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Background miRNAs regulate the expression of several genes with one miRNA able to target multiple genes and with one gene able to be simultaneously targeted by more than one miRNA. Therefore, it has become indispensable to shorten the long list of miRNA-target interactions to put in the spotlight in order to gain insight into understanding the regulatory mechanism orchestrated by miRNAs in various cellular processes. A reasonable solution is certainly to prioritize miRNA-target interactions to maximize the effectiveness of the downstream analysis. Results We propose a new and easy-to-use web tool MIENTURNET (MicroRNA ENrichment TURned NETwork) that receives in input a list of miRNAs or mRNAs and tackles the problem of prioritizing miRNA-target interactions by performing a statistical analysis followed by a fully featured network-based visualization and analysis. The statistics is used to assess the significance of an over-representation of miRNA-target interactions and then MIENTURNET filters based on the statistical significance associated with each miRNA-target interaction. In addition, the holistic approach of the network theory is used to infer possible evidences of miRNA regulation by capturing emergent properties of the miRNA-target regulatory network that would be not evident through a pairwise analysis of the individual components. Conclusion MIENTURNET offers the possibility to consistently perform both statistical and network-based analyses by using only a single tool leading to a more effective prioritization of the miRNA-target interactions. This has the potential to avoid researchers without computational and informatics skills to navigate multiple websites and thus to independently investigate miRNA activity in every cellular process of interest in an easy and at the same time exhaustive way thanks to the intuitive web interface. The web application along with a well-documented and comprehensive user guide are freely available at http://userver.bio.uniroma1.it/apps/mienturnet/ without any login requirement.

Background A variety of high-throughput analyses, such as transcriptome, proteome, and metabolome analysis, have been developed, producing unprecedented amounts of omics data. These studies generate large gene lists, of which the biological significance shall be deeply understood. However, manually interpreting these lists is difficult, especially for non-bioinformatics-savvy scientists. Results We developed an R package and a corresponding web server—Genekitr, to assist biologists in exploring large gene sets. Genekitr comprises four modules: gene information retrieval, ID (identifier) conversion, enrichment analysis and publication-ready plotting. Currently, the information retrieval module can retrieve information on up to 23 attributes for genes of 317 organisms. The ID conversion module assists in ID-mapping of genes, probes, proteins, and aliases. The enrichment analysis module organizes 315 gene set libraries in different biological contexts by over-representation analysis and gene set enrichment analysis. The plotting module performs customizable and high-quality illustrations that can be used directly in presentations or publications. Conclusions This web server tool will make bioinformatics more accessible to scientists who might not have programming expertise, allowing them to perform bioinformatics tasks without coding.

Background The Transmembrane Serine Protease 2 (TMPRSS2) of human cell plays a significant role in proteolytic cleavage of SARS-Cov-2 coronavirus spike protein and subsequent priming to the receptor ACE2. Approaching TMPRSS2 as a therapeutic target for the inhibition of SARS-Cov-2 infection is highly promising. Hence, in the present study, we docked the binding efficacy of ten naturally available phyto compounds with known anti-viral potential with TMPRSS2. The aim is to identify the best phyto compound with a high functional affinity towards the active site of the TMPRSS2 with the aid of two different docking software. Molecular Dynamic Simulations were performed to analyse the conformational space of the binding pocket of the target protein with selected molecules. Results Docking analysis using PyRx version 0.8 along with AutoDockVina reveals that among the screened phyto compounds, Genistein shows the maximum binding affinity towards the hydrophobic substrate-binding site of TMPRSS2 with three hydrogen bonds interaction ( − 7.5 kcal/mol). On the other hand, molecular docking analysis using Schrodinger identified Quercetin as the most potent phyto compound with a maximum binding affinity towards the hydrophilic catalytic site of TMPRSS2 ( − 7.847 kcal/mol) with three hydrogen bonds interaction. The molecular dynamics simulation reveals that the Quercetin-TMPRSS complex is stable until 50 ns and forms stable interaction with the protein ( − 22.37 kcal/mol of MM-PBSA binding free energy). Genistein creates a weak interaction with the loop residues and hence has an unstable binding and exits from the binding pocket. Conclusion The compounds, Quercetin and Genistein, can inhibit the TMPRSS2 guided priming of the spike protein. The compounds could reduce the interaction of the host cell with the type I transmembrane glycoprotein to prevent the entry of the virus. The critical finding is that compared to Genistein, Quercetin exhibits higher binding affinity with the catalytic unit of TMPRSS2 and forms a stable complex with the target. Thus, enhancing our innate immunity by consuming foods rich in Quercetin and Genistein or developing a novel drug in the combination of Quercetin and Genistein could be the brilliant choices to prevent SARS-Cov-2 infection when we consider the present chaos associated with vaccines and anti-viral medicines.

Whole genome sequencing (WGS) of foodborne pathogens has become an effective method for investigating the information contained in the genome sequence of bacterial pathogens. In addition, its highly discriminative power enables the comparison of genetic relatedness between bacteria even on a sub-species level. For this reason, WGS is being implemented worldwide and across sectors (human, veterinary, food, and environment) for the investigation of disease outbreaks, source attribution, and improved risk characterization models. In order to extract relevant information from the large quantity and complex data produced by WGS, a host of bioinformatics tools has been developed, allowing users to analyze and interpret sequencing data, starting from simple gene-searches to complex phylogenetic studies. Depending on the research question, the complexity of the dataset and their bioinformatics skill set, users can choose between a great variety of tools for the analysis of WGS data. In this review, we describe the relevant approaches for phylogenomic studies for outbreak studies and give an overview of selected tools for the characterization of foodborne pathogens based on WGS data. Despite the efforts of the last years, harmonization and standardization of typing tools are still urgently needed to allow for an easy comparison of data between laboratories, moving towards a one health worldwide surveillance system for foodborne pathogens.

Background Understanding and comparing three-dimensional (3D) structures of proteins can advance bioinformatics, molecular biology, and drug discovery. While 3D models offer detailed insights, comparing multiple structures simultaneously remains challenging, especially on two-dimensional (2D) displays. Existing 2D visualization tools lack standardized approaches for pipelined inspection of large protein sets, limiting their utility in large-scale pre-filtering. Results We introduce FlatProt, a tool designed to complement 3D viewers by enabling standardized 2D visualization of individual protein structures or large sets thereof. By including Foldseek-based family rotation alignment or an inertia-based fallback, FlatProt creates consistent and scalable visual representations for user-defined protein structures. It supports domain-aware decomposition, family-level overlays, and lightweight visual abstraction of secondary structures. FlatProt processes proteins efficiently, as showcased on a subset of the human-proteome. Conclusion FlatProt provides clear, consistent, user-friendly visualizations that support rapid, comparative inspection of protein structures at scale. By bridging the gap between interactive 3D tools and static visual summaries, it enables users to explore conserved features, detect outliers, and prioritize structures for further analysis. Availability GitHub ( https://github.com/t03i/FlatProt ); Zenodo ( https://doi.org/10.5281/zenodo.15697296 ).

In biomedical research, validating a scientific discovery hinges on the reproducibility of its experimental results. However, in genomics, the definition and implementation of reproducibility remain imprecise. We argue that genomic reproducibility, defined as the ability of bioinformatics tools to maintain consistent results across technical replicates, is essential for advancing scientific knowledge and medical applications. Initially, we examine different interpretations of reproducibility in genomics to clarify terms. Subsequently, we discuss the impact of bioinformatics tools on genomic reproducibility and explore methods for evaluating these tools regarding their effectiveness in ensuring genomic reproducibility. Finally, we recommend best practices to improve genomic reproducibility.

Background The precise prediction of transcription factor binding sites (TFBSs) is pivotal for unraveling the gene regulatory networks underlying biological processes. While numerous tools have emerged for in silico TFBS prediction in recent years, the evolving landscape of computational biology necessitates thorough assessments of tool performance to ensure accuracy and reliability. Only a limited number of studies have been conducted to evaluate the performance of TFBS prediction tools comprehensively. Thus, the present study focused on assessing twelve widely used TFBS prediction tools and four de novo motif discovery tools using a benchmark dataset comprising real, generic, Markov, and negative sequences. TFBSs of Arabidopsis thaliana and Homo sapiens genomes downloaded from the JASPAR database were implanted in these sequences and the performance of tools was evaluated using several statistical parameters at different overlap percentages between the lengths of known and predicted binding sites. Results Overall, the Multiple Cluster Alignment and Search Tool (MCAST) emerged as the best TFBS prediction tool, followed by Find Individual Motif Occurrences (FIMO) and MOtif Occurrence Detection Suite (MOODS). In addition, MotEvo and Dinucleotide Weight Tensor Toolbox (DWT-toolbox) demonstrated the highest sensitivity in identifying TFBSs at 90% and 80% overlap. Further, MCAST and DWT-toolbox managed to demonstrate the highest sensitivity across all three data types real, generic, and Markov. Among the de novo motif discovery tools, the Multiple Em for Motif Elicitation (MEME) emerged as the best performer. An analysis of the promoter regions of genes involved in the anthocyanin biosynthesis pathway in plants and the pentose phosphate pathway in humans, using the three best-performing tools, revealed considerable variation among the top 20 motifs identified by these tools. Conclusion The findings of this study lay a robust groundwork for selecting optimal TFBS prediction tools for future research. Given the variability observed in tool performance, employing multiple tools for identifying TFBSs in a set of sequences is highly recommended. In addition, further studies are recommended to develop an integrated toolbox that incorporates TFBS prediction or motif discovery tools, aiming to streamline result precision and accuracy.

Glucose-6-phosphate dehydrogenase (G6PD) is a key regulatory enzyme in the pentose phosphate pathway which produces nicotinamide adenine dinucleotide phosphate (NADPH) to maintain an adequate reducing environment in the cells and is especially important in red blood cells (RBC). Given its central role in the regulation of redox state, it is understandable that mutations in the gene encoding G6PD can cause deficiency of the protein activity leading to clinical manifestations such as neonatal jaundice and acute hemolytic anemia. Recently, an extensive review has been published about variants in the g6pd gene; recognizing 186 mutations. In this work, we review the state of the art in G6PD deficiency, describing 217 mutations in the g6pd gene; we also compile information about 31 new mutations, 16 that were not recognized and 15 more that have recently been reported. In order to get a better picture of the effects of new described mutations in g6pd gene, we locate the point mutations in the solved three-dimensional structure of the human G6PD protein. We found that class I mutations have the most deleterious effects on the structure and stability of the protein.

Genome engineering is crucial for answering fundamental questions about, and exploring practical applications of, microorganisms. Various microbial genome-engineering tools, including CRISPR/Cas-enhanced homologous recombination (HR), have been developed, with ever-improving simplicity, efficiency, and applicability. Recently, a powerful emerging technology based on CRISPR/Cas-nucleobase deaminase fusions, known as base editing, opened new avenues for microbial genome engineering. Base editing enables nucleotide transition without inducing lethal double-stranded (ds)DNA cleavage, adding foreign donor DNA, or depending on inefficient HR. Here, we review ongoing efforts to develop and apply base editing to engineer industrially and clinically relevant microorganisms. We also summarize bioinformatics tools that would greatly facilitate guide (g)RNA design and sequencing data analysis and discuss the future challenges and prospects associated with this technology. Base editing represents an emerging genome-editing technology that cleverly combines the programmability of CRISPR/Cas and the catalytic activity of nucleobase deaminase, enabling point mutations at multiple target loci without generating double-strand DNA breaks, requiring exogenous DNA donors, or relying on homology-directed repair.Simplicity, accuracy, and multiplex editing capability facilitate the quick adoption of base editing in the genetic manipulation of many industrially and clinically relevant microorganisms.User-friendly bioinformatics tools have been developed to design base editing-tailored guide RNAs and quantify editing efficiency from sequencing data.Developments in engineering DNA binding and deamination modules for expanded genome-targeting scope and adjustable editing window render base editing a more powerful microbial genome-engineering technology.

Background Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome. Results While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395). Conclusions NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.