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We present ANANASTRA, https://ananastra.autosome.org, a web server for the identification and annotation of regulatory single-nucleotide polymorphisms (SNPs) with allele-specific binding events. ANANASTRA accepts a list of dbSNP IDs or a VCF file and reports allele-specific binding (ASB) sites of particular transcription factors or in specific cell types, highlighting those with ASBs significantly enriched at SNPs in the query list. ANANASTRA is built on top of a systematic analysis of allelic imbalance in ChIP-Seq experiments and performs the ASB enrichment test against background sets of SNPs found in the same source experiments as ASB sites but not displaying significant allelic imbalance. We illustrate ANANASTRA usage with selected case studies and expect that ANANASTRA will help to conduct the follow-up of GWAS in terms of establishing functional hypotheses and designing experimental verification.

BioUML (https://www.biouml.org)—is a web-based integrated platform for systems biology and data analysis. It supports visual modelling and construction of hierarchical biological models that allow us to construct the most complex modular models of blood pressure regulation, skeletal muscle metabolism, COVID-19 epidemiology. BioUML has been integrated with git repositories where users can store their models and other data. We have also expanded the capabilities of BioUML for data analysis and visualization of biomedical data: (i) any programs and Jupyter kernels can be plugged into the BioUML platform using Docker technology; (ii) BioUML is integrated with the Galaxy and Galaxy Tool Shed; (iii) BioUML provides two-way integration with R and Python (Jupyter notebooks): scripts can be executed on the BioUML web pages, and BioUML functions can be called from scripts; (iv) using plug-in architecture, specialized viewers and editors can be added. For example, powerful genome browsers as well as viewers for molecular 3D structure are integrated in this way; (v) BioUML supports data analyses using workflows (own format, Galaxy, CWL, BPMN, nextFlow). Using these capabilities, we have initiated a new branch of the BioUML development—u-science—a universal scientific platform that can be configured for specific research requirements.

Patients with COVID-19 may develop pneumonia, severe symptoms of acute respiratory distress syndrome, and multiple organ failure. Nevertheless, the variety of forms of this disease, requires further research on the pathogenesis of this disease. Based on the analysis of published data on the concentrations of SARS-CoV-2 in biological fluids of the nasopharynx, lungs and intestines and using a developed modular model of the virus distribution in human tissue and organs, an assessment of the SARS-CoV-2 reproduction in various compartments of the body is presented. Most of viral particles can enter into the esophagus from nasopharynx. Entering viral particles into the gastrointestinal tract will obviously be accompanied by infection of the intestinal epithelium and accumulation of the virus in the intestinal lumen in an amount proportional their secretory and protein-synthetic activities. The relatively low concentration of SARS-CoV-2 in tissues implies an essential role of transport processes and redistribution of the virus from nasopharynx and intestines to lungs. The model simulations also supposes that sanitation of the nasopharynx mucosa at the initial stage of the infectious process considering inhibition of the virus accumulation by means of cellular and humoral responses has prospects for the use in medicine practice.

SEIR (Susceptible - Exposed - Infected - Recovered) approach is a classic modeling method that has frequently been applied to the study of infectious disease epidemiology. However, in the vast majority of SEIR models and models derived from them transitions from one population group to another are described using the mass-action law which assumes population homogeneity. That causes some methodological limitations or even drawbacks, particularly inability to reproduce observable dynamics of key characteristics of infection such as, for example, the incubation period and progression of the disease’s symptoms which require considering different time scales as well as probabilities of different disease trajectories. In this paper, we propose an alternative approach to simulate the epidemic dynamics that is based on a system of differential equations with time delays to precisely reproduce a duration of infectious processes (e.g. incubation period of the virus) and competing processes like transition from infected state to the hospitalization or recovery. The suggested modeling approach is fundamental and can be applied to the study of many infectious disease epidemiology. However, due to the urgency of the COVID-19 pandemic we have developed and calibrated the delay-based model of the epidemic in Germany and France using the BioUML platform. Additionally, the stringency index was used as a generalized characteristic of the non-pharmaceutical government interventions implemented in corresponding countries to contain the virus spread. The numerical analysis of the calibrated model demonstrates that adequate simulation of each new wave of the SARS-CoV-2 virus spread requires dynamic changes in the parameter values during the epidemic like reduction of the population adherence to non-pharmaceutical interventions or enhancement of the infectivity parameter caused by an emergence of novel virus strains with higher contagiousness than original one. Both models may be accessed and simulated at https://gitlab.sirius-web.org/covid-19/dde-epidemiology-model utilizing visual representation as well as Jupyter Notebook.

Understanding the basic mechanisms of transcription regulation is a major challenge in modern biology. Regulation of transcription is a complex process in which transcription factors (TFs) play a key role. Chromatin immunoprecipitation followed by high throughput sequencing is a widely and intensively used experimental technology for the identification of TF binding sites (TFBSs). Nowadays, there are tens or hundreds of ChIP-seq datasets measured for the same transcription factor. Meta-processing of such datasets into an integrated dataset is relevant. We have developed a novel method for creating these integrated datasets of TFBSs. This method consists of a three-stage application of the Rank Aggregation approach. The identified TFBSs can be sorted to further select the most reliable TFBSs. We have found a high saturation of site motifs in the most reliable TFBSs. We have also demonstrated that the most reliable TFBSs prefer to be located in open chromatin regions.

GEO database contains a lot of different experiments about transcription regulation. Most part of such experiments (ChIP-seq, DNase-seq and Histone-ChIP-seq) for 9 species (Homo sapiens, Mus musculus, Rattus norvegicus, Danio rerio, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae, Schizosaccharomyces pombe and Arabidopsis thaliana) were annotated and uniformly processed in GTRD database. Here we are describing the latest advances in these data annotations for GTRD database to formalize experimental data: hierarchical dictionaries of cell types and experimental factors. This approach helps us to integrate experimental data by cell lines (cell types and tissues) and experimental conditions as well as simplifies searching for relevant information for a user.

Background Leishmania (L.) are intracellular protozoan parasites able to survive and replicate in the hostile phagolysosomal environment of infected macrophages. They cause leishmaniasis, a heterogeneous group of worldwide-distributed affections, representing a paradigm of neglected diseases that are mainly embedded in impoverished populations. To establish successful infection and ensure their own survival, Leishmania have developed sophisticated strategies to subvert the host macrophage responses. Despite a wealth of gained crucial information, these strategies still remain poorly understood. MicroRNAs (miRNAs), an evolutionarily conserved class of endogenous 22-nucleotide non-coding RNAs, are described to participate in the regulation of almost every cellular process investigated so far. They regulate the expression of target genes both at the levels of mRNA stability and translation; changes in their expression have a profound effect on their target transcripts. Methodology/Principal Findings We report in this study a comprehensive analysis of miRNA expression profiles in L. major-infected human primary macrophages of three healthy donors assessed at different time-points post-infection (three to 24 h). We show that expression of 64 out of 365 analyzed miRNAs was consistently deregulated upon infection with the same trends in all donors. Among these, several are known to be induced by TLR-dependent responses. GO enrichment analysis of experimentally validated miRNA-targeted genes revealed that several pathways and molecular functions were disturbed upon parasite infection. Finally, following parasite infection, miR-210 abundance was enhanced in HIF-1α-dependent manner, though it did not contribute to inhibiting anti-apoptotic pathways through pro-apoptotic caspase-3 regulation. Conclusions/Significance Our data suggest that alteration in miRNA levels likely plays an important role in regulating macrophage functions following L. major infection. These results could contribute to better understanding of the dynamics of gene expression in host cells during leishmaniasis., Author Summary Leishmania parasites belong to different species, each one characterized by specific vectors and reservoirs, and causes cutaneous or visceral disease(s) of variable clinical presentation and severity. In its mammalian host, the parasite is an obligate intracellular pathogen infecting the monocyte/macrophage lineage. Leishmania have developed ambiguous relationships with macrophages. Indeed, these cells are the shelter of invading parasites, where they will grow and eventually will reside in a silent state for life. But macrophages are also the cells that participate, through the induction of several pro-inflammatory mediators and antigen presentation, to shape the host immune response and ultimately kill the invader. To subvert these anti-parasite responses, Leishmania manipulate the host machinery for their own differentiation and survival. We aimed to evaluate the impact of L. major (the causative agent of zoonotic cutaneous leishmaniasis) infection on deregulation of non-coding miRNAs, a class of important regulators of gene expression. Our results revealed the implication of several miRNAs on macrophage fate upon parasite infection through regulation of different pathways, including cell death. Our findings provided a new insight for understanding mechanisms governing this miRNA deregulation by parasite infection and will help to provide clues for the development of control strategies for this disease.

Using a modular principle of computer hardware as a metaphor, we defined and implemented in the BioUML platform a module concept for biological pathways. BioUML provides a user interface to create modular models and convert them automatically into plain models for further simulations. Using this approach, we created the apoptosis model including 13 modules: death stimuli (TRAIL, CD95L, and TNF-α)-induced activation of caspase-8; survival stimuli (p53, EGF, and NF-κB) regulation; the mitochondria level; cytochrome C- and Smac-induced activation of caspase-3; direct activation of effector caspases by caspase-8 and − 12; PARP and apoptosis execution phase modules. Each module is based on earlier published models and extended by data from the Reactome and TRANSPATH databases. The model ability to simulate the apoptosis-related processes was checked; the modules were validated using experimental data. Availability: http://www.biouml.org/apoptosis.shtml.

Despite wide application of the powerful ChIP-Seq technology for experimental identification of transcription factor (TF) binding sites, the computational prediction of the TF-binding sites is also relevant. Many methods for the prediction of the TF-binding sites have been developed over the last decades. Some of them represent position weight matrix (PWM) approach that is the most common and widely used. However, there exists little guidance in the choice among these methods because of a comprehensive comparison of existing methods is still challenging in practice. Thus, the direct use of the ChIP-Seq data for assessing predictive ability of the methods does not seem advisable because of such reasons as the tethered binding or false positive rates of peak detection algorithms. We have developed computational toolkit for reliable comparison of prediction methods under condition that unknown fraction of the ChIP-Seq data do not contain genuine TF-binding sites. On the base of developed toolkit, we have performed comparative analysis of three existing methods that represent PWM approach. The analysis has revealed that MATCH performed significantly worse than two other methods while common additive method outperformed others.

Motivation and Aim: Non-linear mixed effects (NLME) model is an efficient tool for analyzing of population data and estimating of model parameters. It is widely used in pharmacological modeling, but may be applied to a variety of fields. NLME model contains core structural model which is the mathematical model of studying process (e.g. drug dynamics in organism) and suggests distribution of model parameters across population. nlme is a library written in R language implementing log-likelihood approach for NLME models creation and analysis. Core structural model should be supplied as R function which complicates usage of sophisticated mathematical models. Library nlmeODE allows usage of ODEs inside nlme, but it has certain restrictions on possible models and textual format complicates using of complex ODE systems. Results: We have implemented plugin for BioUML platform for population-based modeling. Plugin includes graphical notation facilitating work with detailed and large-scaled models, parameter estimation is performed by R function, supplied (using rJava) with Java simulator from BioUML. It grants possibility to use any model created in BioUML as a structural model. Executing R script is automatically generated on the basis of the diagram. Availability: Plugin is freely available as a part of BioUML software at www.biouml.org .

Motivation: Systems biology deals with many different types of experimental data representing individual components of biological systems. Behavior of these systems over time could be described using systems of ordinary differential equations (ODE). In order to analyze dynamics of the ODEs and estimate their parameters based on data obtained in different experimental conditions, biologists need a flexible framework that allows them to create dynamic models and perform multi-experiment parameter fitting. Results: We present optimization tools of the BioUML software ( http://biouml.org ) developed for modeling and analysis of biochemical systems. We created optimization plug-in to solve non-linear optimization problems via minimization of the function of deviations between experimental data and model simulation results. Experimental data can be considered as separate sets of time courses or steady states stored in different tab-separated files. BioUML includes several deterministic and stochastic optimization methods which find reasonably accurate solutions faster than the COPASI software. Some of these methods provide constrained optimization and some of them were parallelized.

Motivation: Modeling of complex biological systems such as cells, organs or even whole organisms is not a trivial task because of their intricate structure. Although modern computers allow simulation for quite complex models, such models are difficult to support and work with. Modular approach facilitates the creation of complex models by representing them as combinations of submodels. On the other hand, there are a large number of models describing particular subsystems created by different authors using different formalisms and scales. These models may be reused as “bricks” in the creation of comprehensive overall models. Results: We have developed a modular approach to the modeling of complex biological systems. It includes a formal definition of graphical notation for modular models, an algorithm for the transformation of a modular model into a non-modular model which is appropriate for simulation using standard methods for solving ordinary differential equations (ODE), and an algorithm for simulating modular model based on the agent-model principles. The approach was implemented in software plug-in for BioUML platform. Availability: The developed software and the source code are freely available as a part of BioUML in both standalone and web versions at www.biouml.org .

Nat. Biotechnol. 27 735–741 (2009); published online 7 August 2009; corrected after print 11 August 2009 In the version of this article initially published, the wrong versions of Figures 1, 2 and 3 were used. The error has been corrected in the HTML and PDF versions of the article.