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With its constitutive and functional characteristics, the intestinal microbiota plays a crucial role in the health condition of the animals. Variations in the composition and gene expression of the intestinal microbiota are associated with the risk of the onset of various pathologies of the gastrointestinal tract and chronic inflammatory intestinal diseases. The objectives of this study were to evaluate the variability in the composition of the intestinal microbiota of goats of different breeds (Sarda, Maltese, and Alpine) farmed in different flocks of the region of Ogliastra (Sardegna, Italy) and to assess whether the type of feeding (natural pasture grazing-based versus intensive) could affect the intestinal bacterial composition. We also evaluated possible differences in the composition of the intestinal microbiota between healthy and Caprine arthritis encephalitis (CAE)-affected goats. The economic damage caused by this pathology is due to the reduction in milk production, with infected animals having greater susceptibility to contract diseases. The results of our study highlighted a statistically significant difference (P = 0.001–0.005) in the intestinal bacterial composition between the intensively managed flock and the other natural pasture-based flock.g In particular, a significantly greater abundance of Acidoaminococcaceae in the intensive flock was obgserved. Furthermore, a significantly greater abundance of Prevotellaceae was found in two localities in which, out of a total of 29 animals, only four tested negative for CAE. From these data, we deduced that the presence of Prevotellaceae can be an indication of the disease. This difference could be attributed to the farming system, the Cardedu farm being the only intensive one, and to the geographical distance of this location from the other sampling sites. Therefore, the results of the present study suggest that extensive or intensive farm management may affect the intestinal microbiota of goats. [ABSTRACT FROM AUTHOR]

The influx of whole genome sequencing (WGS) data in the public health and clinical diagnostic sectors has created a need for data analysis methods and bioinformatics expertise, which can be a bottleneck for many laboratories. At Sciensano, the Belgian national public health institute, an intuitive and user-friendly bioinformatics tool portal was implemented using Galaxy, an open-source platform for data analysis and workflow creation. The Galaxy @Sciensano instance is available to both internal and external scientists and offers a wide range of tools provided by the community, complemented by over 50 custom tools and pipelines developed in-house. The tool selection is currently focused primarily on the analysis of WGS data generated using Illumina sequencing for microbial pathogen typing, characterization and outbreak detection, but it also addresses specific use cases for other data types. Our Galaxy instance includes several custom-developed 'push-button' pipelines, which are user-friendly and intuitive stand-alone tools that perform complete characterization of bacterial isolates based on WGS data and generate interactive HTML output reports with key findings. These pipelines include quality control, de novo assembly, sequence typing, antimicrobial resistance prediction and several relevant species-specific assays. They are tailored for pathogens with active genomic surveillance programs, and clinical relevance, such as Escherichia coli, Listeria monocytogenes, Salmonella spp. and Mycobacterium tuberculosis. These tools and pipelines utilize internationally recognized databases such as PubMLST, EnteroBase, and the NCBI National Database of Antibiotic Resistant Organisms, which are automatically synchronized on a regular basis to ensure up-to-date results. Many of these pipelines are part of the routine activities of Belgian national reference centers and laboratories, some of which use them under ISO accreditation. This resource is publicly available for noncommercial use at https://galaxy.sciensano.be/ and can help other laboratories establish reliable, traceable and reproducible bioinformatics analyses for pathogens encountered in public health settings. [ABSTRACT FROM AUTHOR]

Historical measurements, such as the measurement of the size of our Galaxy and distance to its center, are based on relatively simple assumptions that allow high-school students and undergraduate college students to explore and replicate the basic analysis of astronomical data. Here, I present a practical data analysis exercise that was developed to select Slovenian participants to the International Olympiad on Astronomy and Astrophysics (IOAA). The exercise guides students to discover the globular cluster distribution within our Galaxy, use basic knowledge of spherical astronomy and coordinate transformation, explore the night sky and develop basic skills for the analysis of astronomical data. [ABSTRACT FROM AUTHOR]

Since recent years, mass segregation driven by two-body relaxation in star clusters has been proposed to be measured by the so-called dynamical clock, A + , a measure of the area enclosed between the cumulative radial distribution of blue straggler stars and that of a reference population. Since star clusters spend their lifetime immersed in the gravitational potential of their host galaxy, they are also subject to the effects of galactic tides. In this work, I show that the A + index of a star cluster depends on both its internal dynamics in isolation and the effects of galactic tides. Mainly, I focused on the largest sample of open clusters harboring blue straggler stars with robust cluster membership. I found that these open clusters exhibit an overall dispersion of the A + index in diagnostic diagrams, whereas Milky Way globular clusters show a clear linear trend. However, as also experienced by globular clusters, A + values of open clusters show some dependence on their galactocentric distances, in the sense that clusters located closer or farther than ∼ 11 kpc from the Galactic center have larger and smaller A + values, respectively. This different response to two-body relaxation and galactic tides in globular and open clusters, which happen concurrently, can be due to their different masses. More massive clusters can protect their innermost regions from galactic tides more effectively. [ABSTRACT FROM AUTHOR]

Abstract Background The Joint Programming Initiative on Antimicrobial Resistance (JPIAMR) networks ‘Seq4AMR’ and ‘B2B2B AMR Dx’ were established to promote collaboration between microbial whole genome sequencing (WGS) and antimicrobial resistance (AMR) stakeholders. A key topic discussed was the frequent variability in results obtained between different microbial WGS-related AMR gene prediction workflows. Further, comparative benchmarking studies are difficult to perform due to differences in AMR gene prediction accuracy and a lack of agreement in the naming of AMR genes (semantic conformity) for the results obtained. To illustrate this problem, and as a capacity-building exercise to encourage stakeholder involvement, a comparative Galaxy-based BenchAMRking platform was developed and validated using datasets from bacterial species with PCR-verified AMR gene presence or absence information from abritAMR. Results The Galaxy-based BenchAMRking platform ( https://erasmusmc-bioinformatics.github.io/benchAMRking/ ) specifically focusses on the steps involved in identifying AMR genes from raw reads and sequence assemblies. The platform currently comprises four well-characterised and published workflows that have previously been used to identify AMR genes using WGS data from several different bacterial species. These four workflows, which include the ISO certified abritAMR workflow, make use of different computational tools (or tool versions), and interrogate different AMR gene sequence databases. By utilising their own data, users can investigate potential AMR gene-calling problems associated with their own in silico workflows/protocols, with a potential use case outlined in this publication. Conclusions BenchAMRking is a Galaxy-based comparison platform where users can access, visualise, and explore some of the major discrepancies associated with AMR gene prediction from microbial WGS data.

In the future, human beings will surely expand into space. But given its unique risks, will humanity thrive in space environments? For example, when humans begin living and reproducing in space habitats or on other planets in the solar system, are there risks that future generations may suffer from adverse mutations induced by space radiation, or that embryos and fetuses will develop abnormally in gravitational environments that differ from that of Earth? Moreover, human expansion to other stellar systems requires that for each breed of animal, thousands of individuals must be transported to destination planets to prevent populations from experiencing inbreeding-related degeneration. In even more distant future, when humans have spread throughout the galaxy, all genetic resources on Earth, the planet where humans originated, must be permanently and safely stored— but is this even possible? Such issues with future space colonization may not be an urgent research priority, but research and technological development accompanying advancements in spaceflight will excite many people and contribute to technological improvements that can improve living standards in the present day (e.g., more effective treatments for infertility, etc.). This review will therefore focus primarily on issues related to mammalian reproduction in space environments.

Context: Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the central system in epigenomic exploration. Chromatin immunoprecipitation coupled with sequencing (ChIP-seq) is an important technology to identify the genome-wide location of DNA-binding proteins such as histones proteins, transcription factors, RNA polymerase, or any protein of interest. ChIP-seq has been used to study the binding sites and efficacy of drugs in cancer cell lines etc. Aims: In current research, breast cancer cell line data have been used to study the effect PADI2 (peptidyl arginine deiminase) gene in the progression of breast cancer. Further, this ChIP-seq data have also been used to study the binding site of Amanitin drug in breast cancer. Settings and Design: Breast cancer ChIP-seq data have been retrieved from the European Nucleotide Archive database with project Id PRJNA415426 short read archive. Four samples of FASTQ files were used and analyzed for the genome-wide analysis. Materials and Methods: Galaxy server (https://usegalaxy.org/) was used for complete ChIP-seq data analysis; different tools such as fast-quality control (QC), multi-QC, Bowtie2, model-based analysis of ChIP-sequencing, and ChIPseeker tools were used for motif enrichment and functional analysis. Motif analysis was done through the Multiple Expectation maximizations for Motif Elicitation database (https://meme-suite.org/meme/db/motifs). Results: Computational investigation demonstrates the binding sequences of the T47-D breast cancer cell line as TTTTGTATTTTTAGT, and this motif occurs 2123 times in the Homo Sapiens reference genome that is hg19. Conclusions: This research classifies the binding site and affinity of the T47-D human breast cancer cell line. Further, wet laboratory studies are required to verify the function of the predicted motifs and their importance in drug development or research in breast cancer.

Context: Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the central system in epigenomic exploration. Chromatin immunoprecipitation coupled with sequencing (ChIP-seq) is an important technology to identify the genome-wide location of DNA-binding proteins such as histones proteins, transcription factors, RNA polymerase, or any protein of interest. ChIP-seq has been used to study the binding sites and efficacy of drugs in cancer cell lines etc. Aims: In current research, breast cancer cell line data have been used to study the effect PADI2 (peptidyl arginine deiminase) gene in the progression of breast cancer. Further, this ChIP-seq data have also been used to study the binding site of Amanitin drug in breast cancer. Settings and Design: Breast cancer ChIP-seq data have been retrieved from the European Nucleotide Archive database with project Id PRJNA415426 short read archive. Four samples of FASTQ files were used and analyzed for the genome-wide analysis. Materials and Methods: Galaxy server (https://usegalaxy.org/) was used for complete ChIP-seq data analysis; different tools such as fast-quality control (QC), multi-QC, Bowtie2, model-based analysis of ChIP-sequencing, and ChIPseeker tools were used for motif enrichment and functional analysis. Motif analysis was done through the Multiple Expectation maximizations for Motif Elicitation database (https://meme-suite.org/meme/db/motifs). Results: Computational investigation demonstrates the binding sequences of the T47-D breast cancer cell line as TTTTGTATTTTTAGT, and this motif occurs 2123 times in the Homo Sapiens reference genome that is hg19. Conclusions: This research classifies the binding site and affinity of the T47-D human breast cancer cell line. Further, wet laboratory studies are required to verify the function of the predicted motifs and their importance in drug development or research in breast cancer. [ABSTRACT FROM AUTHOR]

The concept of steady-state solutions to the Navier–Stokes equation is defined. Such solutions extend the notion of stationary ones, diminish exponentially over time, and have a fixed spatial field of velocities and constant pressure in the absence of external fields. A way of constructing these solutions is considered, and the problem of Taylor vortices is solved. A mathematical model of a tornado is proposed, within which a steady-state solution is obtained as an eigenfunction of the problem in the form of a vortex. A model for the formation of the structure of a gas cloud is proposed, based on the Navier–Stokes equation. It is shown that spiral arms arise from flows of gas moving outward, due to the Coriolis force. It is proved that the number of arms is even and their structure is independent of the angular velocity of rotation. We obtain a formula for the twist angle of the spirals, depending on the cloud parameters for when . [ABSTRACT FROM AUTHOR]

Very recently, a straightforward method was proposed to understand galaxies and galactic clusters without using the very elusive dark matter concept. This method is called the κ -model. The main idea is to maintain the form of the usual physical laws, especially Newton's laws of motion when gravity is weak, but only by applying a local scaling procedure for the related lengths, distances, and velocities. This local scaling appears as a correspondence principle in the κ -model. In this model, the fundamental physical constants remain universal, i.e., they are independent of a point in space and of time. The κ -model is Newtonian in its essence, but there is a relativistic extension that can easily be built. The aim of the present paper is to detail the mathematical formalism supporting it. [ABSTRACT FROM AUTHOR]

Galaxies are observed to host magnetic fields with a typical total strength of around 15 μ G. A coherent large-scale field constitutes up to a few microgauss of the total, while the rest is built from strong magnetic fluctuations over a wide range of spatial scales. This represents sufficient magnetic energy for it to be dynamically significant. Several questions immediately arise: What is the physical mechanism that gives rise to such magnetic fields? How do these magnetic fields affect the formation and evolution of galaxies? In which physical processes do magnetic fields play a role, and how can that role be characterized? Numerical modelling of magnetized flows in galaxies is playing an ever-increasing role in finding those answers. We review major techniques used for these models. Current results strongly support the conclusion that field growth occurs during the formation of the first galaxies on timescales shorter than their accretion timescales due to small-scale turbulent dynamos. The saturated small-scale dynamo maintains field strengths at only a few percent of equipartition with turbulence. This is in contradiction with the observed magnitude of turbulent fields, but may be reconciled by the further contribution to the turbulent field of the large-scale dynamo. The subsequent action of large-scale dynamos in differentially rotating discs produces field strengths observed in low redshift galaxies, where it reaches equipartition with the turbulence and has substantial power at large scales. The field structure resulting appears consistent with observations including Faraday rotation and polarisation from synchrotron and dust thermal emission. Major remaining challenges include scaling numerical models toward realistic scale separations and Prandtl and Reynolds numbers. [ABSTRACT FROM AUTHOR]

Aim is to create a system that can precisely and efficiently classify stars and galaxies, which can at that point be utilized for different applications in astronomy and astrophysics. The objective of this project is to create a progressed framework for categorizing stars, galaxies, and quasars, while also evaluating various classification models to identify the most effective one. My hypothesis centered on the possibility of preparing a machine learning model to precisely classify stars, quasars and galaxies utilizing the dataset given by the Sloan Digital Sky Survey (SDSS) [25]. We aimed to accomplish ideal execution with balanced data sets. We have utilized machine learning calculation like Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT). The proposed machine learning show can help astronomers in precisely classifying celestial bodies in the sky, possibly contributing to headways in our understanding of the universe. The proposed machine learning show can help astronomers in precisely classifying celestial bodies in the sky, possibly contributing to headways in our understanding of the universe. This research social suggestions have a huge effect on the logical community, not fair in space science but also in other zones that require to classify things accurately. [ABSTRACT FROM AUTHOR]

FungiDB (https://fungidb.org) serves as a valuable online resource that seamlessly integrates genomic and related large-scale data for a wide range of fungal and oomycete species. As an integral part of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org), FungiDB continually integrates both published and unpublished data addressing various aspects of fungal biology. Established in early 2011, the database has evolved to support 674 datasets. The datasets include over 300 genomes spanning various taxa (e.g. Ascomycota, Basidiomycota, Blastocladiomycota, Chytridiomycota, Mucoromycota, as well as Albuginales, Peronosporales, Pythiales, and Saprolegniales). In addition to genomic assemblies and annotation, over 300 extra datasets encompassing diverse information, such as expression and variation data, are also available. The resource also provides an intuitive web-based interface, facilitating comprehensive approaches to data mining and visualization. Users can test their hypotheses and navigate through omics-scale datasets using a built-in search strategy system. Moreover, FungiDB offers capabilities for private data analysis via the integrated VEuPathDB Galaxy platform. FungiDB also permits genome improvements by capturing expert knowledge through the User Comments system and the Apollo genome annotation editor for structural and functional gene curation. FungiDB facilitates data exploration and analysis and contributes to advancing research efforts by capturing expert knowledge for fungal and oomycete species. [ABSTRACT FROM AUTHOR]

This white paper explores the detectability of intermediate-mass black holes (IMBHs) wandering in the Milky Way (MW) and massive local galaxies, with a particular emphasis on the role of AXIS. IMBHs, ranging within 10 3 − 6 M ⊙ , are commonly found at the centers of dwarf galaxies and may exist, yet undiscovered, in the MW. By using model spectra for advection-dominated accretion flows (ADAFs), we calculated the expected fluxes emitted by a population of wandering IMBHs with masses of 10 5 M ⊙ in various MW environments and extrapolated our results to massive local galaxies. Around 40 % of the potential population of wandering IMBHs in the MW can be detected in an AXIS deep field. We proposed criteria to aid with selecting IMBH candidates using already available optical surveys. We also showed that IMBHs wandering in >200 galaxies within 10 Mpc can be easily detected with AXIS when passing within dense galactic environments (e.g., molecular clouds and cold neutral medium). In summary, we highlighted the potential X-ray detectability of wandering IMBHs in local galaxies and provided insights for guiding future surveys. Detecting wandering IMBHs is crucial for understanding their demographics and evolution and the merging history of galaxies. This white paper is part of a series commissioned for the AXIS Probe Concept Mission; additional AXIS white papers can be found at the AXIS website. [ABSTRACT FROM AUTHOR]

We study the metallicity distribution and evolution in the galactic disk based on the largest sample of open star clusters in the galaxy. From the catalog of 1,879 open clusters in the range of galactocentric distance (RGC) from 4 to 20 kpc, we investigate the variation in metallicity in the galactic disk as functions of RGC, vertical distance (Z), and ages of the clusters. In the direction perpendicular to the galactic plane, the variation in metallicity is found to follow a stepped linear relation. We estimate a vertical metallicity gradient?????H" of -0.545 ± 0.046 dex kpc-1 for |Z| < 0.487 kpc and -0.075 ± 0.093 dex kpc-1 for 0.487 < |Z| < 1.8 kpc. On average, metallicity variations above and below the galactic plane are found to change at similar rates. The change in metallicity in the radial direction is also found to follow a two-function linear relation. We obtain a radial metallicity gradient ^^ of -0.070 ± 0.002 dex kpc-1 for 4.0 < RGC < 12.8 dRGC kpc and -0.005 ± 0.018 dex kpc-1 for 12.8 < RGC < 20.5 kpc, which clearly shows a strong variation in the metallicity gradient when moving from the inner to the outer galactic disk. The age-metallicity relation (AMR) is found to follow a steeper negative slope of -0.031 ± 0.006 dex Gyr-1 for clusters older than 240 Myr; however, there is some hint of positive metallicity age gradient for younger clusters. [ABSTRACT FROM AUTHOR]

Our main goal here is to conduct a comparative analysis between the well-known MOND theory and a more recent model called the κ -model. An additional connection, between the κ -model and two other novel MOND-type theories, Newtonian Fractional-Dimension Gravity (NFDG) and Refracted Gravity (RG), is likewise presented. All these models are built to overtake the DM paradigm, or at least to strongly reduce the dark matter content. Whereas they rely on different formalisms, however, all four seem to suggest that the universal parameter, a 0 , appearing in MOND theory could intrinsically be correlated to either the sole baryonic mean mass density (RG and κ -model) and/or to the dimension of the object under consideration (NFDG and κ -model). We then confer to parameter a 0 a more flexible status of multiscale parameter, as required to explain the dynamics together in galaxies and in galaxy clusters. Eventually, the conformal gravity theory (CFT) also seems to have some remote link with the κ -model, even though the first one is an extension of general relativity, and the second one is Newtonian in essence. The κ -model has been tested on a small sample of spiral galaxies and in galaxy clusters. Now, we test this model on a large sample of galaxies issued from the SPARC database. [ABSTRACT FROM AUTHOR]

Abstract Background Not changing the native constitution of genes prior to their expression by a heterologous host can affect the amount of proteins synthesized as well as their folding, hampering their activity and even cell viability. Over the past decades, several strategies have been developed to optimize the translation of heterologous genes by accommodating the difference in codon usage between species. While there have been a handful of studies assessing various codon optimization strategies, to the best of our knowledge, no research has been performed towards the evaluation and comparison of codon harmonization algorithms. To highlight their importance and encourage meaningful discussion, we compared different open-source codon harmonization tools pertaining to their in silico performance, and we investigated the influence of different gene-specific factors. Results In total, 27 genes were harmonized with four tools toward two different heterologous hosts. The difference in %MinMax values between the harmonized and the original sequences was calculated (ΔMinMax), and statistical analysis of the obtained results was carried out. It became clear that not all tools perform similarly, and the choice of tool should depend on the intended application. Almost all biological factors under investigation (GC content, RNA secondary structures and choice of heterologous host) had a significant influence on the harmonization results and thus must be taken into account. These findings were substantiated using a validation dataset consisting of 8 strategically chosen genes. Conclusions Due to the size of the dataset, no complex models could be developed. However, this initial study showcases significant differences between the results of various codon harmonization tools. Although more elaborate investigation is needed, it is clear that biological factors such as GC content, RNA secondary structures and heterologous hosts must be taken into account when selecting the codon harmonization tool.

Abstract Background Galaxy is a web-based open-source platform for scientific analyses. Researchers use thousands of high-quality tools and workflows for their respective analyses in Galaxy. Tool recommender system predicts a collection of tools that can be used to extend an analysis. In this work, a tool recommender system is developed by training a transformer on workflows available on Galaxy Europe and its performance is compared to other neural networks such as recurrent, convolutional and dense neural networks. Results The transformer neural network achieves two times faster convergence, has significantly lower model usage (model reconstruction and prediction) time and shows a better generalisation that goes beyond training workflows than the older tool recommender system created using RNN in Galaxy. In addition, the transformer also outperforms CNN and DNN on several key indicators. It achieves a faster convergence time, lower model usage time, and higher quality tool recommendations than CNN. Compared to DNN, it converges faster to a higher precision@k metric (approximately 0.98 by transformer compared to approximately 0.9 by DNN) and shows higher quality tool recommendations. Conclusion Our work shows a novel usage of transformers to recommend tools for extending scientific workflows. A more robust tool recommendation model, created using a transformer, having significantly lower usage time than RNN and CNN, higher precision@k than DNN, and higher quality tool recommendations than all three neural networks, will benefit researchers in creating scientifically significant workflows and exploratory data analysis in Galaxy. Additionally, the ability to train faster than all three neural networks imparts more scalability for training on larger datasets consisting of millions of tool sequences. Open-source scripts to create the recommendation model are available under MIT licence at https://github.com/anuprulez/galaxy_tool_recommendation_transformers

Abstract Background Cells orchestrate histone biogenesis with strict temporal and quantitative control. To efficiently regulate histone biogenesis, the repetitive Drosophila melanogaster replication-dependent histone genes are arrayed and clustered at a single locus. Regulatory factors concentrate in a nuclear body known as the histone locus body (HLB), which forms around the locus. Historically, HLB factors are largely discovered by chance, and few are known to interact directly with DNA. It is therefore unclear how the histone genes are specifically targeted for unique and coordinated regulation. Results To expand the list of known HLB factors, we performed a candidate-based screen by mapping 30 publicly available ChIP datasets of 27 unique factors to the Drosophila histone gene array. We identified novel transcription factor candidates, including the Drosophila Hox proteins Ultrabithorax (Ubx), Abdominal-A (Abd-A), and Abdominal-B (Abd-B), suggesting a new pathway for these factors in influencing body plan morphogenesis. Additionally, we identified six other factors that target the histone gene array: JIL-1, hormone-like receptor 78 (Hr78), the long isoform of female sterile homeotic (1) (fs(1)h) as well as the general transcription factors TBP associated factor 1 (TAF-1), Transcription Factor IIB (TFIIB), and Transcription Factor IIF (TFIIF). Conclusions Our foundational screen provides several candidates for future studies into factors that may influence histone biogenesis. Further, our study emphasizes the powerful reservoir of publicly available datasets, which can be mined as a primary screening technique.

The Scalar Field Dark Matter model has been known in various ways throughout its history; Fuzzy, BEC, Wave, Ultralight, Axion-like Dark Matter, etc. All of them consist in proposing that dark matter of the universe is a spinless field Φthat follows the Klein-Gordon (KG) equation of motion □Φ-dV/dΦ=0, for a given scalar field potential V. The difference between different models is sometimes the choice of the scalar field potential V. In the literature we find that people usually work in the non-relativistic, weak-field limit of the Klein- Gordon equation, where it transforms into the Schrödinger equation and the Einstein equations into the Poisson equation, reducing the KG-Einstein system, to the Schrödinger-Poisson system. In this paper, we review some of the most interesting achievements of this model from the historical point of view and its comparison with observations, showing that this model could be the last answer to the question about the nature of dark matter in the universe. [ABSTRACT FROM AUTHOR]

Viral helicases are promising targets for the development of antiviral therapies. Given their vital function of unwinding double-stranded nucleic acids, inhibiting them blocks the viral replication cycle. Previous studies have elucidated key structural details of these helicases, including the location of substrate binding sites, flexible domains, and the discovery of potential inhibitors. Here we present a series of new Galaxy tools and workflows for performing and analyzing molecular dynamics simulations of viral helicases. We first validate them by demonstrating recapitulation of data from previous simulations of Zika (NS3) and SARS-CoV-2 (NSP13) helicases in apo and complex with inhibitors. We further demonstrate the utility and generalizability of these Galaxy workflows by applying them to new cases, proving their usefulness as a widely accessible method for exploring antiviral activity. [ABSTRACT FROM AUTHOR]

Background In clinical research, data have to be accessible and reproducible, but the generated data are becoming larger and analysis complex. Here we propose a platform for Findable, Accessible, Interoperable, and Reusable (FAIR) data access and creating reproducible findings. Standardized access to a major genomic repository, the European Genome-Phenome Archive (EGA), has been achieved with API services like PyEGA3. We aim to provide a FAIR data analysis service in Galaxy by retrieving genomic data from the EGA and provide a generalized "omics" platform for FAIR data analysis. Results To demonstrate this, we implemented an end-to-end Galaxy workflow to replicate the findings from an RD-Connect synthetic dataset Beyond the 1 Million Genomes (synB1MG) available from the EGA. We developed the PyEGA3 connector within Galaxy to easily download multiple datasets from the EGA. We added the gene.iobio tool, a diagnostic environment for precision genomics, to Galaxy and demonstrate that it provides a more dynamic and interpretable view for trio analysis results. We developed a Galaxy trio analysis workflow to determine the pathogenic variants from the synB1MG trios using the GEMINI and gene.iobio tool. The complete workflow is available at WorkflowHub, and an associated tutorial was created in the Galaxy Training Network, which helps researchers unfamiliar with Galaxy to run the workflow. Conclusions We showed the feasibility of reusing data from the EGA in Galaxy via PyEGA3 and validated the workflow by rediscovering spiked-in variants in synthetic data. Finally, we improved existing tools in Galaxy and created a workflow for trio analysis to demonstrate the value of FAIR genomics analysis in Galaxy. [ABSTRACT FROM AUTHOR]

Galaxies are one of the most interesting and complex astronomical objects statistically due to their continuous diversification caused mainly due to incidents such as accretion, action, or mergers. Multivariate studies are one of the most useful tools to analyze these type of data and to understand various components of them. We study a sample of the local universe of Orlando 509 galaxies, imputed with a Predictive Mean Matching (PMM) multiple imputation algorithm, with the aim of classifying the galaxies into distinct clusters through k-medoids and k-mean algorithms and, in turn, performing a heuristic evaluation of the two partitioning algorithms through the percentage of misclassification observed. From the clustering algorithms, it was observed that there were four distinct clusters of the galaxies with misclassification of about 1.96 % . Also, comparing the percentage of misclassification heuristically k-means is a superior algorithm to k-medoids under fixed optimal sizes when the said category of galaxy datasets is concerned. By considering that galaxies are continuously evolving complex objects and using appropriate statistical tools, we are able to derive an explanatory classification of galaxies, based on the physical diverse properties of galaxies, and also establish a better method of partitioning when working on the galaxies. [ABSTRACT FROM AUTHOR]

Abstract Background ‘Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as “meta-omics”, they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities. Results In this study, we applied the workflows on a highly efficient cellulose-degrading minimal consortium enriched from a biogas reactor to analyse the key roles of uncultured microorganisms in complex biomass degradation processes. Metagenomic analysis recovered metagenome-assembled genomes (MAGs) for several constituent populations including Hungateiclostridium thermocellum , Thermoclostridium stercorarium and multiple heterogenic strains affiliated to Coprothermobacter proteolyticus. The metagenomics workflow was developed as two modules, one standard, and one optimized for improving the MAG quality in complex samples by implementing a combination of single- and co-assembly, and dereplication after binning. The exploration of the active pathways within the recovered MAGs can be visualized in ViMO, which also provides an overview of the MAG taxonomy and quality (contamination and completeness), and information about carbohydrate-active enzymes (CAZymes), as well as KEGG annotations and pathways, with counts and abundances at both mRNA and protein level. To achieve this, the metatranscriptomic reads and metaproteomic mass-spectrometry spectra are mapped onto predicted genes from the metagenome to analyse the functional potential of MAGs, as well as the actual expressed proteins and functions of the microbiome, all visualized in ViMO. Conclusion Our three workflows for integrative meta-omics in combination with ViMO presents a progression in the analysis of ‘omics data, particularly within Galaxy, but also beyond. The optimized metagenomics workflow allows for detailed reconstruction of microbial community consisting of MAGs with high quality, and thus improves analyses of the metabolism of the microbiome, using the metatranscriptomics and metaproteomics workflows.

Modern investigations in biology often require the efforts of one or more groups of researchers. Often these are groups of specialists from various scientific fields who generate and share data of different formats and sizes. Without modern approaches to work automation and data versioning (where data from different collaborators are stored at different points in time), teamwork quickly devolves into unmanageable confusion. In this review, we present a number of information systems designed to solve these problems. Their application to the organization of scientific activity helps to manage the flow of actions and data, allowing all participants to work with relevant information and solving the issue of reproducibility of both experimental and computational results. The article describes methods for organizing data flows within a team, principles for organizing metadata and ontologies. The information systems Trello, Git, Redmine, SEEK, OpenBIS and Galaxy are considered. Their functionality and scope of use are described. Before using any tools, it is important to understand the purpose of implementation, to define the set of tasks they should solve, and, based on this, to formulate requirements and finally to monitor the application of recommendations in the field. The tasks of creating a framework of ontologies, metadata, data warehousing schemas and software systems are key for a team that has decided to undertake work to automate data circulation. It is not always possible to implement such systems in their entirety, but one should still strive to do so through a step­by­step introduction of principles for organizing data and tasks with the mastery of individual software tools. It is worth noting that Trello, Git, and Redmine are easier to use, customize, and support for small research groups. At the same time, SEEK, OpenBIS, and Galaxy are more specific and their use is advisable if the capabilities of simple systems are no longer sufficient.

Background: Galaxy is a web-based open-source platform for scientific analyses. Researchers use thousands of high-quality tools and workflows for their respective analyses in Galaxy. Tool recommender system predicts a collection of tools that can be used to extend an analysis. In this work, a tool recommender system is developed by training a transformer on workflows available on Galaxy Europe and its performance is compared to other neural networks such as recurrent, convolutional and dense neural networks. Results: The transformer neural network achieves two times faster convergence, has significantly lower model usage (model reconstruction and prediction) time and shows a better generalisation that goes beyond training workflows than the older tool recommender system created using RNN in Galaxy. In addition, the transformer also outperforms CNN and DNN on several key indicators. It achieves a faster convergence time, lower model usage time, and higher quality tool recommendations than CNN. Compared to DNN, it converges faster to a higher precision@k metric (approximately 0.98 by transformer compared to approximately 0.9 by DNN) and shows higher quality tool recommendations. Conclusion: Our work shows a novel usage of transformers to recommend tools for extending scientific workflows. A more robust tool recommendation model, created using a transformer, having significantly lower usage time than RNN and CNN, higher precision@k than DNN, and higher quality tool recommendations than all three neural networks, will benefit researchers in creating scientifically significant workflows and exploratory data analysis in Galaxy. Additionally, the ability to train faster than all three neural networks imparts more scalability for training on larger datasets consisting of millions of tool sequences. Open-source scripts to create the recommendation model are available under MIT licence at https://github.com/anuprulez/galaxy_tool_recommendation_transformers [ABSTRACT FROM AUTHOR]

Genetic modifiers are variants modulating phenotypic outcomes of a primary detrimental variant. They contribute to rare diseases phenotypic variability, but their identification is challenging. Genetic screening with model organisms is a widely used method for demystifying genetic modifiers. Forward genetics screening followed by whole genome sequencing allows the detection of variants throughout the genome but typically produces thousands of candidate variants making the interpretation and prioritization process very time-consuming and tedious. Despite whole genome sequencing is more time and cost-efficient, usage of computational pipelines specific to modifier identification remains a challenge for biological-experiment-focused laboratories doing research with model organisms. To facilitate a broader implementation of whole genome sequencing in genetic screens, we have developed Model Organism Modifier or MOM, a pipeline as a user-friendly Galaxy workflow. Model Organism Modifier analyses raw short-read whole genome sequencing data and implements tailored filtering to provide a Candidate Variant List short enough to be further manually curated. We provide a detailed tutorial to run the Galaxy workflow Model Organism Modifier and guidelines to manually curate the Candidate Variant Lists. We have tested Model Organism Modifier on published and validated Caenorhabditis elegans modifiers screening datasets. As whole genome sequencing facilitates high-throughput identification of genetic modifiers in model organisms, Model Organism Modifier provides a user-friendly solution to implement the bioinformatics analysis of the short-read datasets in laboratories without expertise or support in Bioinformatics. [ABSTRACT FROM AUTHOR]

The fertility of female cows is the main factor for the survival of dairy farming. This study was performed to determine the differentially expressed genes in fertility in Holstein cows using phenotypic data and next-generation sequencing (NGS) technology. Corpus luteum samples from 18 Holstein cows at up to two parturitions were used in two high and low fertility groups, three samples from each group were pooled together and a total of three biological samples from each were submitted for whole genome sequencing. The preparation and analysis steps were performed on the Galaxy 22.01 platform. DAVID (2021) database were used for gene ontology. The results of differential gene expression analysis in luteal tissue showed that 19 genes out of 13049 expressed transcripts had a significant difference in expression between high and low-fertility cows with an FDR-adjusted p-value (q-value) of less than 0.05. UBE3B, NIF3L1 and ORC2 genes were three functional marker genes that were more highly expressed in the corpus luteum of high-fertility cows. The UBE3B gene is involved in biological processes as a protein involved in the catabolic process of ubiquitin-dependent protein. NIF3L1 is involved in the biological process of neuronal differentiation, positive transcriptional regulation, DNA pattern, and negative transcriptional regulation by nucleic acid pattern. The ORC2 gene plays a role in the biological process of DNA replication. The genes KRT8, PHLDB3, PPT2, LOC787628, PPYR1, TOX, TP73, DHX8, KCNN1, CLEC6A, PXMP4, LRRC26, SLC34A3, and OR13C7 have been shown to be more highly expressed in the corpus luteum in low fertility cows. Among these genes, three genes PPT2, PPYR1 and CLEC6A were functional indicators. The loci UBE3B, NIF3L1 and ORC2, which were expressed more strongly in the corpus luteum of highly fertile cows, can be used as functional indicator genes in the genetic selection of dairy cows. [ABSTRACT FROM AUTHOR]

Background: Cells orchestrate histone biogenesis with strict temporal and quantitative control. To efficiently regulate histone biogenesis, the repetitive Drosophila melanogaster replication-dependent histone genes are arrayed and clustered at a single locus. Regulatory factors concentrate in a nuclear body known as the histone locus body (HLB), which forms around the locus. Historically, HLB factors are largely discovered by chance, and few are known to interact directly with DNA. It is therefore unclear how the histone genes are specifically targeted for unique and coordinated regulation. Results: To expand the list of known HLB factors, we performed a candidate-based screen by mapping 30 publicly available ChIP datasets of 27 unique factors to the Drosophila histone gene array. We identified novel transcription factor candidates, including the Drosophila Hox proteins Ultrabithorax (Ubx), Abdominal-A (Abd-A), and Abdominal-B (Abd-B), suggesting a new pathway for these factors in influencing body plan morphogenesis. Additionally, we identified six other factors that target the histone gene array: JIL-1, hormone-like receptor 78 (Hr78), the long isoform of female sterile homeotic (1) (fs(1)h) as well as the general transcription factors TBP associated factor 1 (TAF-1), Transcription Factor IIB (TFIIB), and Transcription Factor IIF (TFIIF). Conclusions: Our foundational screen provides several candidates for future studies into factors that may influence histone biogenesis. Further, our study emphasizes the powerful reservoir of publicly available datasets, which can be mined as a primary screening technique. [ABSTRACT FROM AUTHOR]

Aims. The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ. Traditional selection methods that have been used to identify accreted stars typically rely on full 3D velocity, metallicity information, or both, which significantly reduces the number of classifiable stars. The approach advocated here is applicable to a much larger portion of Gaia DR2. Methods. A method known as "transfer learning" is shown to be effective through extensive testing on a set of mock Gaia catalogs that are based on the FIRE cosmological zoom-in hydrodynamic simulations of Milky Way-mass galaxies. The machine is first trained on simulated data using only 5D kinematics as inputs and is then further trained on a cross-matched Gaia/RAVE data set, which improves sensitivity to properties of the real Milky Way. Results. The result is a catalog that identifies ∼767 000 accreted stars within Gaia DR2. This catalog can yield empirical insights into the merger history of the Milky Way and could be used to infer properties of the dark matter distribution.

Background: 'Omics methods have empowered scientists to tackle the complexity of microbial communities on a scale not attainable before. Individually, omics analyses can provide great insight; while combined as "meta-omics", they enhance the understanding of which organisms occupy specific metabolic niches, how they interact, and how they utilize environmental nutrients. Here we present three integrative meta-omics workflows, developed in Galaxy, for enhanced analysis and integration of metagenomics, metatranscriptomics, and metaproteomics, combined with our newly developed web-application, ViMO (Visualizer for Meta-Omics) to analyse metabolisms in complex microbial communities. Results: In this study, we applied the workflows on a highly efficient cellulose-degrading minimal consortium enriched from a biogas reactor to analyse the key roles of uncultured microorganisms in complex biomass degradation processes. Metagenomic analysis recovered metagenome-assembled genomes (MAGs) for several constituent populations including Hungateiclostridium thermocellum, Thermoclostridium stercorarium and multiple heterogenic strains affiliated to Coprothermobacter proteolyticus. The metagenomics workflow was developed as two modules, one standard, and one optimized for improving the MAG quality in complex samples by implementing a combination of single- and co-assembly, and dereplication after binning. The exploration of the active pathways within the recovered MAGs can be visualized in ViMO, which also provides an overview of the MAG taxonomy and quality (contamination and completeness), and information about carbohydrate-active enzymes (CAZymes), as well as KEGG annotations and pathways, with counts and abundances at both mRNA and protein level. To achieve this, the metatranscriptomic reads and metaproteomic mass-spectrometry spectra are mapped onto predicted genes from the metagenome to analyse the functional potential of MAGs, as well as the actual expressed proteins and functions of the microbiome, all visualized in ViMO. Conclusion: Our three workflows for integrative meta-omics in combination with ViMO presents a progression in the analysis of 'omics data, particularly within Galaxy, but also beyond. The optimized metagenomics workflow allows for detailed reconstruction of microbial community consisting of MAGs with high quality, and thus improves analyses of the metabolism of the microbiome, using the metatranscriptomics and metaproteomics workflows. [ABSTRACT FROM AUTHOR]

Oort constants and local kinematics are vital parameters with which to study the structure and dynamics of the Milky Way. When GCNS was published, it provided a clean sample of stars in the solar vicinity, which gives us an ideal tool with which to determine these parameters. Our aim was to calculate the reliable Oort constants with GCNS. We determined the Oort constants using the GCNS from Gaia EDR3 with d < 100 pc . The proper motions and radial velocities were fitted with a maximum likelihood model. The uncertainties were obtained with an MCMC method. The sample was carefully selected to obtain a reliable result. The result yields the Oort constants A = 15.6 ± 1.6 km s − 1 kpc − 1 , B = − 15.8 ± 1.7 km s − 1 kpc − 1 , C = − 3.5 ± 1.6 km s − 1 kpc − 1 , and K = 2.7 ± 1.5 km s − 1 kpc − 1 . The non-zero C and K imply that the local disc is in a non-asymmetric potential. With the Oort constants, we derived the local angular velocity Ω 0 ≈ A − B = 31.4 ± 2.3 km s − 1 kpc − 1 . The solar motion ( U ⊙ , V ⊙ , W ⊙ ) was calculated as ( 10.1 ± 0.1 , 22.8 ± 0.1 , 7.8 ± 0.1 ) km s − 1 . [ABSTRACT FROM AUTHOR]

In this work, we investigate the possibility of observing quasars, particularly lensed quasars, by the Nancy Grace Roman Space Telescope (Roman). To this aim, based on the capabilities of the Roman Space Telescope and the results from the quasar luminosity function (QLF) in the infrared band of the Spitzer Space Telescope imaging survey, we calculated the number of quasars expected to be in its field of view. In order to estimate the number of lensed quasars, we develop a Monte Carlo simulation to estimate the probability that a quasar is lensed once or more times by foreground galaxies. Using the mass–luminosity distribution function of galaxies and the redshift distributions of galaxies and quasars, we find that 1 per 180 observed quasars will be lensed by foreground galaxies. Further on, adopting a singular isothermal sphere (SIS) model for lens galaxies, we calculate the time delay between lensed images for single and multiple lensing systems and present their distributions. We emphasize that detailed studies of these lensing systems will provide a powerful probe of the physical properties of quasars and may allow testing the mass distribution models of galaxies in addition to being extremely helpful for constraining the cosmological parameters. [ABSTRACT FROM AUTHOR]

Photometric techniques are one of the fundamentals and of great importance in the study of astronomical phenomena, including galaxies, and have witnessed a wide development during the last 100 years in equipment, sensitivity and accuracy in data analysis, especially after the direction toward space telescopes and the widespread use of a CCD camera. Therefore, in this research, an analytical study is made to compare two types of galaxies, which are spiral and lenticular galaxies, using photometric techniques and compare the photometric parameters of each type with tables and illustrations. An analysis of the morphological of the two galaxies is done by using the Least Square Fitting Method, and it is fully explained in the research. The results show the clear difference between the structure of spiral and lenticular galaxies. [ABSTRACT FROM AUTHOR]