins. Multiple networks provide rich information for inferring the function of genes or proteins. To extract functional patterns of genes based on multiple heterogeneous networks, network embedding-based methods, aiming to capture non-linear and low-dimensional feature representation based on network biology, have recently achieved remarkable performance in gene function prediction. However, existing methods do not consider the shared information among different networks during the feature learning process. Results Taking the correlation among the networks into account, we design a novel semi-supervised autoencoder method to integrate multiple networks and generate a low-dimensional feature representation. Then we utilize a convolutional neural network based on the integrated feature embedding to annotate unlabeled gene functions. We test our method on both yeast and human datasets and compare with three state-of-the-art methods. The results demonstrate the superior performance of our method. We not only provide a comprehensive analysis of the performance of the newly proposed algorithm but also provide a tool for extracting features of genes based on multiple networks, which can be used in the downstream machine learning task. Availability DeepMNE-CNN is freely available at https://github.com/xuehansheng/DeepMNE-CNN Contact jiajiepeng@nwpu.edu.cn; shang@nwpu.edu.cn; jianye.hao@tju.edu.cn

Description: Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.

Description: Genome-wide association studies (GWAS) using longitudinal phenotypes collected over time is appealing due to the improvement of power. However, computation burden has been a challenge because of the complex algorithms for modeling the longitudinal data. Approximation methods based on empirical Bayesian estimates (EBEs) from mixed-effects modeling have been developed to expedite the analysis. However, our analysis demonstrated that bias in both association test and estimation for the existing EBE-based methods remains an issue. We propose an incredibly fast and unbiased method (simultaneous correction for EBE, SCEBE) that can correct the bias in the naive EBE approach and provide unbiased P-values and estimates of effect size. Through application to Alzheimer’s Disease Neuroimaging Initiative data with 6 414 695 single nucleotide polymorphisms, we demonstrated that SCEBE can efficiently perform large-scale GWAS with longitudinal outcomes, providing nearly 10 000 times improvement of computational efficiency and shortening the computation time from months to minutes. The SCEBE package and the example datasets are available at https://github.com/Myuan2019/SCEBE.

Description: Infection with human cytomegalovirus (HCMV) can cause severe complications in immunocompromised individuals and congenitally infected children. Characterizing heterogeneous viral populations and their evolution by high-throughput sequencing of clinical specimens requires the accurate assembly of individual strains or sequence variants and suitable variant calling methods. However, the performance of most methods has not been assessed for populations composed of low divergent viral strains with large genomes, such as HCMV. In an extensive benchmarking study, we evaluated 15 assemblers and 6 variant callers on 10 lab-generated benchmark data sets created with two different library preparation protocols, to identify best practices and challenges for analyzing such data. Most assemblers, especially metaSPAdes and IVA, performed well across a range of metrics in recovering abundant strains. However, only one, Savage, recovered low abundant strains and in a highly fragmented manner. Two variant callers, LoFreq and VarScan2, excelled across all strain abundances. Both shared a large fraction of false positive variant calls, which were strongly enriched in T to G changes in a ‘G.G’ context. The magnitude of this context-dependent systematic error is linked to the experimental protocol. We provide all benchmarking data, results and the entire benchmarking workflow named QuasiModo, Quasispecies Metric determination on omics, under the GNU General Public License v3.0 (https://github.com/hzi-bifo/Quasimodo), to enable full reproducibility and further benchmarking on these and other data.

Description: Motivation MicroRNAs (miRNAs) are small noncoding RNAs that play important roles in gene regulation and phenotype development. The identification of miRNA transcription start sites (TSSs) is critical to understand the functional roles of miRNA genes and their transcriptional regulation. Unlike protein-coding genes, miRNA TSSs are not directly detectable from conventional RNA-Seq experiments due to miRNA-specific process of biogenesis. In the past decade, large-scale genome-wide TSS-Seq and transcription activation marker profiling data have become available, based on which, many computational methods have been developed. These methods have greatly advanced genome-wide miRNA TSS annotation. Results In this study, we summarized recent computational methods and their results on miRNA TSS annotation. We collected and performed a comparative analysis of miRNA TSS annotations from 14 representative studies. We further compiled a robust set of miRNA TSSs (RSmirT) that are supported by multiple studies. Integrative genomic and epigenomic data analysis on RSmirT revealed the genomic and epigenomic features of miRNA TSSs as well as their relations to protein-coding and long non-coding genes. Contact xiaoman@mail.ucf.edu, haihu@cs.ucf.edu

Description: Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve the effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3 and LIGER. Furthermore, SMNN retains more cell-type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841.0%.

Description: Machine learning-based scoring functions (MLSFs) have attracted extensive attention recently and are expected to be potential rescoring tools for structure-based virtual screening (SBVS). However, a major concern nowadays is whether MLSFs trained for generic uses rather than a given target can consistently be applicable for VS. In this study, a systematic assessment was carried out to re-evaluate the effectiveness of 14 reported MLSFs in VS. Overall, most of these MLSFs could hardly achieve satisfactory results for any dataset, and they could even not outperform the baseline of classical SFs such as Glide SP. An exception was observed for RFscore-VS trained on the Directory of Useful Decoys-Enhanced dataset, which showed its superiority for most targets. However, in most cases, it clearly illustrated rather limited performance on the targets that were dissimilar to the proteins in the corresponding training sets. We also used the top three docking poses rather than the top one for rescoring and retrained the models with the updated versions of the training set, but only minor improvements were observed. Taken together, generic MLSFs may have poor generalization capabilities to be applicable for the real VS campaigns. Therefore, it should be quite cautious to use this type of methods for VS.

Description: Motivation Although gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected datasets and biological reasoning on the relevance of resulting enriched gene sets. Results We develop an extensible framework for reproducible benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization and detection of relevant processes. This framework incorporates a curated compendium of 75 expression datasets investigating 42 human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods, identifying significant differences in runtime and applicability to RNA-seq data, fraction of enriched gene sets depending on the null hypothesis tested and recovery of the predefined relevance rankings. We make practical recommendations on how methods originally developed for microarray data can efficiently be applied to RNA-seq data, how to interpret results depending on the type of gene set test conducted and which methods are best suited to effectively prioritize gene sets with high phenotype relevance. Availability http://bioconductor.org/packages/GSEABenchmarkeR Contact ludwig.geistlinger@sph.cuny.edu

Description: Gene expression profiling holds great potential as a new approach to histological diagnosis and precision medicine of cancers of unknown primary (CUP). Batch effects and different data types greatly decrease the predictive performance of biomarker-based algorithms, and few methods have been widely applied to identify tissue origin of CUP up to now. To address this problem and assist in more precise diagnosis, we have developed a gene expression rank-based majority vote algorithm for tissue origin diagnosis of CUP (TOD-CUP) of most common cancer types. Based on massive tissue-specific RNA-seq data sets (10 553) found in The Cancer Genome Atlas (TCGA), 538 feature genes (biomarkers) were selected based on their gene expression ranks and used to predict tissue types. The top scoring pairs (TSPs) classifier of the tumor type was optimized by the TCGA training samples. To test the prediction accuracy of our TOD-CUP algorithm, we analyzed (1) two microarray data sets (1029 Agilent and 2277 Affymetrix/Illumina chips) and found 91% and 94% prediction accuracy, respectively, (2) RNA-seq data from five cancer types derived from 141 public metastatic cancer tumor samples and achieved 94% accuracy and (3) a total of 25 clinical cancer samples (including 14 metastatic cancer samples) were able to classify 24/25 samples correctly (96.0% accuracy). Taken together, the TOD-CUP algorithm provides a powerful and robust means to accurately identify the tissue origin of 24 cancer types across different data platforms. To make the TOD-CUP algorithm easily accessible for clinical application, we established a Web-based server for tumor tissue origin diagnosis (http://ibi. zju.edu.cn/todcup/).

Description: Motivation With the spreading of biological and clinical uses of next-generation sequencing (NGS) data, many laboratories and health organizations are facing the need of sharing NGS data resources and easily accessing and processing comprehensively shared genomic data; in most cases, primary and secondary data management of NGS data is done at sequencing stations, and sharing applies to processed data. Based on the previous single-instance GMQL system architecture, here we review the model, language and architectural extensions that make the GMQL centralized system innovatively open to federated computing. Results A well-designed extension of a centralized system architecture to support federated data sharing and query processing. Data is federated thanks to simple data sharing instructions. Queries are assigned to execution nodes; they are translated into an intermediate representation, whose computation drives data and processing distributions. The approach allows writing federated applications according to classical styles: centralized, distributed or externalized. Availability The federated genomic data management system is freely available for non-commercial use as an open source project at http://www.bioinformatics.deib.polimi.it/FederatedGMQLsystem/ Contact {arif.canakoglu, pietro.pinoli}@polimi.it Summary

Description: Messenger RNAs (mRNAs) shoulder special responsibilities that transmit genetic code from DNA to discrete locations in the cytoplasm. The locating process of mRNA might provide spatial and temporal regulation of mRNA and protein functions. The situ hybridization and quantitative transcriptomics analysis could provide detail information about mRNA subcellular localization; however, they are time consuming and expensive. It is highly desired to develop computational tools for timely and effectively predicting mRNA subcellular location. In this work, by using binomial distribution and one-way analysis of variance, the optimal nonamer composition was obtained to represent mRNA sequences. Subsequently, a predictor based on support vector machine was developed to identify the mRNA subcellular localization. In 5-fold cross-validation, results showed that the accuracy is 90.12% for Homo sapiens (H. sapiens). The predictor may provide a reference for the study of mRNA localization mechanisms and mRNA translocation strategies. An online web server was established based on our models, which is available at http://lin-group.cn/server/iLoc-mRNA/.

Description: Reversible post-translational modification (PTM) orchestrates various biological processes by changing the properties of proteins. Since many proteins are multiply modified by PTMs, identification of PTM crosstalk site has emerged to be an intriguing topic and attracted much attention. In this study, we systematically deciphered the in situ crosstalk of ubiquitylation and SUMOylation that co-occurs on the same lysine residue. We first collected 3363 ubiquitylation-SUMOylation (UBS) crosstalk site on 1302 proteins and then investigated the prime sequence motifs, the local evolutionary degree and the distribution of structural annotations at the residue and sequence levels between the UBS crosstalk and the single modification sites. Given the properties of UBS crosstalk sites, we thus developed the mUSP classifier to predict UBS crosstalk site by integrating different types of features with two-step feature optimization by recursive feature elimination approach. By using various cross-validations, the mUSP model achieved an average area under the curve (AUC) value of 0.8416, indicating its promising accuracy and robustness. By comparison, the mUSP has significantly better performance with the improvement of 38.41 and 51.48% AUC values compared to the cross-results by the previous single predictor. The mUSP was implemented as a web server available at http://bioinfo.ncu.edu.cn/mUSP/index.html to facilitate the query of our high-accuracy UBS crosstalk results for experimental design and validation.

Description: Protein complexes are key units for studying a cell system. During the past decades, the genome-scale protein–protein interaction (PPI) data have been determined by high-throughput approaches, which enables the identification of protein complexes from PPI networks. However, the high-throughput approaches often produce considerable fraction of false positive and negative samples. In this study, we propose the mutual important interacting partner relation to reflect the co-complex relationship of two proteins based on their interaction neighborhoods. In addition, a new algorithm called idenPC-MIIP is developed to identify protein complexes from weighted PPI networks. The experimental results on two widely used datasets show that idenPC-MIIP outperforms 17 state-of-the-art methods, especially for identification of small protein complexes with only two or three proteins.

Description: It has been increasingly accepted that microRNA (miRNA) can both activate and suppress gene expression, directly or indirectly, under particular circumstances. Yet, a systematic study on the switch in their interaction pattern between activation and suppression and between normal and cancer conditions based on multi-omics evidences is not available. We built miRactDB, a database for miRNA–gene interaction, at https://ccsm.uth.edu/miRactDB, to provide a versatile resource and platform for annotation and interpretation of miRNA–gene relations. We conducted a comprehensive investigation on miRNA–gene interactions and their biological implications across tissue types in both tumour and normal conditions, based on TCGA, CCLE and GTEx databases. We particularly explored the genetic and epigenetic mechanisms potentially contributing to the positive correlation, including identification of miRNA binding sites in the gene coding sequence (CDS) and promoter regions of partner genes. Integrative analysis based on this resource revealed that top-ranked genes derived from TCGA tumour and adjacent normal samples share an overwhelming part of biological processes, which are quite different than those from CCLE and GTEx. The most active miRNAs predicted to target CDS and promoter regions are largely overlapped. These findings corroborate that adjacent normal tissues might have undergone significant molecular transformations towards oncogenesis before phenotypic and histological change; and there probably exists a small yet critical set of miRNAs that profoundly influence various cancer hallmark processes. miRactDB provides a unique resource for the cancer and genomics communities to screen, prioritize and rationalize their candidates of miRNA–gene interactions, in both normal and cancer scenarios.

Description: Recent advances in transcriptomics have uncovered lots of novel transcripts in plants. To annotate such transcripts, dissecting their coding potential is a critical step. Computational approaches have been proven fruitful in this task; however, most current tools are designed/optimized for mammals and only a few of them have been tested on a limited number of plant species. In this work, we present NAMS webserver, which contains a novel coding potential classifier, NAMS, specifically optimized for plants. We have evaluated the performance of NAMS using a comprehensive dataset containing more than 3 million transcripts from various plant species, where NAMS demonstrates high accuracy and remarkable performance improvements over state-of-the-art software. Moreover, our webserver also furnishes functional annotations, aiming to provide users informative clues to the functions of their transcripts. Considering that most plant species are poorly characterized, our NAMS webserver could serve as a valuable resource to facilitate the transcriptomic studies. The webserver with testing dataset is freely available at http://sunlab.cpy.cuhk.edu.hk/NAMS/.

Description: MicroRNAs (miRNAs) play crucial roles in multifarious biological processes associated with human diseases. Identifying potential miRNA-disease associations contributes to understanding the molecular mechanisms of miRNA-related diseases. Most of the existing computational methods mainly focus on predicting whether a miRNA-disease association exists or not. However, the roles of miRNAs in diseases are prominently diverged, for instance, Genetic variants of miRNA (mir-15) may affect the expression level of miRNAs leading to B cell chronic lymphocytic leukemia, while circulating miRNAs (including mir-1246, mir-1307-3p, etc.) have potentials to detecting breast cancer in the early stage. In this paper, we aim to predict multi-type miRNA-disease associations instead of taking them as binary. To this end, we innovatively represent miRNA-disease-type triples as a tensor and introduce tensor decomposition methods to solve the prediction task. Experimental results on two widely-adopted miRNA-disease datasets: HMDD v2.0 and HMDD v3.2 show that tensor decomposition methods improve a recent baseline in a large scale (up to $38\%$ in Top-1F1). We then propose a novel method, Tensor Decomposition with Relational Constraints (TDRC), which incorporates biological features as relational constraints to further the existing tensor decomposition methods. Compared with two existing tensor decomposition methods, TDRC can produce better performance while being more efficient.

Description: Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.

Description: Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski’s rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.

Description: Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent need for medicines that can help before vaccines are available. In this study, we present a viral-associated disease-specific chemogenomics knowledgebase (Virus-CKB) and apply our computational systems pharmacology-target mapping to rapidly predict the FDA-approved drugs which can quickly progress into clinical trials to meet the urgent demand of the COVID-19 outbreak. Virus-CKB reuses the underlying platform of our DAKB-GPCRs but adds new features like multiple-compound support, multi-cavity protein support and customizable symbol display. Our one-stop computing platform describes the chemical molecules, genes and proteins involved in viral-associated diseases regulation. To date, Virus-CKB archived 65 antiviral drugs in the market, 107 viral-related targets with 189 available 3D crystal or cryo-EM structures and 2698 chemical agents reported for these target proteins. Moreover, Virus-CKB is implemented with web applications for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, NGL Viewer, Spider Plot, etc. The Virus-CKB server is accessible at https://www.cbligand.org/g/virus-ckb.

Description: Background: Determining drug–disease associations is an integral part in the process of drug development. However, the identification of drug–disease associations through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for predicting drug–disease associations is of great significance. Results: In this paper, we propose a novel computational method named as layer attention graph convolutional network (LAGCN) for the drug–disease association prediction. Specifically, LAGCN first integrates the known drug–disease associations, drug–drug similarities and disease–disease similarities into a heterogeneous network, and applies the graph convolution operation to the network to learn the embeddings of drugs and diseases. Second, LAGCN combines the embeddings from multiple graph convolution layers using an attention mechanism. Third, the unobserved drug–disease associations are scored based on the integrated embeddings. Evaluated by 5-fold cross-validations, LAGCN achieves an area under the precision–recall curve of 0.3168 and an area under the receiver–operating characteristic curve of 0.8750, which are better than the results of existing state-of-the-art prediction methods and baseline methods. The case study shows that LAGCN can discover novel associations that are not curated in our dataset. Conclusion: LAGCN is a useful tool for predicting drug–disease associations. This study reveals that embeddings from different convolution layers can reflect the proximities of different orders, and combining the embeddings by the attention mechanism can improve the prediction performances.

Description: Angiosarcomas are soft-tissue sarcomas that form malignant vascular tissues. Angiosarcomas are very rare, and due to their aggressive behavior and high metastatic propensity, they have poor clinical outcomes. Hemangiosarcomas commonly occur in domestic dogs, and share pathological and clinical features with human angiosarcomas. Typical pathognomonic features of this tumor are irregular vascular channels that are filled with blood and are lined by a mixture of malignant and nonmalignant endothelial cells. The current gold standard is the histological diagnosis of angiosarcoma; however, microscopic evaluation may be complicated, particularly when tumor cells are undetectable due to the presence of excessive amounts of nontumor cells or when tissue specimens have insufficient tumor content. In this study, we implemented machine learning applications from next-generation transcriptomic data of canine hemangiosarcoma tumor samples (n = 76) and nonmalignant tissues (n = 10) to evaluate their training performance for diagnostic utility. The 10-fold cross-validation test and multiple feature selection methods were applied. We found that extra trees and random forest learning models were the best classifiers for hemangiosarcoma in our testing datasets. We also identified novel gene signatures using the mutual information and Monte Carlo feature selection method. The extra trees model revealed high classification accuracy for hemangiosarcoma in validation sets. We demonstrate that high-throughput sequencing data of canine hemangiosarcoma are trainable for machine learning applications. Furthermore, our approach enables us to identify novel gene signatures as reliable determinants of hemangiosarcoma, providing significant insights into the development of potential applications for this vascular malignancy.

Description: Single-cell mRNA sequencing has been adopted as a powerful technique for understanding gene expression profiles at the single-cell level. However, challenges remain due to factors such as the inefficiency of mRNA molecular capture, technical noises and separate sequencing of cells in different batches. Normalization methods have been developed to ensure a relatively accurate analysis. This work presents a survey on 10 tools specifically designed for single-cell mRNA sequencing data preprocessing steps, among which 6 tools are used for dropout normalization and 4 tools are for batch effect correction. In this survey, we outline the main methodology for each of these tools, and we also compare these tools to evaluate their normalization performance on datasets which are simulated under the constraints of dropout inefficiency, batch effect or their combined effects. We found that Saver and Baynorm performed better than other methods in dropout normalization, in most cases. Beer and Batchelor performed better in the batch effect normalization, and the Saver–Beer tool combination and the Baynorm–Beer combination performed better in the mixed dropout-and-batch effect normalization. Over-normalization is a common issue occurred to these dropout normalization tools that is worth of future investigation. For the batch normalization tools, the capability of retaining heterogeneity between different groups of cells after normalization can be another direction for future improvement.

Description: Emerging evidence indicates that the abnormal expression of miRNAs involves in the evolution and progression of various human complex diseases. Identifying disease-related miRNAs as new biomarkers can promote the development of disease pathology and clinical medicine. However, designing biological experiments to validate disease-related miRNAs is usually time-consuming and expensive. Therefore, it is urgent to design effective computational methods for predicting potential miRNA-disease associations. Inspired by the great progress of graph neural networks in link prediction, we propose a novel graph auto-encoder model, named GAEMDA, to identify the potential miRNA-disease associations in an end-to-end manner. More specifically, the GAEMDA model applies a graph neural networks-based encoder, which contains aggregator function and multi-layer perceptron for aggregating nodes’ neighborhood information, to generate the low-dimensional embeddings of miRNA and disease nodes and realize the effective fusion of heterogeneous information. Then, the embeddings of miRNA and disease nodes are fed into a bilinear decoder to identify the potential links between miRNA and disease nodes. The experimental results indicate that GAEMDA achieves the average area under the curve of $93.56pm 0.44\%$ under 5-fold cross-validation. Besides, we further carried out case studies on colon neoplasms, esophageal neoplasms and kidney neoplasms. As a result, 48 of the top 50 predicted miRNAs associated with these diseases are confirmed by the database of differentially expressed miRNAs in human cancers and microRNA deregulation in human disease database, respectively. The satisfactory prediction performance suggests that GAEMDA model could serve as a reliable tool to guide the following researches on the regulatory role of miRNAs. Besides, the source codes are available at https://github.com/chimianbuhetang/GAEMDA.

Description: Identification of new drug–target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug–protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, ‘graph convolutional network (GCN)-DTI’, for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.

Description: RNA fulfills a crucial regulatory role in cells by folding into a complex RNA structure. To date, a chemical compound, dimethyl sulfate (DMS), has been developed to probe the RNA structure at the transcriptome level effectively. We proposed a database, RSVdb (https://taolab.nwafu.edu.cn/rsvdb/), for the browsing and visualization of transcriptome RNA structures. RSVdb, including 626 225 RNAs with validated DMS reactivity from 178 samples in eight species, supports four main functions: information retrieval, research overview, structure prediction and resource download. Users can search for species, studies, transcripts and genes of interest; browse the quality control of sequencing data and statistical charts of RNA structure information; preview and perform online prediction of RNA structures in silico and under DMS restraint of different experimental treatments and download RNA structure data for species and studies. Together, RSVdb provides a reference for RNA structure and will support future research on the function of RNA structure at the transcriptome level.

Description: The discrimination of driver from passenger mutations has been a hot topic in the field of cancer biology. Although recent advances have improved the identification of driver mutations in cancer genomic research, there is no computational method specific for the cancer frameshift indels (insertions or/and deletions) yet. In addition, existing pathogenic frameshift indel predictors may suffer from plenty of missing values because of different choices of transcripts during the variant annotation processes. In this study, we proposed a computational model, called PredCID (Predictor for Cancer driver frameshift InDels), for accurately predicting cancer driver frameshift indels. Gene, DNA, transcript and protein level features are combined together and selected for classification with eXtreme Gradient Boosting classifier. Benchmarking results on the cross-validation dataset and independent dataset showed that PredCID achieves better and robust performance compared with existing noncancer-specific methods in distinguishing cancer driver frameshift indels from passengers and is therefore a valuable method for deeper understanding of frameshift indels in human cancer. PredCID is freely available for academic research at http://bioinfo.ahu.edu.cn:8080/PredCID.

Description: Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process—the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h.

Description: Biological network-based strategies are useful in prioritizing genes associated with diseases. Several comprehensive human gene networks such as STRING, GIANT and HumanNet were developed and used in network-assisted algorithms to identify disease-associated genes. However, none of these networks are disease-specific and may not accurately reflect gene interactions for a specific disease. Aiming to improve disease gene prioritization using networks, we propose a Disease-Specific Network Enhancement Prioritization (DiSNEP) framework. DiSNEP first enhances a comprehensive gene network specifically for a disease through a diffusion process on a gene–gene similarity matrix derived from disease omics data. The enhanced disease-specific gene network thus better reflects true gene interactions for the disease and may improve prioritizing disease-associated genes subsequently. In simulations, DiSNEP that uses an enhanced disease-specific network prioritizes more true signal genes than comparison methods using a general gene network or without prioritization. Applications to prioritize cancer-associated gene expression and DNA methylation signal genes for five cancer types from The Cancer Genome Atlas (TCGA) project suggest that more prioritized candidate genes by DiSNEP are cancer-related according to the DisGeNET database than those prioritized by the comparison methods, consistently across all five cancer types considered, and for both gene expression and DNA methylation signal genes.

Description: High-throughput data generated by new biotechnologies require specific and adapted statistical treatment in order to be efficiently used in biological studies. In this article, we propose a powerful framework to manage and analyse multi-omics heterogeneous data to carry out an integrative analysis. We have illustrated this using the mixOmics package for R software as it specifically addresses data integration issues. Our work also aims at applying the most recent functionalities of mixOmics to real datasets. Although multi-block integrative methodologies exist, we hope to encourage a more widespread use of such approaches in an operational framework by biologists. We have used natural populations of the model plant Arabidopsis thaliana in this work, but the framework proposed is not limited to this plant and can be deployed whatever the organisms of interest and the biological question may be. Four omics datasets (phenomics, metabolomics, cell wall proteomics and transcriptomics) were collected, analysed and integrated to study the cell wall plasticity of plants exposed to sub-optimal temperature growth conditions. The methodologies presented here start from basic univariate statistics leading to multi-block integration analysis. We have also highlighted the fact that each method, either unsupervised or supervised, is associated with one biological issue. Using this powerful framework enabled us to arrive at novel conclusions on the biological system, which would not have been possible using standard statistical approaches.

Description: Protein S-sulfenylation is one kind of crucial post-translational modifications (PTMs) in which the hydroxyl group covalently binds to the thiol of cysteine. Some recent studies have shown that this modification plays an important role in signaling transduction, transcriptional regulation and apoptosis. To date, the dynamic of sulfenic acids in proteins remains unclear because of its fleeting nature. Identifying S-sulfenylation sites, therefore, could be the key to decipher its mysterious structures and functions, which are important in cell biology and diseases. However, due to the lack of effective methods, scientists in this field tend to be limited in merely a handful of some wet lab techniques that are time-consuming and not cost-effective. Thus, this motivated us to develop an in silico model for detecting S-sulfenylation sites only from protein sequence information. In this study, protein sequences served as natural language sentences comprising biological subwords. The deep neural network was consequentially employed to perform classification. The performance statistics within the independent dataset including sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the curve rates achieved 85.71%, 69.47%, 77.09%, 0.5554 and 0.833, respectively. Our results suggested that the proposed method (fastSulf-DNN) achieved excellent performance in predicting S-sulfenylation sites compared to other well-known tools on a benchmark dataset.

Description: The current outbreak of COVID-19 has generated an unprecedented scientific response worldwide, with the generation of vast amounts of publicly available epidemiological, biological and clinical data. Bioinformatics scientists have quickly produced online methods to provide non-computational users with the opportunity of analyzing such data. In this review, we report the results of this effort, by cataloguing the currently most popular web tools for COVID-19 research and analysis. Our focus was driven on tools drawing data from the fields of epidemiology, genomics, interactomics and pharmacology, in order to provide a meaningful depiction of the current state of the art of COVID-19 online resources.

Description: In order to extract useful information from a huge amount of biological data nowadays, simple and convenient tools are urgently needed for data analysis and modeling. In this paper, an automatic data mining tool, termed as ABCModeller (Automatic Binary Classification Modeller), with a user-friendly graphical interface was developed here, which includes automated functions as data preprocessing, significant feature extraction, classification modeling, model evaluation and prediction. In order to enhance the generalization ability of the final model, a consistent voting method was built here in this tool with the utilization of three popular machine-learning algorithms, as artificial neural network, support vector machine and random forest. Besides, Fibonacci search and orthogonal experimental design methods were also employed here to automatically select significant features in the data space and optimal hyperparameters of the three algorithms to achieve the best model. The reliability of this tool has been verified through multiple benchmark data sets. In addition, with the advantage of a user-friendly graphical interface of this tool, users without any programming skills can easily obtain reliable models directly from original data, which can reduce the complexity of modeling and data mining, and contribute to the development of related research including but not limited to biology. The excitable file of this tool can be downloaded from http://lishuyan.lzu.edu.cn/ABCModeller.rar.

Description: Human papillomavirus (HPV) integrating into human genome is the main cause of cervical carcinogenesis. HPV integration selection preference shows strong dependence on local genomic environment. Due to this theory, it is possible to predict HPV integration sites. However, a published bioinformatic tool is not available to date. Thus, we developed an attention-based deep learning model DeepHPV to predict HPV integration sites by learning environment features automatically. In total, 3608 known HPV integration sites were applied to train the model, and 584 reviewed HPV integration sites were used as the testing dataset. DeepHPV showed an area under the receiver-operating characteristic (AUROC) of 0.6336 and an area under the precision recall (AUPR) of 0.5670. Adding RepeatMasker and TCGA Pan Cancer peaks improved the model performance to 0.8464 and 0.8501 in AUROC and 0.7985 and 0.8106 in AUPR, respectively. Next, we tested these trained models on independent database VISDB and found the model adding TCGA Pan Cancer performed better (AUROC: 0.7175, AUPR: 0.6284) than the model adding RepeatMasker peaks (AUROC: 0.6102, AUPR: 0.5577). Moreover, we introduced attention mechanism in DeepHPV and enriched the transcription factor binding sites including BHLHA15, CHR, COUP-TFII, DMRTA2, E2A, HIC1, INR, NPAS, Nr5a2, RARa, SCL, Snail1, Sox10, Sox3, Sox4, Sox6, STAT6, Tbet, Tbx5, TEAD, Tgif2, ZNF189, ZNF416 near attention intensive sites. Together, DeepHPV is a robust and explainable deep learning model, providing new insights into HPV integration preference and mechanism. Availability: DeepHPV is available as an open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepHPV.git, Contact: huzheng1998@163.com, liangjiuxing@m.scnu.edu.cn, lizheyzy@163.com

Description: Gastric cancer (GC) continues to be one of the major causes of cancer deaths worldwide. Meanwhile, liquid biopsies have received extensive attention in the screening and detection of cancer along with better understanding and clinical practice of biomarkers. In this work, 58 routine blood biochemical indices were tentatively used as integrated markers, which further expanded the scope of liquid biopsies and a discrimination system for GC consisting of 17 top-ranked indices, elaborated by random forest method was constructed to assist in preliminary assessment prior to histological and gastroscopic diagnosis based on the test data of a total of 2951 samples. The selected indices are composed of eight routine blood indices (MO%, IG#, IG%, EO%, P-LCR, RDW-SD, HCT and RDW-CV) and nine blood biochemical indices (TP, AMY, GLO, CK, CHO, CK-MB, TG, ALB and γ-GGT). The system presented a robust classification performance, which can quickly distinguish GC from other stomach diseases, different cancers and healthy people with sensitivity, specificity, total accuracy and area under the curve of 0.9067, 0.9216, 0.9138 and 0.9720 for the cross-validation set, respectively. Besides, this system can not only provide an innovative strategy to facilitate rapid and real-time GC identification, but also reveal the remote correlation between GC and these routine blood biochemical parameters, which helped to unravel the hidden association of these parameters with GC and serve as the basis for subsequent studies of the clinical value in prevention program and surveillance management for GC. The identification system, called GC discrimination, is now available online at http://lishuyan.lzu.edu.cn/GC/.

Description: Least absolute shrinkage and selection operator (LASSO) regression is often applied to select the most promising set of single nucleotide polymorphisms (SNPs) associated with a molecular phenotype of interest. While the penalization parameter λ restricts the number of selected SNPs and the potential model overfitting, the least-squares loss function of standard LASSO regression translates into a strong dependence of statistical results on a small number of individuals with phenotypes or genotypes divergent from the majority of the study population—typically comprised of outliers and high-leverage observations. Robust methods have been developed to constrain the influence of divergent observations and generate statistical results that apply to the bulk of study data, but they have rarely been applied to genetic association studies. In this article, we review, for newcomers to the field of robust statistics, a novel version of standard LASSO that utilizes the Huber loss function. We conduct comprehensive simulations and analyze real protein, metabolite, mRNA expression and genotype data to compare the stability of penalization, the cross-iteration concordance of the model, the false-positive and true-positive rates and the prediction accuracy of standard and robust Huber-LASSO. Although the two methods showed controlled false-positive rates ≤2.1% and similar true-positive rates, robust Huber-LASSO outperformed standard LASSO in the accuracy of predicted protein, metabolite and gene expression levels using individual SNP data. The conducted simulations and real-data analyses show that robust Huber-LASSO represents a valuable alternative to standard LASSO in genetic studies of molecular phenotypes.

Description: Competitive endogenous RNA (ceRNA) represents a novel layer of gene regulation that controls both physiological and pathological processes. However, there is still lack of computational tools for quickly identifying ceRNA regulation. To address this problem, we presented an R-package, CeRNASeek, which allows identifying and analyzing ceRNA–ceRNA interactions by integration of multiple-omics data. CeRNASeek integrates six widely used computational methods to identify ceRNA–ceRNA interactions, including two global and four context-specific ceRNA regulation prediction methods. In addition, it provides several downstream analyses for predicted ceRNA–ceRNA pairs, including regulatory network analysis, functional annotation and survival analysis. With examples of cancer-related ceRNA prioritization and cancer subtyping, we demonstrate that CeRNASeek is a valuable tool for investigating the function of ceRNAs in complex diseases. In summary, CeRNASeek provides a comprehensive and efficient tool for identifying and analysis of ceRNA regulation. The package is available on the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=CeRNASeek.

Description: Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods’ performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development.

Description: The unprecedented coverage offered by next-generation sequencing (NGS) technology has facilitated the assessment of the population complexity of intra-host RNA viral populations at an unprecedented level of detail. Consequently, analysis of NGS datasets could be used to extract and infer crucial epidemiological and biomedical information on the levels of both infected individuals and susceptible populations, thus enabling the development of more effective prevention strategies and antiviral therapeutics. Such information includes drug resistance, infection stage, transmission clusters and structures of transmission networks. However, NGS data require sophisticated analysis dealing with millions of error-prone short reads per patient. Prior to the NGS era, epidemiological and phylogenetic analyses were geared toward Sanger sequencing technology; now, they must be redesigned to handle the large-scale NGS datasets and properly model the evolution of heterogeneous rapidly mutating viral populations. Additionally, dedicated epidemiological surveillance systems require big data analytics to handle millions of reads obtained from thousands of patients for rapid outbreak investigation and management. We survey bioinformatics tools analyzing NGS data for (i) characterization of intra-host viral population complexity including single nucleotide variant and haplotype calling; (ii) downstream epidemiological analysis and inference of drug-resistant mutations, age of infection and linkage between patients; and (iii) data collection and analytics in surveillance systems for fast response and control of outbreaks.

Description: Cholangiocarcinoma (CCA) is a type of cancer with limited treatment options and a poor prognosis. Although some important genes and pathways associated with CCA have been identified, the relationship between coexpression and phenotype in CCA at the systems level remains unclear. In this study, the relationships underlying the molecular and clinical characteristics of CCA were investigated by employing weighted gene coexpression network analysis (WGCNA). The gene expression profiles and clinical features of 36 patients with CCA were analyzed to identify differentially expressed genes (DEGs). Subsequently, the coexpression of DEGs was determined by using the WGCNA method to investigate the correlations between pairs of genes. Network modules that were significantly correlated with clinical traits were identified. In total, 1478 mRNAs were found to be aberrantly expressed in CCA. Seven coexpression modules that significantly correlated with clinical characteristics were identified and assigned representative colors. Among the 7 modules, the green and blue modules were significantly related to tumor differentiation. Seventy-eight hub genes that were correlated with tumor differentiation were found in the green and blue modules. Survival analysis showed that 17 hub genes were prognostic biomarkers for CCA patients. In addition, we found five new targets (ISM1, SULT1B1, KIFC1, AURKB and CCNB1) that have not been studied in the context of CCA and verified their differential expression in CCA through experiments. Our results not only promote our understanding of the relationship between the transcriptome and clinical data in CCA but will also guide the development of targeted molecular therapy for CCA.

Description: Long noncoding RNAs (lncRNAs) have been associated with cancer immunity regulation and the tumor microenvironment (TME). However, functions of lncRNAs of tumor-infiltrating B lymphocytes (TIL-Bs) and their clinical significance have not yet been fully elucidated. In the present study, a machine learning-based computational framework is presented for the identification of lncRNA signature of TIL-Bs (named ‘TILBlncSig’) through integrative analysis of immune, lncRNA and clinical profiles. The TILBlncSig comprising eight lncRNAs (TNRC6C-AS1, WASIR2, GUSBP11, OGFRP1, AC090515.2, PART1, MAFG-DT and LINC01184) was identified from the list of 141 B-cell-specific lncRNAs. The TILBlncSig was capable of distinguishing worse compared with improved survival outcomes across different independent patient datasets and was also independent of other clinical covariates. Functional characterization of TILBlncSig revealed it to be an indicator of infiltration of mononuclear immune cells (i.e. natural killer cells, B-cells and mast cells), and it was associated with hallmarks of cancer, as well as immunosuppressive phenotype. Furthermore, the TILBlncSig revealed predictive value for the survival outcome and immunotherapy response of patients with anti-programmed death-1 (PD-1) therapy and added significant predictive power to current immune checkpoint gene markers. The present study has highlighted the value of the TILBlncSig as an indicator of immune cell infiltration in the TME from a noncoding RNA perspective and strengthened the potential application of lncRNAs as predictive biomarkers of immunotherapy response, which warrants further investigation.

Description: Delineating the fingerprint or feature vector of a receptor/protein will facilitate the structural and biological studies, as well as the rational design and development of drugs with high affinities and selectivity. However, protein is complicated by its different functional regions that can bind to some of its protein partner(s), substrate(s), orthosteric ligand(s) or allosteric modulator(s) where cogent methods like molecular fingerprints do not work well. We here elaborate a scoring-function-based computing protocol Molecular Complex Characterizing System to help characterize the binding feature of protein–ligand complexes. Based on the reported receptor-ligand interactions, we first quantitate the energy contribution of each individual residue which may be an alternative of MD-based energy decomposition. We then construct a vector for the energy contribution to represent the pattern of the ligand recognition at a receptor and qualitatively analyze the matching level with other receptors. Finally, the energy contribution vector is explored for extensive use in similarity and clustering. The present work provides a new approach to cluster proteins, a perspective counterpart for determining the protein characteristics in the binding, and an advanced screening technique where molecular docking is applicable.

Description: The DNA methyltransferases (DNMTs) (DNMT3A, DNMT3B and DNMT3L) are primarily responsible for the establishment of genomic locus-specific DNA methylation patterns, which play an important role in gene regulation and animal development. However, this important protein family’s binding mechanism, i.e. how and where the DNMTs bind to genome, is still missing in most tissues and cell lines. This motivates us to explore DNMTs and TF’s cooperation and develop a network regularized logistic regression model, GuidingNet, to predict DNMTs’ genome-wide binding by integrating gene expression, chromatin accessibility, sequence and protein–protein interaction data. GuidingNet accurately predicted methylation experimental data validated DNMTs’ binding, outperformed single data source based and sparsity regularized methods and performed well in within and across tissue prediction for several DNMTs in human and mouse. Importantly, GuidingNet can reveal transcription cofactors assisting DNMTs for methylation establishment. This provides biological understanding in the DNMTs’ binding specificity in different tissues and demonstrate the advantage of network regularization. In addition to DNMTs, GuidingNet achieves good performance for other chromatin regulators’ binding. GuidingNet is freely available at https://github.com/AMSSwanglab/GuidingNet.

Description: Motivation: DNA methylation is a biological process impacting the gene functions without changing the underlying DNA sequence. The DNA methylation machinery usually attaches methyl groups to some specific cytosine residues, which modify the chromatin architectures. Such modifications in the promoter regions will inactivate some tumor-suppressor genes. DNA methylation within the coding region may significantly reduce the transcription elongation efficiency. The gene function may be tuned through some cytosines are methylated. Methods: This study hypothesizes that the overall methylation level across a gene may have a better association with the sample labels like diseases than the methylations of individual cytosines. The gene methylation level is formulated as a regression model using the methylation levels of all the cytosines within this gene. A comprehensive evaluation of various feature selection algorithms and classification algorithms is carried out between the gene-level and residue-level methylation levels. Results: A comprehensive evaluation was conducted to compare the gene and cytosine methylation levels for their associations with the sample labels and classification performances. The unsupervised clustering was also improved using the gene methylation levels. Some genes demonstrated statistically significant associations with the class label, even when no residue-level methylation features have statistically significant associations with the class label. So in summary, the trained gene methylation levels improved various methylome-based machine learning models. Both methodology development of regression algorithms and experimental validation of the gene-level methylation biomarkers are worth of further investigations in the future studies. The source code, example data files and manual are available at http://www.healthinformaticslab.org/supp/.

Description: Drug response prediction arises from both basic and clinical research of personalized therapy, as well as drug discovery for cancers. With gene expression profiles and other omics data being available for over 1000 cancer cell lines and tissues, different machine learning approaches have been applied to drug response prediction. These methods appear in a body of literature and have been evaluated on different datasets with only one or two accuracy metrics. We systematically assess 17 representative methods for drug response prediction, which have been developed in the past 5 years, on four large public datasets in nine metrics. This study provides insights and lessons for future research into drug response prediction.

Description: Motivation: The functional changes of the genes, RNAs and proteins will eventually be reflected in the metabolic level. Increasing number of researchers have researched mechanism, biomarkers and targeted drugs by metabolites. However, compared with our knowledge about genes, RNAs, and proteins, we still know few about diseases-related metabolites. All the few existed methods for identifying diseases-related metabolites ignore the chemical structure of metabolites, fail to recognize the association pattern between metabolites and diseases, and fail to apply to isolated diseases and metabolites. Results: In this study, we present a graph deep learning based method, named Deep-DRM, for identifying diseases-related metabolites. First, chemical structures of metabolites were used to calculate similarities of metabolites. The similarities of diseases were obtained based on their functional gene network and semantic associations. Therefore, both metabolites and diseases network could be built. Next, Graph Convolutional Network (GCN) was applied to encode the features of metabolites and diseases, respectively. Then, the dimension of these features was reduced by Principal components analysis (PCA) with retainment 99% information. Finally, Deep neural network was built for identifying true metabolite-disease pairs (MDPs) based on these features. The 10-cross validations on three testing setups showed outstanding AUC (0.952) and AUPR (0.939) of Deep-DRM compared with previous methods and similar approaches. Ten of top 15 predicted associations between diseases and metabolites got support by other studies, which suggests that Deep-DRM is an efficient method to identify MDPs. Contact: liangcheng@hrbmu.edu.cn. Availability and implementation: https://github.com/zty2009/GPDNN-for-Identify-ing-Disease-related-Metabolites.

Description: Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in ontologies and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies.

Description: Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, and which may be causatively neutral. After briefly reviewing generic tools, we focus on a subset of these methods specifically geared toward predicting which variants in the human cancer genome may act as enablers of unregulated cell proliferation. We consider the resultant view of the cancer genome indicated by these predictors and discuss ways in which these types of prediction tools may be progressed by further research.

Description: Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).

Description: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the cause of coronavirus disease (COVID-19) that causes a major threat to humanity. As the spread of the virus is probably getting out of control on every day, the epidemic is now crossing the most dreadful phase. Idiopathic pulmonary fibrosis (IPF) is a risk factor for COVID-19 as patients with long-term lung injuries are more likely to suffer in the severity of the infection. Transcriptomic analyses of SARS-CoV-2 infection and IPF patients in lung epithelium cell datasets were selected to identify the synergistic effect of SARS-CoV-2 to IPF patients. Common genes were identified to find shared pathways and drug targets for IPF patients with COVID-19 infections. Using several enterprising Bioinformatics tools, protein–protein interactions (PPIs) network was designed. Hub genes and essential modules were detected based on the PPIs network. TF-genes and miRNA interaction with common differentially expressed genes and the activity of TFs are also identified. Functional analysis was performed using gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway and found some shared associations that may cause the increased mortality of IPF patients for the SARS-CoV-2 infections. Drug molecules for the IPF were also suggested for the SARS-CoV-2 infections.

Description: Exploring protein–ligand interactions is a subject of immense interest, as it provides deeper insights into molecular recognition, mechanism of interaction and subsequent functions. Predicting an accurate model for a protein–ligand interaction is a challenging task. Molecular docking is a computational method used for predicting the preferred orientation, binding conformations and the binding affinity of a ligand to a macromolecular target, especially protein. It has been applied in ‘virtual high-throughput screening’ of chemical libraries containing millions of compounds to find potential leads in drug design and discovery. Here, we have developed InstaDock, a free and open access Graphical User Interface (GUI) program that performs molecular docking and high-throughput virtual screening efficiently. InstaDock is a single-click GUI that uses QuickVina-W, a modified version of AutoDock Vina for docking calculations, made especially for the convenience of non-bioinformaticians and for people who are not experts in using computers. InstaDock facilitates onboard analysis of docking and visual results in just a single click. To sum up, InstaDock is the easiest and more interactive interface than ever existing GUIs for molecular docking and high-throughput virtual screening. InstaDock is freely available for academic and industrial research purposes via https://hassanlab.org/instadock.

Description: Cells are compartmentalized by numerous membrane-bounded organelles and membraneless organelles (MLOs) to ensure temporal and spatial regulation of various biological processes. A number of MLOs, such as nucleoli, nuclear speckles and stress granules, exist as liquid droplets within the cells and arise from the condensation of proteins and RNAs via liquid–liquid phase separation (LLPS). By concentrating certain proteins and RNAs, MLOs accelerate biochemical reactions and protect cells during stress, and dysfunction of MLOs is associated with various pathological processes. With the development in this field, more and more relations between the MLOs and diseases have been described; however, these results have not been made available in a centralized resource. Herein, we build MloDisDB, a database which aims to gather the relations between MLOs and diseases from dispersed literature. In addition, the relations between LLPS and diseases were included as well. Currently, MloDisDB contains 771 curated entries from 607 publications; each entry in MloDisDB contains detailed information about the MLO, the disease and the functional factor in the relation. Furthermore, an efficient and user-friendly interface for users to search, browse and download all entries was provided. MloDisDB is the first comprehensive database of the relations between MLOs and diseases so far, and the database is freely accessible at http://mlodis.phasep.pro/.

Description: Circular RNAs (circRNAs) are widely expressed in eukaryotes. The genome-wide interactions between circRNAs and RNA-binding proteins (RBPs) can be probed from cross-linking immunoprecipitation with sequencing data. Therefore, computational methods have been developed for identifying RBP binding sites on circRNAs. Unfortunately, those computational methods often suffer from the low discriminative power of feature representations, numerical instability and poor scalability. To address those limitations, we propose a novel computational method called iCircRBP-DHN using deep hierarchical network for discriminating circRNA-RBP binding sites. The network architecture can be regarded as a deep multi-scale residual network followed by bidirectional gated recurrent units (BiGRUs) with the self-attention mechanism, which can simultaneously extract local and global contextual information. Meanwhile, we propose novel encoding schemes by integrating CircRNA2Vec and the K-tuple nucleotide frequency pattern to represent different degrees of nucleotide dependencies. To validate the effectiveness of our proposed iCircRBP-DHN, we compared its performance with other computational methods on 37 circRNAs datasets and 31 linear RNAs datasets, respectively. The experimental results reveal that iCircRBP-DHN can achieve superior performance over those state-of-the-art algorithms. Moreover, we perform motif analysis on circRNAs bound by those different RBPs, demonstrating that our proposed CircRNA2Vec encoding scheme can be promising. The iCircRBP-DHN method is made available at https://github.com/houzl3416/iCircRBP-DHN.

Description: Although great progress has been made in prognostic outcome prediction, small sample size remains a challenge in obtaining accurate and robust classifiers. We proposed the Rescaled linear square Regression based Least Squares Learning (RRLSL), a jointly developed semi-supervised feature selection and classifier, for predicting prognostic outcome of cancer patients. RRLSL used the least square regression to identify the scale factors and then rank the features in available multiple types of molecular data. We applied the unlabeled multiple molecular data in conjunction with the labeled data to develop a similarity graph. RRLSL produced the constraint with kernel functions to bridge the gap between label information and geometry information from messenger RNA and microRNA expression profiling. Importantly, this semi-supervised model proposed the least squares learning with L2 regularization to develop a semi-supervised classifier. RRLSL suggested the performance improvement in the prognostic outcome prediction and successfully discriminated between the recurrent patients and non-recurrent ones. We also demonstrated that RRLSL improved the accuracy and Area Under the Precision Recall Curve (AUPRC) as compared to the baseline semi-supervised methods. RRLSL is available for a stand-alone software package (https://github.com/ShiMGLab/RRLSL). A short abstract We proposed the Rescaled linear square Regression based Least Squares Learning (RRLSL), a jointly developed semi-supervised feature selection and classifier, for predicting prognostic outcome of cancer patients. RRLSL used the least square regression to identify the scale factors to rank the features in available multiple types of molecular data. RRLSL produced the constraint with kernel functions to bridge the gap between label information and geometry information from messenger RNA and microRNA expression profiling. Importantly, this semi-supervised model proposed the least squares learning with L2 regularization to develop the semi-supervised classifier. RRLSL suggested the performance improvement in the prognostic outcome prediction and successfully discriminated between the recurrent patients and non-recurrent ones.

Description: Breast cancer is a highly heterogeneous disease, and there are many forms of categorization for breast cancer based on gene expression profiles. Gene expression profiles are variables and may show differences if measured at different time points or under different conditions. In contrast, biological networks are relatively stable over time and under different conditions. In this study, we used a gene interaction network from a new point of view to explore the subtypes of breast cancer based on individual-specific edge perturbations measured by relative gene expression value. Our study reveals that there are four breast cancer subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of breast cancer show strong heterogeneity in prognosis, somatic mutations, phenotypic changes and enriched pathways. The network-based subtypes are closely related to the PAM50 subtypes and immunohistochemistry index. This work helps us to better understand the heterogeneity and mechanisms of breast cancer from a network perspective.

Description: Prognostic tests using expression profiles of several dozen genes help provide treatment choices for prostate cancer (PCa). However, these tests require improvement to meet the clinical need for resolving overtreatment, which continues to be a pervasive problem in PCa management. Genomic selection (GS) methodology, which utilizes whole-genome markers to predict agronomic traits, was adopted in this study for PCa prognosis. We leveraged The Cancer Genome Atlas (TCGA) database to evaluate the prediction performance of six GS methods and seven omics data combinations, which showed that the Best Linear Unbiased Prediction (BLUP) model outperformed the other methods regarding predictability and computational efficiency. Leveraging the BLUP-HAT method, an accelerated version of BLUP, we demonstrated that using expression data of a large number of disease-relevant genes and with an integration of other omics data (i.e. miRNAs) significantly increased outcome predictability when compared with panels consisting of a small number of genes. Finally, we developed a novel stepwise forward selection BLUP-HAT method to facilitate searching multiomics data for predictor variables with prognostic potential. The new method was applied to the TCGA data to derive mRNA and miRNA expression signatures for predicting relapse-free survival of PCa, which were validated in six independent cohorts. This is a transdisciplinary adoption of the highly efficient BLUP-HAT method and its derived algorithms to analyze multiomics data for PCa prognosis. The results demonstrated the efficacy and robustness of the new methodology in developing prognostic models in PCa, suggesting a potential utility in managing other types of cancer.

Description: Cancer is a highly heterogeneous disease caused by dysregulation in different cell types and tissues. However, different cancers may share common mechanisms. It is critical to identify decisive genes involved in the development and progression of cancer, and joint analysis of multiple cancers may help to discover overlapping mechanisms among different cancers. In this study, we proposed a fusion feature selection framework attributed to ensemble method named Fisher score and Gradient Boosting Decision Tree (FS–GBDT) to select robust and decisive feature genes in high-dimensional gene expression datasets. Joint analysis of 11 human cancers types was conducted to explore the key feature genes subset of cancer. To verify the efficacy of FS–GBDT, we compared it with four other common feature selection algorithms by Support Vector Machine (SVM) classifier. The algorithm achieved highest indicators, outperforms other four methods. In addition, we performed gene ontology analysis and literature validation of the key gene subset, and this subset were classified into several functional modules. Functional modules can be used as markers of disease to replace single gene which is difficult to be found repeatedly in applications of gene chip, and to study the core mechanisms of cancer.

Description: Background High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of ‘noisy compounds’ in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery. Results In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram. Conclusion Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

Description: Sepsis is a life-threatening complication of pneumonia, including coronavirus disease-2019 (COVID-19)-induced pneumonia. Evidence of the benefits of vitamin C (VC) for the treatment of sepsis is accumulating. However, data revealing the targets and molecular mechanisms of VC action against sepsis are limited. In this report, a bioinformatics analysis of network pharmacology was conducted to demonstrate screening targets, biological functions, and the signaling pathways of VC action against sepsis. As shown in network assays, 63 primary causal targets for the VC action against sepsis were identified from the data, and four optimal core targets for the VC action against sepsis were identified. These core targets were epidermal growth factor receptor (EGFR), mitogen-activated protein kinase-1 (MAPK1), proto-oncogene c (JUN), and signal transducer and activator of transcription-3 (STAT3). In addition, all biological processes (including a top 20) and signaling pathways (including a top 20) potentially involved in the VC action against sepsis were identified. The hub genes potentially involved in the VC action against sepsis and interlaced networks from the Kyoto Encyclopedia of Genes and Genomes Mapper assays were highlighted. Considering all the bioinformatic findings, we conclude that VC antisepsis effects are mechanistically and pharmacologically implicated with suppression of immune dysfunction-related and inflammation-associated functional processes and other signaling pathways. These primary predictive biotargets may potentially be used to treat sepsis in future clinical practice.

Description: Drug repositioning can drastically decrease the cost and duration taken by traditional drug research and development while avoiding the occurrence of unforeseen adverse events. With the rapid advancement of high-throughput technologies and the explosion of various biological data and medical data, computational drug repositioning methods have been appealing and powerful techniques to systematically identify potential drug-target interactions and drug-disease interactions. In this review, we first summarize the available biomedical data and public databases related to drugs, diseases and targets. Then, we discuss existing drug repositioning approaches and group them based on their underlying computational models consisting of classical machine learning, network propagation, matrix factorization and completion, and deep learning based models. We also comprehensively analyze common standard data sets and evaluation metrics used in drug repositioning, and give a brief comparison of various prediction methods on the gold standard data sets. Finally, we conclude our review with a brief discussion on challenges in computational drug repositioning, which includes the problem of reducing the noise and incompleteness of biomedical data, the ensemble of various computation drug repositioning methods, the importance of designing reliable negative samples selection methods, new techniques dealing with the data sparseness problem, the construction of large-scale and comprehensive benchmark data sets and the analysis and explanation of the underlying mechanisms of predicted interactions.

Description: Motivation The advancements of single-cell sequencing methods have paved the way for the characterization of cellular states at unprecedented resolution, revolutionizing the investigation on complex biological systems. Yet, single-cell sequencing experiments are hindered by several technical issues, which cause output data to be noisy, impacting the reliability of downstream analyses. Therefore, a growing number of data science methods has been proposed to recover lost or corrupted information from single-cell sequencing data. To date, however, no quantitative benchmarks have been proposed to evaluate such methods. Results We present a comprehensive analysis of the state-of-the-art computational approaches for denoising and imputation of single-cell transcriptomic data, comparing their performance in different experimental scenarios. In detail, we compared 19 denoising and imputation methods, on both simulated and real-world datasets, with respect to several performance metrics related to imputation of dropout events, recovery of true expression profiles, characterization of cell similarity, identification of differentially expressed genes and computation time. The effectiveness and scalability of all methods were assessed with regard to distinct sequencing protocols, sample size and different levels of biological variability and technical noise. As a result, we identify a subset of versatile approaches exhibiting solid performances on most tests and show that certain algorithmic families prove effective on specific tasks but inefficient on others. Finally, most methods appear to benefit from the introduction of appropriate assumptions on noise distribution of biological processes.

Description: Single-cell RNA-sequencing (scRNA-seq) data widely exist in bioinformatics. It is crucial to devise a distance metric for scRNA-seq data. Almost all existing clustering methods based on spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretization of the learned labels by k-means clustering. However, this common practice has potential flaws that may lead to severe information loss and degradation of performance. Furthermore, the performance of a kernel method is largely determined by the selected kernel; a self-weighted multiple kernel learning model can help choose the most suitable kernel for scRNA-seq data. To this end, we propose to automatically learn similarity information from data. We present a new clustering method in the form of a multiple kernel combination that can directly discover groupings in scRNA-seq data. The main proposition is that automatically learned similarity information from scRNA-seq data is used to transform the candidate solution into a new solution that better approximates the discrete one. The proposed model can be efficiently solved by the standard support vector machine (SVM) solvers. Experiments on benchmark scRNA-Seq data validate the superior performance of the proposed model. Spectral clustering with multiple kernels is implemented in Matlab, licensed under Massachusetts Institute of Technology (MIT) and freely available from the Github website, https://github.com/Cuteu/SMSC/.

Description: Drug discovery and development is a time-consuming and costly process. Therefore, drug repositioning has become an effective approach to address the issues by identifying new therapeutic or pharmacological actions for existing drugs. The drug’s anatomical therapeutic chemical (ATC) code is a hierarchical classification system categorized as five levels according to the organs or systems that drugs act and the pharmacology, therapeutic and chemical properties of drugs. The 2nd-, 3rd- and 4th-level ATC codes reserved the therapeutic and pharmacological information of drugs. With the hypothesis that drugs with similar structures or targets would possess similar ATC codes, we exploited a network-based approach to predict the 2nd-, 3rd- and 4th-level ATC codes by constructing substructure drug-ATC (SD-ATC), target drug-ATC (TD-ATC) and Substructure&Target drug-ATC (STD-ATC) networks. After 10-fold cross validation and two external validations, the STD-ATC models outperformed the SD-ATC and TD-ATC ones. Furthermore, with KR as fingerprint, the STD-ATC model was identified as the optimal model with AUC values at 0.899 ± 0.015, 0.916 and 0.893 for 10-fold cross validation, external validation set 1 and external validation set 2, respectively. To illustrate the predictive capability of the STD-ATC model with KR fingerprint, as a case study, we predicted 25 FDA-approved drugs (22 drugs were actually purchased) to have potential activities on heart failure using that model. Experiments in vitro confirmed that 8 of the 22 old drugs have shown mild to potent cardioprotective activities on both hypoxia model and oxygen–glucose deprivation model, which demonstrated that our STD-ATC prediction model would be an effective tool for drug repositioning.

Description: Atomic charges play a very important role in drug-target recognition. However, computation of atomic charges with high-level quantum mechanics (QM) calculations is very time-consuming. A number of machine learning (ML)-based atomic charge prediction methods have been proposed to speed up the calculation of high-accuracy atomic charges in recent years. However, most of them used a set of predefined molecular properties, such as molecular fingerprints, for model construction, which is knowledge-dependent and may lead to biased predictions due to the representation preference of different molecular properties used for training. To solve the problem, we present a new architecture based on graph convolutional network (GCN) and develop a high-accuracy atomic charge prediction model named DeepAtomicCharge. The new GCN architecture is designed with only the atomic properties and the connection information between the atoms in molecules and can dynamically learn and convert molecules into appropriate atomic features without any prior knowledge of the molecules. Using the designed GCN architecture, substantial improvement is achieved for the prediction accuracy of atomic charges. The average root-mean-square error (RMSE) of DeepAtomicCharge is 0.0121 e, which is obviously more accurate than that (0.0180 e) reported by the previous benchmark study on the same two external test sets. Moreover, the new GCN architecture needs much lower storage space compared with other methods, and the predicted DDEC atomic charges can be efficiently used in large-scale structure-based drug design, thus opening a new avenue for high-performance atomic charge prediction and application.

Description: Based on clinical outcomes in colorectal cancer, high microsatellite instability (MSI-H) has recently been approved by the Food and Drug Administration (FDA) as a genetic test to select patients for immunotherapy targeting PD-1 and/or CTLA-4 without limitation to cancer type. However, it is unclear whether the MSI-H would broadly alter the tumor microenvironment to confer the therapeutic response of different cancer types to immunotherapy. To fill in this gap, we performed an in silico analysis of tumor immunity among different MSI statuses in five cancer types. We found that consistent with clinical responses to immunotherapy, MSI-H and non-MSI-H samples from colorectal cancer (COAD-READ) exhibited distinct infiltration levels and immune phenotypes. Surprisingly, the immunological difference between MSI-H and non-MSI-H samples was diminished in stomach adenocarcinoma and esophageal carcinoma (STAD-ESCA) and completely disappeared in uterine corpus endometrial carcinoma (UCEC). Regardless of cancer types, the abundance of tumor-infiltrating immune cells, rather than MSI status, strongly associated with the clinical outcome. Since preexisting antitumor immune response in the tumor (hot cancer) is accepted as a prerequisite to the therapeutic response to anti-PD-1/CTLA-4 immunotherapy, our data demonstrate that the impact of MSI varied on immune contexture will lead to the further evaluation of predictive immunotherapy responsiveness based on the universal biomarker of MSI status.

Description: Topologically associated domains (TADs) are spatial and functional units of metazoan chromatin structure. Interpretation of the interplay between regulatory factors and chromatin structure within TADs is crucial to understand the spatial and temporal regulation of gene expression. However, a computational metric for the sensitive characterization of TAD regulatory landscape is lacking. Here, we present the spatial density of open chromatin (SDOC) metric as a quantitative measurement of intra-TAD chromatin state and structure. SDOC sensitively reflects epigenetic properties and gene transcriptional activity in TADs. During mouse T-cell development, we found that TADs with decreased SDOC are enriched in repressed developmental genes, and the joint effect of SDOC-decreasing and TAD clustering corresponds to the highest level of gene repression. In addition, we revealed a pervasive preference for TADs with similar SDOC to interact with each other, which may reflect the principle of chromatin organization.

Description: Plasmids play important roles in microbial evolution and also in the spread of antibiotic resistance. Plasmid sequences are extensively studied from clinical isolates but rarely from the environment with a metagenomic approach focused on the plasmid fraction referred to as the plasmidome. A clear challenge in this context is to define a workflow for discriminating plasmids from chromosomal contaminants existing in the plasmidome. For this purpose, we benchmarked existing tools from assembly to detection of the plasmids by reference-free methods (cBar and PlasFlow) and database-guided approaches. Our simulations took into account short-reads alone or combined with moderate long-reads like those actually generated in environmental genomics experiments. This benchmark allowed us to select the best tools for limiting false-positives associated to plasmid prediction tools and a combination of reference-guided methods based on plasmid and bacterial databases.