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Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-depth research on related biological mechanisms, disease treatments and drug design. Due to the high cost and time consumption of high-throughput sequencing techniques, developing machine learning-based predictors has been considered an effective approach to rapidly recognize potential modified sites. However, the imbalanced distribution of true and false PTM sites, namely, the data imbalance problem, largely effects the reliability and application of prediction tools. In this article, we conduct a systematic survey of the research progress in the imbalanced PTMs classification. First, we describe the modeling process in detail and outline useful data imbalance solutions. Then, we summarize the recently proposed bioinformatics tools based on imbalanced PTM data and simultaneously build a convenient website, ImClassi_PTMs (available at lab.malab.cn/∼dlj/ImbClassi_PTMs/), to facilitate the researchers to view. Moreover, we analyze the challenges of current computational predictors and propose some suggestions to improve the efficiency of imbalance learning. We hope that this work will provide comprehensive knowledge of imbalanced PTM recognition and contribute to advanced predictors in the future. [ABSTRACT FROM AUTHOR]

With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant–pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within the explosion of data, machine learning offers powerful tools to process these complex omics data by various algorithms, such as Bayesian reasoning, support vector machine and random forest. Here, we introduce the basic frameworks of machine learning in dissecting plant–pathogen interactions and discuss the applications and advances of machine learning in plant–pathogen interactions from molecular to network biology, including the prediction of pathogen effectors, plant disease resistance protein monitoring and the discovery of protein–protein networks. The aim of this review is to provide a summary of advances in plant defense and pathogen infection and to indicate the important developments of machine learning in phytopathology. [ABSTRACT FROM AUTHOR]

Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers. [ABSTRACT FROM AUTHOR]

Anticancer peptides constitute one of the most promising therapeutic agents for combating common human cancers. Using wet experiments to verify whether a peptide displays anticancer characteristics is time-consuming and costly. Hence, in this study, we proposed a computational method named identify anticancer peptides via deep representation learning features (iACP-DRLF) using light gradient boosting machine algorithm and deep representation learning features. Two kinds of sequence embedding technologies were used, namely soft symmetric alignment embedding and unified representation (UniRep) embedding, both of which involved deep neural network models based on long short-term memory networks and their derived networks. The results showed that the use of deep representation learning features greatly improved the capability of the models to discriminate anticancer peptides from other peptides. Also, UMAP (uniform manifold approximation and projection for dimension reduction) and SHAP (shapley additive explanations) analysis proved that UniRep have an advantage over other features for anticancer peptide identification. The python script and pretrained models could be downloaded from https://github.com/zhibinlv/iACP-DRLF or from http://public.aibiochem.net/iACP-DRLF/. [ABSTRACT FROM AUTHOR]

Motivation The developmental process of epithelial-mesenchymal transition (EMT) is abnormally activated during breast cancer metastasis. Transcriptional regulatory networks that control EMT have been well studied; however, alternative RNA splicing plays a vital regulatory role during this process and the regulating mechanism needs further exploration. Because of the huge cost and complexity of biological experiments, the underlying mechanisms of alternative splicing (AS) and associated RNA-binding proteins (RBPs) that regulate the EMT process remain largely unknown. Thus, there is an urgent need to develop computational methods for predicting potential RBP-AS event associations during EMT. Results We developed a novel model for RBP-AS target prediction during EMT that is based on inductive matrix completion (RAIMC). Integrated RBP similarities were calculated based on RBP regulating similarity, and RBP Gaussian interaction profile (GIP) kernel similarity, while integrated AS event similarities were computed based on AS event module similarity and AS event GIP kernel similarity. Our primary objective was to complete missing or unknown RBP-AS event associations based on known associations and on integrated RBP and AS event similarities. In this paper, we identify significant RBPs for AS events during EMT and discuss potential regulating mechanisms. Our computational results confirm the effectiveness and superiority of our model over other state-of-the-art methods. Our RAIMC model achieved AUC values of 0.9587 and 0.9765 based on leave-one-out cross-validation (CV) and 5-fold CV, respectively, which are larger than the AUC values from the previous models. RAIMC is a general matrix completion framework that can be adopted to predict associations between other biological entities. We further validated the prediction performance of RAIMC on the genes CD44 and MAP3K7. RAIMC can identify the related regulating RBPs for isoforms of these two genes. Availability and implementation The source code for RAIMC is available at https://github.com/yushanqiu/RAIMC. Contact zouquan@nclab.net online. [ABSTRACT FROM AUTHOR]

Motivation mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals important limitations: (1) turning multiple classifications into multiple dichotomies makes the training process tedious; (2) the majority of the models trained by classical algorithm are based on the extraction of single sequence information; (3) the existing state-of-the-art models have not reached an ideal level in terms of prediction and generalization ability. To achieve better assignment of subcellular localization of eukaryotic mRNA, a better and more comprehensive model must be developed. Results In this paper, SubLocEP is proposed as a two-layer integrated prediction model for accurate prediction of the location of sequence samples. Unlike the existing models based on limited features, SubLocEP comprehensively considers additional feature attributes and is combined with LightGBM to generated single feature classifiers. The initial integration model (single-layer model) is generated according to the categories of a feature. Subsequently, two single-layer integration models are weighted (sequence-based: physicochemical properties = 3:2) to produce the final two-layer model. The performance of SubLocEP on independent datasets is sufficient to indicate that SubLocEP is an accurate and stable prediction model with strong generalization ability. Additionally, an online tool has been developed that contains experimental data and can maximize the user convenience for estimation of subcellular localization of eukaryotic mRNA. [ABSTRACT FROM AUTHOR]

Over the past decades, learning to rank (LTR) algorithms have been gradually applied to bioinformatics. Such methods have shown significant advantages in multiple research tasks in this field. Therefore, it is necessary to summarize and discuss the application of these algorithms so that these algorithms are convenient and contribute to bioinformatics. In this paper, the characteristics of LTR algorithms and their strengths over other types of algorithms are analyzed based on the application of multiple perspectives in bioinformatics. Finally, the paper further discusses the shortcomings of the LTR algorithms, the methods and means to better use the algorithms and some open problems that currently exist. [ABSTRACT FROM AUTHOR]

Immunoglobulin, which is also called an antibody, is a type of serum protein produced by B cells that can specifically bind to the corresponding antigen. Immunoglobulin is closely related to many diseases and plays a key role in medical and biological circles. Therefore, the use of effective methods to improve the accuracy of immunoglobulin classification is of great significance for disease research. In this paper, the CC–PSSM and monoTriKGap methods were selected to extract the immunoglobulin features, MRMD1.0 and MRMD2.0 were used to reduce the feature dimension, and the effect of discriminating the two–dimensional key features identified by the single dimension reduction method from the mixed two–dimensional key features was used to distinguish the immunoglobulins. The data results indicated that monoTrikGap (k = 1) can accurately predict 99.5614% of immunoglobulins under 5-fold cross–validation. In addition, CC–PSSM is the best method for identifying mixed two–dimensional key features and can distinguish 92.1053% of immunoglobulins. The above proves that the method used in this paper is reliable for predicting immunoglobulin and identifying key features. [ABSTRACT FROM AUTHOR]

Highly immunogenic exotoxins are used as carrier proteins because they efficiently improve the immunogenicity of polysaccharides. However, their efficiency with protein antigens remains unclear. In the current study, the candidate antigen PA0833 from Pseudomonas aeruginosa was fused to the α-hemolysin mutant HlaH35A from Staphylococcus aureus to form a HlaH35A-PA0833 fusion protein (HPF). Immunization with HPF resulted in increased PA0833-specific antibody titers, higher protective efficacy, and decreased bacterial burden and pro-inflammatory cytokine secretion compared with PA0833 immunization alone. Using fluorescently labeled antigens to track antigen uptake and delivery, we found that HlaH35A fusion significantly improved antigen uptake in injected muscles and antigen delivery to draining lymph nodes. Both in vivo and in vitro studies demonstrated that the increased antigen uptake after immunization with HPF was mainly due to monocyte- and macrophage-dependent macropinocytosis, which was probably the result of HPF binding to ADAM10, the Hla host receptor. Furthermore, a transcriptome analysis showed that several immune signaling pathways were activated by HPF, shedding light on the mechanism whereby HlaH35A fusion improves immunogenicity. Finally, the improvement in immunogenicity by HlaH35A fusion was also confirmed with two other antigens, GlnH from Klebsiella pneumoniae and the model antigen OVA, indicating that HlaH35A could serve as a universal carrier protein to improve the immunogenicity of protein antigens. Author summary: Pore-forming toxins, a kind of exotoxins utilized by many pathogens as immune escaping weapons that targeting the immune cells and disturbing the immune system, are conventionally deemed as perfect antigens for vaccine development against infectious diseases. In this study, we reported that fusion of HlaH35A, a typical pore-forming toxin toxoid, to candidate protein antigens from different species resulted in improved immunogenicity and protective efficacy. The improvement was mainly due to the increased antigen uptake and activating of immune-associated signaling pathways, probably by targeting ADAM10, the receptor of Hla on host immune cells. The importance of this work was to demonstrate the possible mechanisms of that pore-forming toxin function as atypical carrier protein to improve the immunogenicity of other proteins and confirm the potential of non-conservative but highly immunogenic exotoxins derived from hyper-virulent clinical strains for application in rational antigen design and vaccines development. [ABSTRACT FROM AUTHOR]

In viruses, posttranslational modifications (PTMs) are essential for their life cycle. Recognizing viral PTMs is very important for a better understanding of the mechanism of viral infections and finding potential drug targets. However, few studies have investigated the roles of viral PTMs in virus–human interactions using comprehensive viral PTM datasets. To fill this gap, we developed the first comprehensive viral posttranslational modification database (VPTMdb) for collecting systematic information of PTMs in human viruses and infected host cells. The VPTMdb contains 1240 unique viral PTM sites with 8 modification types from 43 viruses (818 experimentally verified PTM sites manually extracted from 150 publications and 422 PTMs extracted from SwissProt) as well as 13 650 infected cells' PTMs extracted from seven global proteomics experiments in six human viruses. The investigation of viral PTM sequences motifs showed that most viral PTMs have the consensus motifs with human proteins in phosphorylation and five cellular kinase families phosphorylate more than 10 viral species. The analysis of protein disordered regions presented that more than 50% glycosylation sites of double-strand DNA viruses are in the disordered regions, whereas single-strand RNA and retroviruses prefer ordered regions. Domain–domain interaction analysis indicating potential roles of viral PTMs play in infections. The findings should make an important contribution to the field of virus–human interaction. Moreover, we created a novel sequence-based classifier named VPTMpre to help users predict viral protein phosphorylation sites. VPTMdb online web server (http://vptmdb.com:8787/VPTMdb/) was implemented for users to download viral PTM data and predict phosphorylation sites of interest. [ABSTRACT FROM AUTHOR]

The peptide therapeutics market is providing new opportunities for the biotechnology and pharmaceutical industries. Therefore, identifying therapeutic peptides and exploring their properties are important. Although several studies have proposed different machine learning methods to predict peptides as being therapeutic peptides, most do not explain the decision factors of model in detail. In this work, an Interpretable Therapeutic Peptide Prediction (ITP-Pred) model based on efficient feature fusion was developed. First, we proposed three kinds of feature descriptors based on sequence and physicochemical property encoded, namely amino acid composition (AAC), group AAC and coding autocorrelation, and concatenated them to obtain the feature representation of therapeutic peptide. Then, we input it into the CNN-Bi-directional Long Short-Term Memory (BiLSTM) model to automatically learn recognition of therapeutic peptides. The cross-validation and independent verification experiments results indicated that ITP-Pred has a higher prediction performance on the benchmark dataset than other comparison methods. Finally, we analyzed the output of the model from two aspects: sequence order and physical and chemical properties, mining important features as guidance for the design of better models that can complement existing methods. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at the cellular level. However, due to the extremely low levels of transcripts in a single cell and technical losses during reverse transcription, gene expression at a single-cell resolution is usually noisy and highly dimensional; thus, statistical analyses of single-cell data are a challenge. Although many scRNA-seq data analysis tools are currently available, a gold standard pipeline is not available for all datasets. Therefore, a general understanding of bioinformatics and associated computational issues would facilitate the selection of appropriate tools for a given set of data. In this review, we provide an overview of the goals and most popular computational analysis tools for the quality control, normalization, imputation, feature selection and dimension reduction of scRNA-seq data. [ABSTRACT FROM AUTHOR]

Over the last decade, genome-wide association studies (GWAS) have discovered thousands of genetic variants underlying complex human diseases and agriculturally important traits. These findings have been utilized to dissect the biological basis of diseases, to develop new drugs, to advance precision medicine and to boost breeding. However, the potential of GWAS is still underexploited due to methodological limitations. Many challenges have emerged, including detecting epistasis and single-nucleotide polymorphisms (SNPs) with small effects and distinguishing causal variants from other SNPs associated through linkage disequilibrium. These issues have motivated advancements in GWAS analyses in two contrasting cultures—statistical modelling and machine learning. In this review, we systematically present the basic concepts and the benefits and limitations in both methods. We further discuss recent efforts to mitigate their weaknesses. Additionally, we summarize the state-of-the-art tools for detecting the missed signals, ultrarare mutations and gene–gene interactions and for prioritizing SNPs. Our work can offer both theoretical and practical guidelines for performing GWAS analyses and for developing further new robust methods to fully exploit the potential of GWAS. [ABSTRACT FROM AUTHOR]

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/. [ABSTRACT FROM AUTHOR]

Rationale: Neutrophils constitute massive cellular constituents in inflammatory human gastric cancer (GC) tissues, but their roles in pathogenesis of inflammatory T helper (Th) subsets are still unknown. Methods: Flow cytometry analysis and immunohistochemistry were used to analyze the responses and phenotypes of neutrophils in different samples from 51 patients with GC. Kaplan‐Meier plots and Multivariate analysis for the survival of patients were used by log‐rank tests and Cox proportional hazards models. Neutrophils and CD4+ T cells were purified and cultured for ex vivo, in vitro and in vivo regulation and function assays. Results: GC patients exhibited increased tumoral neutrophil infiltration with GC progression and poor patient prognosis. Intratumoral neutrophils accumulated in GC tumors via CXCL6/CXCL8‐CXCR1‐mediated chemotaxis, and expressed activated molecule CD54 and co‐signaling molecule B7‐H2. Neutrophils induced by tumors strongly expressed CD54 and B7‐H2 in both dose‐ and time‐dependent manners, and a close correlation was obtained between the expressions of CD54 and B7‐H2 on intratumoral neutrophils. Tumor‐derived tumor necrosis factor‐α (TNF‐α) promoted neutrophil activation and neutrophil B7‐H2 expression through ERK‐NF‐κB pathway, and a significant correlation was found between the levels of TNF‐α and CD54+ or B7‐H2+ neutrophils in tumor tissues. Tumor‐infiltrating and tumor‐conditioned neutrophils effectively induced IL‐17A‐producing Th subset polarization through a B7‐H2‐dependent manner ex vivo and these polarized IL‐17A‐producing Th cells exerted protumorigenic roles by promoting GC tumor cell proliferation via inflammatory molecule IL‐17A in vitro, which promoted the progression of human GC in vivo; these effects could be reversed when IL‐17A is blocked. Moreover, increased B7‐H2+ neutrophils and IL‐17A in tumors were closely related to advanced GC progression and predicted poor patient survival. Conclusion: We illuminate novel underlying mechanisms that TNF‐α‐activated neutrophils link B7‐H2 to protumorigenic IL‐17A‐producing Th subset polarization in human GC. Blocking this pathological TNF‐α‐B7‐H2‐IL‐17A pathway may be useful therapeutic strategies for treating GC. [ABSTRACT FROM AUTHOR]

The interaction between proteins and nucleic acid plays an important role in many processes, such as transcription, translation and DNA repair. The mechanisms of related biological events can be understood by exploring the function of proteins in these interactions. The number of known protein sequences has increased rapidly in recent years, but the databases for describing the structure and function of protein have unfortunately grown quite slowly. Thus, improving such databases is meaningful for predicting protein–nucleic acid interactions. Furthermore, the mechanism of related biological events, such as viral infection or designing novel drug targets, can be further understood by understanding the function of proteins in these interactions. The information for each sequence, including its function and interaction sites, were collected and identified, and a database called PNIDB was built. The proteins in PNIDB were grouped into 27 classes, such as transcription, immune system, and structural protein, etc. The function of each protein was then predicted using a machine learning method. Using our method, the predictor was trained on labeled sequences, and then the function of a protein was predicted based on the trained classifier. The prediction accuracy achieved a score of 77.43% by 10-fold cross validation. [ABSTRACT FROM AUTHOR]

Quantifying DNA properties is a challenging task in the broad field of human genomics. Since the vast majority of non-coding DNA is still poorly understood in terms of function, this task is particularly important to have enormous benefit for biology research. Various DNA sequences should have a great variety of representations, and specific functions may focus on corresponding features in the front part of learning model. Currently, however, for multi-class prediction of non-coding DNA regulatory functions, most powerful predictive models do not have appropriate feature extraction and selection approaches for specific functional effects, so that it is difficult to gain a better insight into their internal correlations. Hence, we design a category attention layer and category dense layer in order to select efficient features and distinguish different DNA functions. In this study, we propose a hybrid deep neural network method, called DeepATT, for identifying |$919$| regulatory functions on nearly |$5$| million DNA sequences. Our model has four built-in neural network constructions: convolution layer captures regulatory motifs, recurrent layer captures a regulatory grammar, category attention layer selects corresponding valid features for different functions and category dense layer classifies predictive labels with selected features of regulatory functions. Importantly, we compare our novel method, DeepATT, with existing outstanding prediction tools, DeepSEA and DanQ. DeepATT performs significantly better than other existing tools for identifying DNA functions, at least increasing |$1.6\%$| area under precision recall. Furthermore, we can mine the important correlation among different DNA functions according to the category attention module. Moreover, our novel model can greatly reduce the number of parameters by the mechanism of attention and locally connected, on the basis of ensuring accuracy. [ABSTRACT FROM AUTHOR]

Abstract Background Staphylococcus aureus is the major cause of hospital-acquired and community-acquired pneumonia. Host defense to S.aureus infection is largely mediated by the innate immune system. γδ T cells play an important role in innate immunity to many infectious diseases. However, less is known about the role of these cells during S.aureus-induced pneumonia. In this study, we examined the response and the role of γδ T cells to pulmonary S.aureus infection. Results Mice infected with S. aureus intranasally showed rapid γδ T cells accumulation in the lung. Deficiency of γδ T cells led to attenuated bacterial clearance and less tissue damage in lung compared with WT mice. Moreover, TCR-δ−/− mice exhibited impaired neutrophil recruitment and reduced cytokine production at the site of infection. The γδ T cells in response to pulmonary S. aureus infection mainly secreted IL-17 and γδ T cells deficiency reduced IL-17 production, which might regulate the production of neutrophil-inducing cytokine/chemokine in the S. aureus-infected lungs. Conclusions Accumulation of γδ T cells in the lungs to S. aureus infection is beneficial for bacteria clearance and also contributes to the tissue damage. These cells were the primary source of IL-17, which might influence the recruitment of neutrophils at the early stage of infection.

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. [ABSTRACT FROM AUTHOR]

Deep learning has been increasingly used in bioinformatics, especially in sequence-based protein prediction tasks, as large amounts of biological data are available and deep learning techniques have been developed rapidly in recent years. For sequence-based protein prediction tasks, the selection of a suitable model architecture is essential, whereas sequence data representation is a major factor in controlling model performance. Here, we summarized all the main approaches that are used to represent protein sequence data (amino acid sequence encoding or embedding), which include end-to-end embedding methods, non-contextual embedding methods and embedding methods that use transfer learning and others that are applied for some specific tasks (such as protein sequence embedding based on extracted features for protein structure predictions and graph convolutional network-based embedding for drug discovery tasks). We have also reviewed the architectures of various types of embedding models theoretically and the development of these types of sequence embedding approaches to facilitate researchers and users in selecting the model that best suits their requirements. [ABSTRACT FROM AUTHOR]

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM%5fdata/ some sample data on protein, RNA and DNA modification can be downloaded from that website. [ABSTRACT FROM AUTHOR]

Motivation The Golgi apparatus has a key functional role in protein biosynthesis within the eukaryotic cell with malfunction resulting in various neurodegenerative diseases. For a better understanding of the Golgi apparatus, it is essential to identification of sub-Golgi protein localization. Although some machine learning methods have been used to identify sub-Golgi localization proteins by sequence representation fusion, more accurate sub-Golgi protein identification is still challenging by existing methodology. Results we developed a protein sub-Golgi localization identification protocol using deep representation learning features with 107 dimensions. By this protocol, we demonstrated that instead of multi-type protein sequence feature representation fusion as in previous state-of-the-art sub-Golgi-protein localization classifiers, it is sufficient to exploit only one type of feature representation for more accurately identification of sub-Golgi proteins. Compared with independent testing results for benchmark datasets, our protocol is able to perform generally, reliably and robustly for sub-Golgi protein localization prediction. Availabilityand implementation A use-friendly webserver is freely accessible at http://isGP-DRLF.aibiochem.net and the prediction code is accessible at https://github.com/zhibinlv/isGP-DRLF. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In Earth science, information science, space science, and other disciplines, scientists use the land surface parameter inversion method in their work, applying this to the atmosphere, vegetation, soil, drought, and so on. Multidisciplinary experts sometimes collaborate on a particular application. However, these remote sensing models do not have a unified method of description and management and cannot effectively achieve the sharing of models and data resources. It is also hard to meet user demand for global data and models in the current state, especially in the face of global problems and long-term series problems. In this paper, we examine the scientific questions of the computability and scalability of remote sensing models. This paper adopts a data dependency approach to describe a remote sensing model and implements a hierarchical unified description and management method using modelling based on four layers: a data-processing view, an atomic model view, an on-demand resource package view, and a workflow view. We choose three typical remote sensing models for disaster monitoring as use cases and describe the practical application process of the proposed method. The results demonstrate the advantages and powerful capabilities of this efficient method. [ABSTRACT FROM AUTHOR]

Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics; explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications; and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance. [ABSTRACT FROM AUTHOR]

Human protein subcellular localization has an important research value in biological processes, also in elucidating protein functions and identifying drug targets. Over the past decade, a number of protein subcellular localization prediction tools have been designed and made freely available online. The purpose of this paper is to summarize the progress of research on the subcellular localization of human proteins in recent years, including commonly used data sets proposed by the predecessors and the performance of all selected prediction tools against the same benchmark data set. We carry out a systematic evaluation of several publicly available subcellular localization prediction methods on various benchmark data sets. Among them, we find that mLASSO-Hum and pLoc-mHum provide a statistically significant improvement in performance, as measured by the value of accuracy, relative to the other methods. Meanwhile, we build a new data set using the latest version of Uniprot database and construct a new GO-based prediction method HumLoc-LBCI in this paper. Then, we test all selected prediction tools on the new data set. Finally, we discuss the possible development directions of human protein subcellular localization. Availability: The codes and data are available from http://www.lbci.cn/syn/. [ABSTRACT FROM AUTHOR]

Motivation Methylation and transcription factors (TFs) are part of the mechanisms regulating gene expression. However, the numerous mechanisms regulating the interactions between methylation and TFs remain unknown. We employ machine-learning techniques to discover the characteristics of TFs that bind to methylation sites. Results The classical machine-learning analysis process focuses on improving the performance of the analysis method. Conversely, we focus on the functional properties of the TF sequences. We obtain the principal properties of TFs, namely, the basic polar and hydrophobic Ile amino acids affecting the interaction between TFs and methylated DNA. The recall of the positive instances is 0.878 when their basic polar value is >0.1743. Both basic polar and hydrophobic Ile amino acids distinguish 74% of TFs bound to methylation sites. Therefore, we infer that basic polar amino acids affect the interactions of TFs with methylation sites. Based on our results, the role of the hydrophobic Ile residue is consistent with that described in previous studies, and the basic polar amino acids may also be a key factor modulating the interactions between TFs and methylation. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Peptide is a promising candidate for therapeutic and diagnostic development due to its great physiological versatility and structural simplicity. Thus, identifying therapeutic peptides and investigating their properties are fundamentally important. As an inexpensive and fast approach, machine learning-based predictors have shown their strength in therapeutic peptide identification due to excellences in massive data processing. To date, no reported therapeutic peptide predictor can perform high-quality generic prediction and informative physicochemical properties (IPPs) identification simultaneously. Results In this work, P hysicochemical P roperty-based T herapeutic P eptide P redictor (PPTPP), a Random Forest-based prediction method was presented to address this issue. A novel feature encoding and learning scheme were initiated to produce and rank physicochemical property-related features. Besides being capable of predicting multiple therapeutics peptides with high comparability to established predictors, the presented method is also able to identify peptides' informative IPP. Results presented in this work not only illustrated the soundness of its working capacity but also demonstrated its potential for investigating other therapeutic peptides. Availability and implementation https://github.com/YPZ858/PPTPP. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has generated numerous data and renewed our understanding of biological phenomena at the cellular scale. Identification of cell types has been one of the most prevalent means for interpreting scRNA-seq data, based upon which connections are made between the transcriptome and phenotype. Herein, we attempt to review the methods and tools that dedicate to the task regarding their feature and usage and look at the possibilities for scRNA-seq development in the near future. [ABSTRACT FROM AUTHOR]

Identification of disease-associated circular RNAs (circRNAs) is of critical importance, especially with the dramatic increase in the amount of circRNAs. However, the availability of experimentally validated disease-associated circRNAs is limited, which restricts the development of effective computational methods. To our knowledge, systematic approaches for the prediction of disease-associated circRNAs are still lacking. In this study, we propose the use of deep forests combined with positive-unlabeled learning methods to predict potential disease-related circRNAs. In particular, a heterogeneous biological network involving 17 961 circRNAs, 469 miRNAs, and 248 diseases was constructed, and then 24 meta-path-based topological features were extracted. We applied 5-fold cross-validation on 15 disease data sets to benchmark the proposed approach and other competitive methods and used Recall@k and PRAUC@k to evaluate their performance. In general, our method performed better than the other methods. In addition, the performance of all methods improved with the accumulation of known positive labels. Our results provided a new framework to investigate the associations between circRNA and disease and might improve our understanding of its functions. [ABSTRACT FROM AUTHOR]

Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs. [ABSTRACT FROM AUTHOR]

Acinetobacter baumannii (A. baumannii) is becoming a common global concern due to the emergence of multi-drug or pan-drug resistant strains. Confronting the issue of antimicrobial resistance by developing vaccines against the resistant pathogen is becoming a common strategy. In this study, different methods for preparing A. baumannii outer membrane vesicles (AbOMVs) vaccines were developed. sOMV (spontaneously released AbOMV) was extracted from the culture supernatant, while SuOMV (sucrose-extracted AbOMV) and nOMV (native AbOMV) were prepared from the bacterial cells. Three AbOMVs exhibited significant differences in yield, particle size, protein composition, and LPS/DNA content. To compare the protective efficacy of the three AbOMVs, groups of mice were immunized either intramuscularly or intranasally with each AbOMV. Vaccination via both routes conferred significant protection against lethal and sub-lethal A. baumannii challenge. Moreover, intranasal vaccination provided more robust protection, which may be attributed to the induction of significant sIgA response in mucosal sites. Among the three AbOMVs, SuOMV elicited the highest level of protective immunity against A. baumannii infection, whether intramuscular or intranasal immunization, which was characterized by the expression of the most profound specific serum IgG or mucosal sIgA. Taken together, the preparation method had a significant effect on the yield, morphology, and composition of AbOMVs, that further influenced the protective effect against A. baumannii infection. [ABSTRACT FROM AUTHOR]

Motivation Cell-penetrating peptides (CPPs) are a vehicle for transporting into living cells pharmacologically active molecules, such as short interfering RNAs, nanoparticles, plasmid DNAs and small peptides, thus offering great potential as future therapeutics. Existing experimental techniques for identifying CPPs are time-consuming and expensive. Thus, the prediction of CPPs from peptide sequences by using computational methods can be useful to annotate and guide the experimental process quickly. Many machine learning-based methods have recently emerged for identifying CPPs. Although considerable progress has been made, existing methods still have low feature representation capabilities, thereby limiting further performance improvements. Results We propose a method called StackCPPred, which proposes three feature methods on the basis of the pairwise energy content of the residue as follows: RECM-composition, PseRECM and RECM–DWT. These features are used to train stacking-based machine learning methods to effectively predict CPPs. On the basis of the CPP924 and CPPsite3 datasets with jackknife validation, StackDPPred achieved 94.5% and 78.3% accuracy, which was 2.9% and 5.8% higher than the state-of-the-art CPP predictors, respectively. StackCPPred can be a powerful tool for predicting CPPs and their uptake efficiency, facilitating hypothesis-driven experimental design and accelerating their applications in clinical therapy. Availability and implementation Source code and data can be downloaded from https://github.com/Excelsior511/StackCPPred. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The completion of the rice genome sequence paved the way for rice functional genomics research. Additionally, the functional characterization of transcription factors is currently a popular and crucial objective among researchers. Transcription factors are one of the groups of proteins that bind to either enhancer or promoter regions of genes to regulate expression. On the basis of several typical examples of transcription factor analyses, we herein summarize selected research strategies and methods and introduce their advantages and disadvantages. This review may provide some theoretical and technical guidelines for future investigations of transcription factors, which may be helpful to develop new rice varieties with ideal traits. [ABSTRACT FROM AUTHOR]

A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network. [ABSTRACT FROM AUTHOR]

Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20–25 residues long than peptides in other length ranges. [ABSTRACT FROM AUTHOR]

Motivation: The biological function of N 6-methyladenine DNA (6mA) in plants is largely unknown. Rice is one of the most important crops worldwide and is a model species for molecular and genetic studies. There are few methods for 6mA site recognition in the rice genome, and an effective computational method is needed. Results: In this paper, we propose a new computational method called 6mA-Pred to identify 6mA sites in the rice genome. 6mA-Pred employs a feature fusion method to combine advantageous features from other methods and thus obtain a new feature to identify 6mA sites. This method achieved an accuracy of 87.27% in the identification of 6mA sites with 10-fold cross-validation and achieved an accuracy of 85.6% in independent test sets. [ABSTRACT FROM AUTHOR]

Sequence clustering is a basic bioinformatics task that is attracting renewed attention with the development of metagenomics and microbiomics. The latest sequencing techniques have decreased costs and as a result, massive amounts of DNA/RNA sequences are being produced. The challenge is to cluster the sequence data using stable, quick and accurate methods. For microbiome sequencing data, 16S ribosomal RNA operational taxonomic units are typically used. However, there is often a gap between algorithm developers and bioinformatics users. Different software tools can produce diverse results and users can find them difficult to analyze. Understanding the different clustering mechanisms is crucial to understanding the results that they produce. In this review, we selected several popular clustering tools, briefly explained the key computing principles, analyzed their characters and compared them using two independent benchmark datasets. Our aim is to assist bioinformatics users in employing suitable clustering tools effectively to analyze big sequencing data. Related data, codes and software tools were accessible at the link http://lab.malab.cn/∼lg/clustering/. [ABSTRACT FROM AUTHOR]

Due to the concerted efforts to utilize the microbial features to improve disease prediction capabilities, automated machine learning (AutoML) systems aiming to get rid of the tediousness in manually performing ML tasks are in great demand. Here we developed mAML, an ML model-building pipeline, which can automatically and rapidly generate optimized and interpretable models for personalized microbiome-based classification tasks in a reproducible way. The pipeline is deployed on a web-based platform, while the server is user-friendly and flexible and has been designed to be scalable according to the specific requirements. This pipeline exhibits high performance for 13 benchmark datasets including both binary and multi-class classification tasks. In addition, to facilitate the application of mAML and expand the human disease-related microbiome learning repository, we developed GMrepo ML repository (GMrepo Microbiome Learning repository) from the GMrepo database. The repository involves 120 microbiome-based classification tasks for 85 human-disease phenotypes referring to 12 429 metagenomic samples and 38 643 amplicon samples. The mAML pipeline and the GMrepo ML repository are expected to be important resources for researches in microbiology and algorithm developments. Database URL : http://lab.malab.cn/soft/mAML [ABSTRACT FROM AUTHOR]

The advent of high-throughput genomic technologies has resulted in the accumulation of massive amounts of genomic information. However, biologists are challenged with how to effectively analyze these data. Machine learning can provide tools for better and more efficient data analysis. Unfortunately, because many plant biologists are unfamiliar with machine learning, its application in plant molecular studies has been restricted to a few species and a limited set of algorithms. Thus, in this study, we provide the basic steps for developing machine learning frameworks and present a comprehensive overview of machine learning algorithms and various evaluation metrics. Furthermore, we introduce sources of important curated plant genomic data and R packages to enable plant biologists to easily and quickly apply appropriate machine learning algorithms in their research. Finally, we discuss current applications of machine learning algorithms for identifying various genes related to resistance to biotic and abiotic stress. Broad application of machine learning and the accumulation of plant sequencing data will advance plant molecular studies. [ABSTRACT FROM AUTHOR]

Quorum-sensing peptides (QSPs) are the signal molecules that are closely associated with diverse cellular processes, such as cell–cell communication, and gene expression regulation in Gram-positive bacteria. It is therefore of great importance to identify QSPs for better understanding and in-depth revealing of their functional mechanisms in physiological processes. Machine learning algorithms have been developed for this purpose, showing the great potential for the reliable prediction of QSPs. In this study, several sequence-based feature descriptors for peptide representation and machine learning algorithms are comprehensively reviewed, evaluated and compared. To effectively use existing feature descriptors, we used a feature representation learning strategy that automatically learns the most discriminative features from existing feature descriptors in a supervised way. Our results demonstrate that this strategy is capable of effectively capturing the sequence determinants to represent the characteristics of QSPs, thereby contributing to the improved predictive performance. Furthermore, wrapping this feature representation learning strategy, we developed a powerful predictor named QSPred-FL for the detection of QSPs in large-scale proteomic data. Benchmarking results with 10-fold cross validation showed that QSPred-FL is able to achieve better performance as compared to the state-of-the-art predictors. In addition, we have established a user-friendly webserver that implements QSPred-FL, which is currently available at http://server.malab.cn/QSPred-FL. We expect that this tool will be useful for the high-throughput prediction of QSPs and the discovery of important functional mechanisms of QSPs. [ABSTRACT FROM AUTHOR]

As one of the most important fundamental problems in protein sequence analysis, protein remote homology detection is critical for both theoretical research (protein structure and function studies) and real world applications (drug design). Although several computational predictors have been proposed, their detection performance is still limited. In this study, we treat protein remote homology detection as a document retrieval task, where the proteins are considered as documents and its aim is to find the highly related documents with the query documents in a database. A protein similarity network was constructed based on the true labels of proteins in the database, and the query proteins were then connected into the network based on the similarity scores calculated by three ranking methods, including PSI-BLAST, Hmmer and HHblits. The PageRank algorithm and Hyperlink-Induced Topic Search (HITS) algorithm were respectively performed on this network to move the homologous proteins of query proteins to the neighbors of the query proteins in the network. Finally, PageRank and HITS algorithms were combined, and a predictor called HITS-PR-HHblits was proposed to further improve the predictive performance. Tested on the SCOP and SCOPe benchmark datasets, the experimental results showed that the proposed protocols outperformed other state-of-the-art methods. For the convenience of the most experimental scientists, a web server for HITS-PR-HHblits was established at http://bioinformatics.hitsz.edu.cn/HITS-PR-HHblits , by which the users can easily get the results without the need to go through the mathematical details. The HITS-PR-HHblits predictor is a protocol for protein remote homology detection using different sets of programs, which will become a very useful computational tool for proteome analysis. [ABSTRACT FROM AUTHOR]

Motivation Accurate identification of N4-methylcytosine (4mC) modifications in a genome wide can provide insights into their biological functions and mechanisms. Machine learning recently have become effective approaches for computational identification of 4mC sites in genome. Unfortunately, existing methods cannot achieve satisfactory performance, owing to the lack of effective DNA feature representations that are capable to capture the characteristics of 4mC modifications. Results In this work, we developed a new predictor named 4mcPred-IFL, aiming to identify 4mC sites. To represent and capture discriminative features, we proposed an iterative feature representation algorithm that enables to learn informative features from several sequential models in a supervised iterative mode. Our analysis results showed that the feature representations learnt by our algorithm can capture the discriminative distribution characteristics between 4mC sites and non-4mC sites, enlarging the decision margin between the positives and negatives in feature space. Additionally, by evaluating and comparing our predictor with the state-of-the-art predictors on benchmark datasets, we demonstrate that our predictor can identify 4mC sites more accurately. Availability and implementation The user-friendly webserver that implements the proposed 4mcPred-IFL is well established, and is freely accessible at http://server.malab.cn/4mcPred-IFL. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Tumor metastasis is the key reason for the high mortality rate of tumor. Growing number of scholars have begun to pay attention to the research on tumor metastasis and have achieved satisfactory results in this field. The advent of the era of sequencing has enabled us to study cancer metastasis at the molecular level, which is essential for understanding the molecular mechanism of metastasis, identifying diagnostic markers and therapeutic targets and guiding clinical decision-making. We reviewed the metastasis-related studies using sequencing data, covering detection of metastasis origin sites, determination of metastasis potential and identification of distal metastasis sites. These findings include the discovery of relevant markers and the presentation of prediction tools. Finally, we discussed the challenge of studying metastasis considering the difficulty of obtaining metastatic cancer data, the complexity of tumor heterogeneity and the uncertainty of sample labels. [ABSTRACT FROM AUTHOR]

Motivation Prediction of therapeutic peptides is critical for the discovery of novel and efficient peptide-based therapeutics. Computational methods, especially machine learning based methods, have been developed for addressing this need. However, most of existing methods are peptide-specific; currently, there is no generic predictor for multiple peptide types. Moreover, it is still challenging to extract informative feature representations from the perspective of primary sequences. Results In this study, we have developed PEPred-Suite, a bioinformatics tool for the generic prediction of therapeutic peptides. In PEPred-Suite, we introduce an adaptive feature representation strategy that can learn the most representative features for different peptide types. To be specific, we train diverse sequence-based feature descriptors, integrate the learnt class information into our features, and utilize a two-step feature optimization strategy based on the area under receiver operating characteristic curve to extract the most discriminative features. Using the learnt representative features, we trained eight random forest models for eight different types of functional peptides, respectively. Benchmarking results showed that as compared with existing predictors, PEPred-Suite achieves better and robust performance for different peptides. As far as we know, PEPred-Suite is currently the first tool that is capable of predicting so many peptide types simultaneously. In addition, our work demonstrates that the learnt features can reliably predict different peptides. Availability and implementation The user-friendly webserver implementing the proposed PEPred-Suite is freely accessible at http://server.malab.cn/PEPred-Suite. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary Removing duplicate and near-duplicate reads, generated by high-throughput sequencing technologies, is able to reduce computational resources in downstream applications. Here we develop minirmd, a de novo tool to remove duplicate reads via multiple rounds of clustering using different length of minimizer. Experiments demonstrate that minirmd removes more near-duplicate reads than existing clustering approaches and is faster than existing multi-core tools. To the best of our knowledge, minirmd is the first tool to remove near-duplicates on reverse-complementary strand. Availability and implementation https://github.com/yuansliu/minirmd. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Posttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities. [ABSTRACT FROM AUTHOR]

Abstract Vanadium (V)-contaminated soil poses health risks to plants, animals, and humans via both direct exposure and through the food chain. Stabilization treatment of metal-contaminated soil can chemically convert metal contaminants into less soluble, mobile, and toxic forms. However, the stabilization mechanisms of V-contaminated soil have not been thoroughly investigated. Therefore, we performed geochemical modeling of V-contaminated soil stabilized with the common binders calcium oxide (CaO) and ferrous sulfate (FeSO 4), as well as their mixture, using Visual MINTEQ software. The results were validated and exhibited good agreement with experimental results. For CaO, the formation of Ca 2 V 2 O 7 (s) and Ca 3 (VO 4) 2 ·4H 2 O(s) under mild and strong alkaline conditions (pH = 8.0–11.5 and 11.5–12.5), respectively, were predicted as the main immobilization routes. For FeSO 4 , there appeared to be three reaction routes, corresponding to approaches A, B, and C, during the stabilization process. In the simulation, approach C (adsorption of V(V) onto ferrihydrite) was undervalued, whereas approaches A (formation of Fe(VO 3) 2 (s)) and B (reduction of V(V) into V(IV) to form V 2 O 4 (s) or adsorb onto soil organic matter) were overvalued. Among the three approaches, approach C had a dominant role and exhibited good agreement with the experimental results. Additionally, soil pH and the saturation index of precipitation had major roles in the stabilization process. The optimal pH ranges for the stabilization of V-contaminated soil using CaO and FeSO 4 were pH = 9.5–12.5 and pH = 4.0–5.0, respectively. Graphical abstract fx1 Highlights • Geochemical modeling on V-contaminated soil stabilization were simulated and validated. • Three possible approaches were proposed for V-contaminated soil stabilized by FeSO 4. • Saturation index and soil pH were assessed to play a key role in stabilization process. • Optimal pH ranges of 9.5–12.5 and 4.0–5.0 were advised for CaO and FeSO4, respectively. [ABSTRACT FROM AUTHOR]