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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.

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 The increasing availability of sequenced genomes has enabled comparative analyses of various organisms. Numerous tools and online platforms have been developed for this purpose, facilitating the identification of unique features within selected organisms. However, choosing the most appropriate tools can be unclear during the initial stages of analysis, often requiring multiple attempts to match the specific characteristics of the data. Here, we introduce CompàreGenome, a command-line tool specifically designed for genomic diversity estimation analyses. Suitable for both prokaryotes and eukaryotes, this tool is particularly valuable in the early stages of studies when little information is available about the genetic differences or similarities among compared organisms. Results In all the tests conducted, CompàreGenome successfully identified specific genetic features of the selected organisms, detected the most conserved genes, pinpointed highly divergent ones, and functionally annotated these genes. This provided insights into biological processes, molecular functions, and cellular components associated with each gene. The tool also distinguished organisms at the strain level and quantified genetic distances using three distinct analytical methods. Conclusion CompàreGenome empowers users to explore genomic differences among organisms, translating technical outputs from various tools into actionable insights for biologists. While primarily tested on small microbial genomes, the tool has potential applications for larger genomes. CompàreGenome is implemented in Bash, R, and Python and is freely available under an LGPL-2.1 license.

Background The rapid expansion of next-generation sequencing (NGS) technologies has generated vast amounts of genomic data, creating a growing demand for secure, scalable, and accessible tools to support variant interpretation. However, many existing solutions are command-line based, rely on cloud or server infrastructures that may pose data privacy risks, lack flexibility in supporting both VCF, CSV and TSV formats, or struggle to handle the scale and complexity of modern genomic datasets. There is a clear need for a user-friendly, locally operated application capable of efficiently processing annotated variant data for large-scale cohort level analysis. Results We introduce GenMasterTable, a free, secure, and cross-platform desktop application designed to simplify variant analysis through an intuitive graphical user interface (GUI). As the first tool to enable comprehensive cohort-level analysis from VCF, CSV to TSV files, GenMasterTable provides advanced functionality for concatenation, filtering, summarizing, and visualizing large-scale annotated datasets. Tailored for users without programming expertise, it enables rapid and accurate exploration of genetic variants, making it a practical solution for both research and clinical settings. Conclusion GenMasterTable addresses critical limitations in current variant analysis workflows by combining usability, data security, and scalability. Its support for multiple input formats and locally executed operations empowers clinicians, geneticists, and researchers to perform comprehensive variant analysis efficiently without the need for programming expertise.

Background The visual sequence logo has been a hot area in the development of bioinformatics tools. ggseqlogo written in R language has been the most popular API since it was published. With the popularity of artificial intelligence and deep learning, Python is currently the most popular programming language. The programming language used by bioinformaticians began to shift to Python. Providing APIs in Python that are similar to those in R can reduce the learning cost of relearning a programming language. And compared to ggplot2 in R, drawing framework is not as easy to use in Python. The appearance of plotnine (ggplot2 in Python version) makes it possible to unify the programming methods of bioinformatics visualization tools between R and Python. Results Here, we introduce plotnineSeqSuite, a new plotnine-based Python package provides a ggseqlogo-like API for programmatic drawing of sequence logos, sequence alignment diagrams and sequence histograms. To be more precise, it supports custom letters, color themes, and fonts. Moreover, the class for drawing layers is based on object-oriented design so that users can easily encapsulate and extend it. Conclusions plotnineSeqSuite is the first ggplot2-style package to implement visualization of sequence -related graphs in Python. It enhances the uniformity of programmatic plotting between R and Python. Compared with tools appeared already, the categories supported by plotnineSeqSuite are much more complete. The source code of plotnineSeqSuite can be obtained on GitHub ( https://github.com/caotianze/plotnineseqsuite ) and PyPI ( https://pypi.org/project/plotnineseqsuite ), and the documentation homepage is freely available on GitHub at ( https://caotianze.github.io/plotnineseqsuite/ ).

Background Recent advances in genomics indicate functional significance of a majority of genome sequences and their long range interactions. As a detailed examination of genome organization and function requires very high quality genome sequence, the objective of this study was to improve reference genome assembly of banana ( Musa acuminata ). Results We have developed a modular bioinformatics pipeline to improve genome sequence assemblies, which can handle various types of data. The pipeline comprises several semi-automated tools. However, unlike classical automated tools that are based on global parameters, the semi-automated tools proposed an expert mode for a user who can decide on suggested improvements through local compromises. The pipeline was used to improve the draft genome sequence of Musa acuminata. Genotyping by sequencing (GBS) of a segregating population and paired-end sequencing were used to detect and correct scaffold misassemblies. Long insert size paired-end reads identified scaffold junctions and fusions missed by automated assembly methods. GBS markers were used to anchor scaffolds to pseudo-molecules with a new bioinformatics approach that avoids the tedious step of marker ordering during genetic map construction. Furthermore, a genome map was constructed and used to assemble scaffolds into super scaffolds. Finally, a consensus gene annotation was projected on the new assembly from two pre-existing annotations. This approach reduced the total Musa scaffold number from 7513 to 1532 (i.e. by 80 %), with an N50 that increased from 1.3 Mb (65 scaffolds) to 3.0 Mb (26 scaffolds). 89.5 % of the assembly was anchored to the 11 Musa chromosomes compared to the previous 70 %. Unknown sites (N) were reduced from 17.3 to 10.0 %. Conclusion The release of the Musa acuminata reference genome version 2 provides a platform for detailed analysis of banana genome variation, function and evolution. Bioinformatics tools developed in this work can be used to improve genome sequence assemblies in other species.

Background The global Coronavirus Disease 2019 (COVID-19) pandemic highlighted the need to quickly diagnose infections to identify and prevent viral spread in the population. In response to the pandemic, BioFire Defense leveraged its PCR-based “lab-in-a-pouch” technology for expedited development of the BioFire® COVID-19 Test, a novel in vitro diagnostic detecting SARS-CoV-2 nucleic acid in human samples. Following clearance of an in vitro diagnostic device, regulatory bodies such as the U.S. Food and Drug Administration (FDA) require regular post market surveillance to monitor test performance against viral lineages circulating in the field, using predictive in silico inclusivity evaluations. Exponential increases in the number of sequences deposited in bioinformatic repositories such as GISAID, during the pandemic, impeded progress in meeting these post market requirements. In response, BioFire Defense developed a new bioinformatic tool to overcome scalability problems and the loss of accuracy encountered with the standard inclusivity method. Results The Coronavirus Monitoring for Inclusivity Tool (CoMIT) uses the Variant Sorter Algorithm to sidestep multiple sequence alignments, a significant barrier inherent in the standard inclusivity method. The implementation of CoMIT and its Variant Sorter Algorithm are described. Automated summary tables and visualizations from a typical inclusivity evaluation are presented. We report our approach to filter and display relevant information in the pipeline outputs using risk factors tied to test performance. Conclusions BioFire Defense has developed CoMIT, an automated bioinformatic pipeline for efficient processing and reporting of variant inclusivity from the GISAID EpiCoV™ repository. This tool ensures continuous and comprehensive post market evaluations of BioFire COVID-19 Test performance even from datasets large enough to impede standard inclusivity analyses. CoMIT’s low computational space complexity and modular code allow this tool to be generalized for inclusivity monitoring of multianalyte or single analyte tests with complex assay designs and/or highly variable targets. CoMIT’s databasing capabilities and metadata handling hold the potential for new investigations to improve readiness for future outbreaks.

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.

Background Multiplex Substrate Profiling by Mass Spectrometry (MSP-MS) is a powerful method for determining the substrate specificity of proteolytic enzymes, which is essential for developing protease inhibitors, diagnostics, and protease-activated therapeutics. However, the complex datasets generated by MSP-MS pose significant analytical challenges and have limited accessibility for non-specialist users. Results We developed mspms , a Bioconductor R package with an accompanying graphical interface, to streamline the analysis of MSP-MS data. Mspms standardizes workflows for data preparation, processing, statistical analysis, and visualization. The tool is designed for accessibility, serving advanced users through the R package and broader audiences through a web-based interface. We validated mspms using data from four well-characterized cathepsins (A–D), demonstrating that it reliably captures expected substrate specificities. Conclusions mspms is the first publicly available, comprehensive platform for MSP-MS data analysis downstream of peptide identification and quantification. It integrates preprocessing, normalization, statistical testing, and visualization into a single, transparent, and user-friendly framework, making it a valuable resource for the protease research community. The package is distributed via Bioconductor, and a graphical interface is available online for interactive use.