Global preventive feedback of powerful radio jets on galaxy formation.

Firmly anchored on observational data, giant radio lobes from massive galaxies hosting supermassive black holes can exert a major negative feedback effect, by endowing the intergalactic gas with significant magnetic pressure hence retarding or preventing gas accretion onto less massive halos in the vicinity. Since massive galaxies that are largely responsible for producing the giant radio lobes, this effect is expected to be stronger in more overdense large-scale environments, such as protoclusters, than in underdense regions, such as voids. We show that by redshift [Formula: see text] halos with masses up to [Formula: see text] are significantly hindered from accreting gas due to this effect for radio bubble volume filling fraction of [Formula: see text], respectively. Since the vast majority of the stars in the universe at [Formula: see text][Formula: see text] 2 to 3 form precisely in those halos, this negative feedback process is likely one major culprit for causing the global downturn in star formation in the universe. It also provides a natural explanation for the rather sudden flattening of the slope of the galaxy rest-frame UV luminosity function around [Formula: see text]. A cross-correlation between protoclusters and Faraday rotation measures may test the predicted magnetic field. Inclusion of this external feedback process in the next generation of cosmological simulations may be imperative.

Apple Watch 6 vs. Galaxy Watch 4: A Validity Study of Step-Count Estimation in Daily Activities.

The purpose of this study was to examine the validity of two wearable smartwatches (the Apple Watch 6 (AW) and the Galaxy Watch 4 (GW)) and smartphone applications (Apple Health for iPhone mobiles and Samsung Health for Android mobiles) for estimating step counts in daily life. A total of 104 healthy adults (36 AW, 25 GW, and 43 smartphone application users) were engaged in daily activities for 24 h while wearing an ActivPAL accelerometer on the thigh and a smartwatch on the wrist. The validities of the smartwatch and smartphone estimates of step counts were evaluated relative to criterion values obtained from an ActivPAL accelerometer. The strongest relationship between the ActivPAL accelerometer and the devices was found for the AW (r = 0.99, p < 0.001), followed by the GW (r = 0.82, p < 0.001), and the smartphone applications (r = 0.93, p < 0.001). For overall group comparisons, the MAPE (Mean Absolute Percentage Error) values (computed as the average absolute value of the group-level errors) were 6.4%, 10.5%, and 29.6% for the AW, GW, and smartphone applications, respectively. The results of the present study indicate that the AW and GW showed strong validity in measuring steps, while the smartphone applications did not provide reliable step counts in free-living conditions.

Estimating minimal clinically important difference (MCID) for gastrointestinal symptoms in cystic fibrosis.

BACKGROUND: Minimal clinically important difference (MCID) is important to establish as a meaningful outcome in research when using patient reported outcome measures (PROMs). We determined the MCID using the distribution-based approach for three measurements used as part of the GALAXY study, which is an observational prospective study on gastrointestinal (GI) symptoms in cystic fibrosis (CF). METHODS: Four hundred and two persons with cystic fibrosis (PwCF) participated in the GALAXY study, all with baseline values available for all questionnaires. Mean age was 20.9 years (2.1- 61.1) with 75 females and 94 males under the age of 18 (42.04 %) and 118 females and 115 males aged 18 or older (57.99 %). MCID was measured for Patient Assessment of Constipation Symptoms (PAC-SYM), Patient Assessment of Upper Gastrointestinal Symptoms (PAGI-SYM), Patient Assessment of Constipation-Quality of Life (PAC-QOL) and their subscales. Two distribution-based approaches, defined as multiplications of the standard deviation (SD) or standard error of the mean (SEM), were used to approximate the MCID. RESULTS: The two distribution-based approaches for determining the MCID estimates produced comparable results in trends in MCIDs across the subscales and total scores. In general, MCID estimates of subscales for all three measurements were higher than their total score MCIDs. The one-half SD- and SEM-based MCID estimates for total scores of each questionnaire are as follows: PAC-SYM: 0.26 and 0.14; PAGI-SYM: 0.32 and 0.15; PAC-QOL: 0.27 and 0.18, respectively. CONCLUSION: This paper establishes initial MCIDs estimated by the distribution-based approach for the PAC-SYM, PAGI-SYM and PAC-QOL that can now be used to evaluate interventional studies that may impact gastrointestinal symptoms in PwCF.

A reproducible workflow for assembling the mitochondrial genome of Acheta domesticus (Orthoptera: Gryllidae).

In this study, we report the assembly and annotation of the mitochondrial genome (mitogenome) of Acheta domesticus from breeding facility, a species commonly known as the house cricket. This species is considered to be an important edible cricket. The mitogenome was assembled using a reproducible protocol implemented on the Galaxy Europe Server, which involved uploading paired-end fastq reads for bioinformatic analysis. The resulting mitogenome is 15,784 base pairs in length and has a GC content of 29.05%. The nucleotide composition of this mitogenome is similar to that of other insect mitogenomes, with A, T, C, and G nucleotides comprising 39.2%, 31.7%, 19.6%, and 9.5% of the mitogenome, respectively. The gene organization of the A. domesticus mitogenome is identical to that of other cricket species. The mitogenome consists of 37 genes, including 13 protein-coding genes, 22 tRNA genes, and two rRNA genes. The congruence between PCA and Bayesian evolutionary tree analysis in clustering the divergent A. domesticus sequences highlights these genomes as candidates for further study to elucidate their distinct features and evolutionary history.

Computational approaches to modeling dynamos in galaxies.

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.

Generalized open-source workflows for atomistic molecular dynamics simulations of viral helicases.

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.

Metaproteomics for Coinfections in the Upper Respiratory Tract: The Case of COVID-19.

The upper respiratory tract (URT) is home to a diverse range of microbial species. Respiratory infections disturb the microbial flora in the URT, putting people at risk of secondary infections. The potential dangers and clinical effects of bacterial and fungal coinfections with SARS-CoV-2 support the need to investigate the microbiome of the URT using clinical samples. Mass spectrometry (MS)-based metaproteomics analysis of microbial proteins is a novel approach to comprehensively assess the clinical specimens with complex microbial makeup. The coronavirus that causes severe acute respiratory syndrome (SARS-CoV-2) is responsible for the COVID-19 pandemic resulting in a plethora of microbial coinfections impeding therapy, prognosis, and overall disease management. In this chapter, the corresponding workflows for MS-based shotgun proteomics and metaproteomic analysis are illustrated.

Transcriptomic Data Analysis Using the Galaxy Platform: Coffee (Coffea arabica L.) Flowers as Example.

Coffee, an important agricultural product for tropical producing countries, is facing challenges due to climate change, including periods of drought, irregular rain distribution, and high temperatures. These changes result in plant water stress, leading to significant losses in coffee productivity and quality. Understanding the processes that affect coffee flowering is crucial for improving productivity and quality. In this chapter, we describe a protocol for transcriptome analysis using available Internet software, mainly in the Galaxy Platform, using RNA-Seq data from flowers collected from different parts of the coffee tree. The methods presented in this chapter provide a comprehensive protocol for transcriptome analysis of differentially expressed genes from flowers of coffee plant. This knowledge can be utilized in coffee genetic improvement programs, particularly in the selection of cultivars that are tolerant to water deficit.

What is new in FungiDB: a web-based bioinformatics platform for omics-scale data analysis for fungal and oomycete species.

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.

In vivo detection of DNA secondary structures using permanganate/S1 footprinting with direct adapter ligation and sequencing (PDAL-Seq).

DNA secondary structures are essential elements of the genomic landscape, playing a critical role in regulating various cellular processes. These structures refer to G-quadruplexes, cruciforms, Z-DNA or H-DNA structures, amongst others (collectively called 'non-B DNA'), which DNA molecules can adopt beyond the B conformation. DNA secondary structures have significant biological roles, and their landscape is dynamic and can rearrange due to various factors, including changes in cellular conditions, temperature, and DNA-binding proteins. Understanding this dynamic nature is crucial for unraveling their functions in cellular processes. Detecting DNA secondary structures remains a challenge. Conventional methods, such as gel electrophoresis and chemical probing, have limitations in terms of sensitivity and specificity. Emerging techniques, including next-generation sequencing and single-molecule approaches, offer promise but face challenges since these techniques are mostly limited to only one type of secondary structure. Here we describe an updated version of a technique permanganate/S1 nuclease footprinting, which uses potassium permanganate to trap single-stranded DNA regions as found in many non-B structures, in combination with S1 nuclease digest and adapter ligation to detect genome-wide non-B formation. To overcome technical hurdles, we combined this method with direct adapter ligation and sequencing (PDAL-Seq). Furthermore, we established a user-friendly pipeline available on Galaxy to standardize PDAL-Seq data analysis. This optimized method allows the analysis of many types of DNA secondary structures that form in a living cell and will advance our knowledge of their roles in health and disease.

FAIR data retrieval for sensitive clinical research data in Galaxy.

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.

Nanoparticle Deep-Subwavelength Dynamics Empowered by Optical Meron-Antimeron Topology.

Optical meron is a type of nonplanar topological texture mainly observed in surface plasmon polaritons and highly symmetric points of photonic crystals in the reciprocal space. Here, we report Poynting-vector merons formed at the real space of a photonic crystal for a Γ-point illumination. Optical merons can be utilized for subwavelength-resolution manipulation of nanoparticles, resembling a topological Hall effect on electrons via magnetic merons. In particular, staggered merons and antimerons impose strong radiation pressure on large gold nanoparticles (AuNPs), while focused hot spots in antimerons generate dominant optical gradient forces on small AuNPs. Synergistically, differently sized AuNPs in a still environment can be trapped or orbit in opposite directions, mimicking a coupled galaxy system. They can also be separated with a 10 nm precision when applying a flow velocity of >1 mm/s. Our study unravels a novel way to exploit topological textures for optical manipulation with deep-subwavelength precision and switchable topology in a lossless environment.

An exploratory in silico comparison of open-source codon harmonization tools.

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.

Transformer-based tool recommendation system in Galaxy.

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.

A Galaxy of informatics resources for MS-based proteomics.

INTRODUCTION: Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. AREAS COVERED: The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. EXPERT OPINION: The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

Meta-analysis of lean and obese RNA-seq datasets to identify genes targeting obesity.

Obesity is a global crisis leading to several metabolic disorders. Modernization and technology innovation has been easier for next generation sequencing using open-source online software galaxy, which allows the users to share their data and workflow mapping in an effortless manner. This study is to identify candidate genes for obesity by performing differential expression of genes. RNA-Seq analysis was performed for six different datasets retrieved from GEO database. 258 datasets from obese patients and 55 datasets from lean patients were analysed for differentially expressed genes (DEGs). DEGs analysis showed 1971 upregulated genes and 615 downregulated genes with log2FC count ≥ 2.5 and p-value < 0.05. The Gene enrichment analysis performed using Gene Ontology resource highlighted pathways associated to obesity such as cholesterol metabolism, Fat digestion and absorption and glycerolipid metabolism. Using string database protein-protein interactions network was built and the network clusters were visualized using Cytoscape software. The protein-protein interactions of the upregulated and downregulated genes were mapped to form a network, wherein PNLIP (Pancreatic lipase) and FTO (Fat mass and obesity associated protein) gene clusters were visualized as densely connected clusters in MCODE. PNLIP and FTO with its associated genes were identified as candidate genes for targeting obesity.

A bioinformatics screen reveals hox and chromatin remodeling factors at the Drosophila histone locus.

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.

Model Organism Modifier (MOM): a user-friendly Galaxy workflow to detect modifiers from genome sequencing data using Caenorhabditis elegans.

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.

Radiological and pathological analysis of the galaxy sign in patients with pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma.

BACKGROUND: Pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma sometimes presents as large pulmonary nodules composed of small nodular opacities (galaxy sign) on computed tomography (CT). The aim of this study was to assess the presence, usefulness, and pathological characteristics of the galaxy sign on CT of pulmonary MALT lymphoma. METHODS: From January 2011 to December 2021, chest CTs of 43 patients with pulmonary MALT lymphoma were reviewed by two radiologists for the galaxy sign and various other findings. Interreader agreement to characterize the galaxy sign and factors associated in making a correct first impression on CT prior to pathological diagnosis were assessed. Resected specimens were reviewed by two pathologists, and the proportion of peripheral lymphoma infiltrates was compared between lesions with and without the galaxy sign. RESULTS: Of 43 patients, 22 patients (44.2%) showed the galaxy sign (κ = 0.768, p < 0.0001). The galaxy sign (p = 0.010) was associated with making a correct first impression on CT prior to pathological diagnosis. On pathological examination, lesions showing the galaxy sign on CT demonstrated a significantly higher proportion of peripheral lymphoma infiltrates (p = 0.001). CONCLUSION: The galaxy sign can be seen on CT of pulmonary MALT lymphoma with a higher proportion of peripheral lymphoma infiltrates and may be useful in making a correct diagnosis of pulmonary MALT lymphoma.

Integrative meta-omics in Galaxy and beyond.

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.