Biology subjects

Backofen, R.

Publications and source records attributed to Backofen, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

RNAformer: A Simple Yet Effective Deep Learning Model for RNA Secondary Structure Prediction

AO_SCPLOWBSTRACTC_SCPLOWPredicting RNA secondary structure is essential for understanding RNA function and developing RNA-based therapeutics. Despite recent advances in deep learning for structural biology, its application to RNA secondary structure prediction remains contentious. A primary concern is the control of homology between training and test data. Moreover, deep learning approaches often incorporate complex multi-model systems, ensemble strategies, or require external data. Here, we present the RNAformer, a scalable axial-attention-based deep learning model designed to predict secondary structure directly from a single RNA sequence without additional requirements. We demonstrate the benefits of this lean architecture by learning an accurate biophysical RNA folding model using synthetic data. Trained on experimental data, our model overcomes previously reported caveats in deep learning approaches with a novel homology-aware data pipeline. The RNAformer achieves state-of-the-art performance on RNA secondary structure prediction, out-performing both traditional non-learning-based methods and existing deep learning approaches, while carefully considering sequence and structure similarities.

bioinformatics↗

bioRxiv2023

Partial RNA Design

RNA design is a key technique to achieve new functionality in fields like synthetic biology or biotechnology. Computational tools could help to find such RNA sequences but they are often limited in their formulation of the search space. In this work, we propose partial RNA design, a novel RNA design paradigm that addresses the limitations of current RNA design formulations. Partial RNA design describes the problem of designing RNAs from arbitrary RNA sequences and structure motifs with multiple design goals. By separating the design space from the objectives, our formulation enables the design of RNAs with variable lengths and desired properties, while still allowing precise control over sequence and structure constraints at individual positions. Based on this formulation, we introduce a new algorithm, libLEARNA, capable of efficiently solving different constraint RNA design tasks. A comprehensive analysis of various problems, including a realistic riboswitch design task, reveals the outstanding performance of libLEARNA and its robustness.

bioinformatics↗

bioRxiv2023

Ribosome profiling reveals the fine-tuned response of Escherichia coli to mild and severe acid stress

The ability to respond to acidic environments is crucial for neutralophilic bacteria. Escherichia coli has a well-characterized regulatory network that triggers a multitude of defense mechanisms to counteract excess of protons. Nevertheless, systemic studies of the transcriptional and translational reprogramming of E. coli to different degrees of acid stress have not yet been performed. Here, we used ribosome profiling and RNA sequencing to compare the response of E. coli (pH 7.6) to sudden mild (pH 5.8) and severe near-lethal acid stress (pH 4.4) conditions that mimic passage through the gastrointestinal tract. We uncovered new differentially regulated genes and pathways, key transcriptional regulators, and 18 novel acid-induced candidate sORFs. By using machine learning leveraging large compendia of publicly available E. coli expression data, we were able to distinguish between the response to acid stress and general stress. These results expand the acid resistance network and provide new insights into the fine-tuned response of E. coli to mild and severe acid stress.

microbiology↗

bioRxiv2023

The role of the 5' sensing function of ribonuclease E in cyanobacteria

RNA degradation is crucial for many processes in pro- and eukaryotic organisms. In bacteria, the preference of the central ribonucleases RNase E, RNase J and RNase Y towards 5-monophosphorylated RNAs is considered important for RNA degradation. For RNase E, the underlying mechanism is termed 5 sensing. Cyanobacteria, such as Synechocystis sp. PCC 6803 (Synechocystis), encode RNase E and RNase J homologs. Here, we constructed a Synechocystis strain lacking the 5 sensing function of RNase E and mapped on a transcriptome-wide level 292 5-sensing-dependent cleavage sites. These included so far unknown targets such as the 5 untranslated region of the response regulator gene lsiR; trxA, apcE and atpI mRNAs, encoding proteins related to energy metabolism; as well as sbtB and rbcLXS encoding proteins relevant for carbon fixation. Cyanobacterial 5 sensing is important for the maturation of rRNA and several tRNAs, including tRNAGluUUC. This tRNA activates glutamate for tetrapyrrole biosynthesis in plant chloroplasts and most prokaryotes. We found that increased RNase activities leads to a higher copy number of the major Synechocystis plasmids pSYSA and pSYSM. The results provide a first step towards understanding the relative importance of different target mechanisms of RNase E outside Escherichia coli.

microbiology↗

bioRxiv2022

Transformer-based tool recommendation system in Galaxy

Galaxy is a web-based open-source platform for scientific analyses. Researchers use thousands of high-quality tools and workflows for their respective analyses. 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-based neural network on workflows available on Galaxy Europe. Compared to the existing tool recommender system on Galaxy Europe that trains a recurrent neural network, the transformer-based neural network achieves two times faster convergence, has a four times lower model usage (model loading + prediction) time and shows a better generalisation that goes beyond training workflows. The scripts to create the recommendation model are available under MIT licence at https://github.com/anuprulez/galaxy_tool_recommendation_transformers.

bioinformatics↗

bioRxiv2022

Improved discovery of RNA-binding protein binding sites in eCLIP data using DEWSeq

Enhanced crosslinking and immunoprecipitation (eCLIP) sequencing is a powerful method for transcriptome-wide detection of binding sites of RNA-binding proteins (RBPs). However, identified crosslink sites can profoundly deviate from experimentally established functional elements of even well-studied RBPs. Current peak-calling strategies result in low replication and high false-positive rates. Here, we present the R/Bioconductor package DEWSeq that makes full use of replicate information and size-matched input controls. We benchmarked DEWSeq on 107 RBPs for which both eCLIP data and RNA sequence motifs are available and were able to more than double the number of motif-containing binding regions relative to standard eCLIP processing (2.3-fold median). The improvement not only relates to the number of binding sites (e.g., 3.1-fold of known motifs for RBFOX2), but also their subcellular localisation (e.g., 1.9-fold of mitochondrial genes for FASTKD2) and structural targets (e.g., 2.2-fold increase of stem-loop regions for SLBP). DEWSeq therefore shows promise as an improved processing method for eCLIP protein-RNA interaction data.

molecular biology↗

bioRxiv2022

Small proteome of the nitrogen-fixing plant symbiont Sinorhizobium meliloti

The soil-dwelling plant symbiont Sinorhizobium meliloti is a major model organism of Alphaproteobacteria. Despite numerous detailed OMICS studies, information about small open reading frame (sORF)-encoded proteins (SEPs) is largely missing, because sORFs are poorly annotated, and SEPs are hard to detect experimentally. However, given that SEPs can fulfill important functions, cataloging the full complement of translated sORFs is critical for analyzing their roles in bacterial physiology. Ribosome profiling (Ribo-seq) can detect translated sORFs with high sensitivity, but is not yet routinely applied to bacteria because it must be adapted for each species. Here, we established a Ribo-seq procedure for S. meliloti 2011 based on RNase I digestion and detected translation for 60% of the annotated coding sequences during growth in minimal medium. Using ORF prediction tools based on Ribo-seq data, subsequent filtering, and manual curation, the translation of 37 non-annotated sORFs with [≤] 70 amino acids was predicted with high confidence. The Ribo-seq data were supplemented by mass spectrometry (MS) analyses from three sample preparation approaches and two integrated proteogenomic search databases (iPtgxDBs). Searches against a standard and a 20-fold smaller Ribo-seq data-informed custom iPtgxDB confirmed many annotated SEPs and identified 11 additional novel SEPs. Epitope tagging and Western blot analysis confirmed the translation of 15 out of 20 SEPs selected from the translatome map. Overall, by applying MS and Ribo-seq as complementary approaches, the small proteome of S. meliloti was substantially expanded by 48 novel SEPs. Several of them are conserved from Rhizobiaceae to Bacteria, suggesting important physiological functions.

molecular biology↗

bioRxiv2022

Complementary Ribo-seq approaches map the translatome and provide a small protein census in the foodborne pathogen Campylobacter jejuni

BackgroundKnowing the molecules encoded by bacterial pathogens and how their expression is regulated is essential to understand how they survive, colonize, and cause disease. RNA-seq technologies that map transcriptomes have revealed a wealth of new transcripts in bacterial pathogens. However, they do not provide direct evidence or coordinates for coding potential. In particular, they miss small proteins ([≤] 50-100 amino acids) translated from small open reading frames (sORFs). However, this still poorly annotated component of bacterial genomes shows emerging roles in bacterial physiology and virulence. ResultsHere, we present an integrated approach based on complementary ribosome profiling (Ribo-seq) techniques to map the "translatome" of Campylobacter jejuni, the most common cause of bacterial gastroenteritis. Besides conventional Ribo-seq, we employed translation initiation site (TIS) profiling to map start codons and reveal internal sORFs. We also developed a Ribo-seq approach for mapping of translation termination sites (TTS), which revealed stop codons not apparent from the reference genome in virulence-associated loci. Our translatome map confirms translation of leaderless ORFs and leader peptides, re-annotates start or stop codons of 35 genes, and reveals isoforms generated by internal start sites. It also adds 42 novel sORFs in diverse contexts to the C. jejuni annotation, such as within small RNAs, in 5' untranslated regions (UTRs), or internal/out-of-frame/antisense in larger ORFs. Using epitope tagging/western blot and mass spectrometry we validated expression of almost 60 annotated and novel sORFs, including cioY, which we show encodes a conserved, 34 amino acid component of the CioAB terminal oxidase. ConclusionsOverall, we provide a blueprint for integrating several Ribo-seq approaches to refine and enrich bacterial annotations.

genomics↗

bioRxiv2022

Genome wide CRISPR screen for Pasteurella multocida toxin (PMT) binding proteins reveals LDL Receptor Related Protein1 (LRP1) as crucial cellular receptor

PMT is a protein toxin produced by Pasteurella multocida serotypes A and D. As causative agent of atrophic rhinitis in swine, it leads to rapid degradation of the nasal turbinate bone. The toxin acts as a deamidase to modify a crucial glutamine in heterotrimeric G proteins, which results in constitutive activation of the G proteins and permanent stimulation of numerous downstream signaling pathways. Using a lentiviral based genome wide CRISPR knockout screen in combination with a lethal toxin chimera, consisting of full length inactive PMT and the catalytic domain of diphtheria toxin, we identified the LRP1 gene encoding the Low-Density Lipoprotein Receptor-related protein 1 as a critical host factor for PMT function. Loss of LRP1 reduced PMT binding and abolished the cellular response and deamidation of heterotrimeric G proteins, confirming LRP1 to be crucial for PMT uptake. Expression of LRP1 or cluster 4 of LRP1, respectively, restored intoxication of the knockout cells. In summary our data demonstrate secretory cells as entry site of PMT into airway epithelia and present LRP1 as crucial host entry factor for PMT intoxication by acting as its primary cell surface receptor.

pharmacology and toxicology↗

bioRxiv2022

TREK-1 knock-down in human atrial fibroblasts leads to a myofibroblastic phenotype: a role in phenoconversion and overview of mechano-sensitive channel mRNA expression in cardiac diseases.

Cardiac cell mechanical environment changes on a beat-by-beat basis and also in the course of various cardiac diseases. Cells sense and adapt to such mechanical cues via specialized mechano-sensors mediating adaptive signaling cascades. Here, we report TREK-1 mRNA expression and activity in human atrial fibroblasts and reveal a cross talk between TREK-1 and fibroblast phenoconversion. With the aim to reveal additional candidates underlying mechano-transduction relevant to cardiac diseases, we investigated mechano-sensitive ion channels (MSC) mRNA expression in diseased and non-diseased human hearts. Our results showed higher TREK-1 expression and activity in fibroblasts compared to myofibroblasts and we found that TREK-1 down-regulation leads a more myofibroblastic phenotype suggesting a role for this mechano-sensor in phenoconversion. In addition, TREK-1 is preferentially expressed in the left atrium compared to the right one and its expression is not significantly changed when fibroblasts from patients in sinus rhythm vs. sustained atrial fibrillation are compared. At the whole-heart level, numerous MSC were differentially expressed between atrial and ventricular or between non-diseased and diseased tissue samples. Thus, we identify atrial fibroblast-specific TREK-1 expression and activity and reveal a role of TREK-1 in atrial fibroblast pheno-conversion. We also provide a comprehensive overview of cardiac MSC mRNA expression in atrial and ventricular tissue from diseased and non-diseased patients, identifying potential novel candidates underlying mechanotransduction in cardiac diseases.

cell biology↗

bioRxiv2022

An accessible infrastructure for artificial intelligence using a docker-based Jupyterlab in Galaxy

Artificial intelligence (AI) programs that train on a large amount of data require powerful compute infrastructure. Jupyterlab notebook provides an excellent framework for developing AI programs but it needs to be hosted on a powerful infrastructure to enable AI programs to train on large data. An open-source, docker-based, and GPU-enabled jupyterlab notebook infrastructure has been developed that runs on the public compute infrastructure of Galaxy Europe for rapid prototyping and developing end-to-end AI projects. Using such a notebook, long-running AI model training programs can be executed remotely. Trained models, represented in a standard open neural network exchange (ONNX) format, and other resulting datasets are created in Galaxy. Other features include GPU support for faster training, git integration for version control, the option of creating and executing pipelines of notebooks, and the availability of multiple dashboards for monitoring compute resources. These features make the jupyterlab notebook highly suitable for creating and managing AI projects. A recent scientific publication that predicts infected regions of COVID-19 CT scan images is reproduced using multiple features of this notebook. In addition, colabfold, a faster implementation of alphafold2, can also be accessed in this notebook to predict the 3D structure of protein sequences. Jupyterlab notebook is accessible in two ways - first as an interactive Galaxy tool and second by running the underlying docker container. In both ways, long-running training can be executed on Galaxys compute infrastructure. The scripts to create the docker container are available under MIT license at https://github.com/anuprulez/ml-jupyter-notebook. Contactkumara@informatik.uni-freiburg.de anup.rulez@gmail.com

bioinformatics↗

bioRxiv2022

Tyrosine kinase inhibitors trigger lysosomal damage-associated cell lysis to activate the NLRP3 inflammasome

Inflammasomes are intracellular protein complexes that control proteolytic maturation and secretion of inflammatory interleukin-1 (IL-1) family cytokines and are thus important in host defense. While some inflammasomes are activated simply by binding to pathogen-derived molecules, others, including those nucleated by NLRP3 and NLRP1, have more complex activation mechanisms that are not fully understood. We screened a library of small molecules to identify new inflammasome activators that might shed light on activation mechanisms. In addition to validating dipeptidyl peptidase (DPP) inhibitors as NLRP1 activators, we find that clinical tyrosine kinase inhibitors (TKIs) including imatinib and masitinib activate the NLRP3 inflammasome. Mechanistically, these TKIs cause lysosomal swelling and damage, leading to cathepsin-mediated destabilization of myeloid cell membranes and cell lysis. This is accompanied by potassium (K+) efflux, which activates NLRP3. Both lytic cell death and NLRP3 activation but not lysosomal damage induced by TKIs are prevented by the cytoprotectant high molecular weight polyethylene glycol (PEG). Our study establishes a screening method that can be expanded for inflammasome research and immunostimulatory drug development, and provides new insight into immunological off-targets that may contribute to efficacy or adverse effects of TKIs. One Sentence SummaryA functional small molecule screen identifies imatinib, masitinib and other tyrosine kinase inhibitors that destabilize myeloid cell lysosomes, leading to cell lysis and K+ efflux-dependent NLRP3 inflammasome activation.

immunology↗

bioRxiv2021

Transcriptome-wide in vivo mapping of cleavage sites for the compact cyanobacterial ribonuclease E reveals insights into its function and substrate recognition

Ribonucleases are crucial enzymes in RNA metabolism and post-transcriptional regulatory processes in bacteria. Cyanobacteria encode the two essential ribonucleases RNase E and RNase J. Cyanobacterial RNase E is shorter than homologues in other groups of bacteria and lacks both the chloroplast-specific N-terminal extension as well as the C-terminal domain typical for RNase E of enterobacteria. In order to investigate the function of RNase E in the model cyanobacterium Synechocystis sp. PCC 6803, we engineered a temperature-sensitive RNase E mutant by introducing two site-specific mutations, I65F and spontaneously occurring V94A. This enabled us to perform RNA-seq after the transient inactivation of RNase E by a temperature shift (TIER-seq) and to map 1,472 RNase-E-dependent cleavage sites. We inferred a dominating cleavage signature consisting of an adenine at the -3 and a uridine at the +2 position within a single-stranded segment of the RNA. The data identified putative RNase-E-dependent instances of operon discoordination, mRNAs likely regulated jointly by RNase E and an sRNA, potential 3 end-derived sRNAs and a dual-acting mechanism for the glutamine riboswitch. Our findings substantiate the pivotal role of RNase E in post-transcriptional regulation and suggest the redundant or concerted action of RNase E and RNase J in cyanobacteria.

microbiology↗

bioRxiv2021

RiboReport - Benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria

Small proteins, those encoded by open reading frames, with less than or equal to 50 codons, are emerging as an important class of cellular macromolecules in all kingdoms of life. However, they are recalcitrant to detection by proteomics or in silico methods. Ribosome profiling (Ribo-seq) has revealed widespread translation of sORFs in diverse species, and this has driven the development of ORF detection tools using Ribo-seq read signals. However, only a handful of tools have been designed for bacterial data, and have not yet been systematically compared. Here, we have performed a comprehensive benchmark of ORF prediction tools which handle bacterial Ribo-seq data. For this, we created a novel Ribo-seq dataset for E. coli, and based on this plus three publicly available datasets for different bacteria, we created a benchmark set by manual labeling of translated ORFs using their Ribo-seq expression profile. This was then used to investigate the predictive performance of four Ribo-seq-based ORF detection tools we found are compatible with bacterial data (Reparation_blast, DeepRibo, Ribo-TISH and SPECtre). The tool IRSOM was also included as a comparison for tools using coding potential and RNA-seq coverage only. DeepRibo and Reparation_blast robustly predicted translated ORFs, including sORFs, with no significant difference for those inside or outside of operons. However, none of the tools was able to predict a set of recently identified, novel, experimentally-verified sORFs with high sensitivity. Overall, we find there is potential for improving the performance, applicability, usability, and reproducibility of prokaryotic ORF prediction tools that use Ribo-Seq as input. Key pointsO_LICreated a benchmark set for Ribo-seq based ORF prediction in bacteria C_LIO_LIDeepRibo the first choice for bacterial ORF prediction tasks C_LIO_LITool performance is comparable between operon vs single gene regions C_LIO_LIIdentification of novel sORF with DeepRibo is, with restrictions, possible, by using the top 100 novel sORFs sorted by rank. C_LIO_LIExperimental results show that considering translation initiation site data could boost the detection of novel small ORFs C_LIO_LIDetermination of novel sORFs in E. coli using a new experimental protocol to enrich for translation initiation site. These data-set shows that still a significant part (here 8 out 24, so 1/3) are not detected dispute sufficient Ribo-seq signal. An additional 7 could be recovered using translation initiation site protocols. C_LIO_LITools should embrace the use of replicate data and improve packaging, usability and documentation. C_LI

bioinformatics↗

bioRxiv2021

CdrS is a global transcriptional regulator influencing cell division in Haloferax volcanii

Transcriptional regulators that integrate cellular and environmental signals to control cell division are well known in bacteria and eukaryotes, but their existence is poorly understood in archaea. We identified a conserved gene (cdrS) that encodes a small protein and is highly transcribed in the model archaeon Haloferax volcanii. The cdrS gene could not be deleted, but CRISPRi-mediated repression of the cdrS gene caused slow growth, cell division defects, and changed the expression of multiple genes and their products associated with cell division, protein degradation and metabolism. Consistent with this complex regulatory network, overexpression of cdrS inhibited cell division, whereas overexpression of the operon encoding both CdrS and a tubulin-like cell division protein (FtsZ2) stimulated division. ChIP-Seq identified 18 DNA-binding sites of the CdrS protein including one upstream of the promoter for diadenylate cyclase, which is an essential gene involved in c-di-AMP signalling implicated in the regulation of cell division. These findings suggest that CdrS is a transcription factor that plays a central role in a regulatory network coordinating metabolism and cell division. ImportanceCell division is a central mechanism of life, and is essential for growth and development. Bacteria and Eukarya have different mechanisms for cell division, which have been studied in detail. In contrast, cell division in Archaea is still understudied, and its regulation is poorly understood. Interestingly, different cell division machineries appear in the Archaea, with the Euryarchaeota using a cell division apparatus based on the tubulin-like cytoskeletal protein FtsZ, as in bacteria. Here we identify the small protein CdrS as essential for survival and a central regulator of cell division in the Euryarchaeon Haloferax volcanii. CdrS also appears to coordinate other cellular pathways including synthesis of signalling molecules and protein degradation. Our results show that CdrS plays a sophisticated role in cell division, including regulation of numerous associated genes. These findings are expected to initiate investigations into conditional regulation of division in archaea.

microbiology↗

bioRxiv2021

The temperature-regulated DEAD-box RNA helicase CrhR interactome: Autoregulation and photosynthesis-related transcripts

RNA helicases play crucial functions in RNA biology. In plants, RNA helicases are encoded by large gene families, performing roles in abiotic stress responses, development, the post-transcriptional regulation of gene expression as well as house-keeping functions. Several of these RNA helicases are targeted to the organelles, mitochondria and chloroplasts. Cyanobacteria are the direct evolutionary ancestors of plant chloroplasts. The cyanobacterium Synechocystis 6803 encodes a single DEAD-box RNA helicase, CrhR, that is induced by a range of abiotic stresses, including low temperature. Though the {Delta}crhR mutant exhibits a severe cold-sensitive phenotype, the physiological function(s) performed by CrhR have not been described. To identify transcripts interacting with CrhR, we performed RNA co-immunoprecipitation with extracts from a Synechocystis crhR deletion mutant expressing the FLAG-tagged native CrhR or a K57A mutated version with an anticipated enhanced RNA binding. The composition of the interactome was strikingly biased towards photosynthesis-associated and redox-controlled transcripts. A transcript highly enriched in all experiments was the crhR mRNA, suggesting an auto-regulatory molecular mechanism. The identified interactome explains the described physiological role of CrhR in response to the redox poise of the photosynthetic electron transport chain and characterizes CrhR as an enzyme with a diverse range of transcripts as molecular targets. HighlightThe cyanobacterial DEAD-box RNA helicase CrhR binds mainly photosynthesis-associated and redox-controlled transcripts connecting its regulation, localization and phenotypes of mutants for the first time with a set of potential RNA targets.

plant biology↗

bioRxiv2020

CRISPRIdentify: Identification of CRISPR arrays using machine learning approach

CRISPR-Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR- Cas systems heavily rely on RNA components. These CRISPR (cr) RNAs are repeat-spacer units that are produced by processing of pre-crRNA, the transcript of CRISPR arrays, and guide Cas protein(s) to the cognate invading nucleic acids, enabling their destruction. Several bioinformatics tools have been developed to detect CRISPR arrays based solely on DNA sequences, but all these tools employ the same strategy of looking for repetitive patterns, which might correspond to CRISPR array repeats. The identified patterns are evaluated using a fixed, built-in scoring function, and arrays exceeding a cut-off value are reported. Here, we instead introduce a data-driven approach that uses machine learning to detect and differentiate true CRISPR arrays from false ones based on several features. Our CRISPR detection tool, CRISPRidentify, performs three steps: detection, feature extraction and classification based on manually curated sets of positive and negative examples of CRISPR arrays. The identified CRISPR arrays are then reported to the user accompanied by detailed annotation. We demonstrate that our approach identifies not only previously detected CRISPR arrays, but also CRISPR array candidates not detected by other tools. Compared to other methods, our tool has a drastically reduced false positive rate. In contrast to the existing tools, our approach not only provides the user with the basic statistics on the identified CRISPR arrays but also produces a certainty score as a practical measure of the likelihood that a given genomic region is a CRISPR array.

bioinformatics↗

bioRxiv2020

A single-cell RNA-seq Training and Analysis Suite using the Galaxy Framework

BackgroundThe vast ecosystem of single-cell RNA-seq tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically-driven methods needed to process and understand these ever-growing datasets. ResultsHere we outline several Galaxy workflows and learning resources for scRNA-seq, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows and trainings that not only enable users to perform one-click 10x preprocessing, but also empowers them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a wide range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal and clustering. The teaching resources cover an assortment of different concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. ConclusionsThe reproducible and training-oriented Galaxy framework provides a sustainable HPC environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy Community provide a means for users to learn, publish and teach scRNA-seq analysis. Key PointsO_LISingle-cell RNA-seq has stabilised towards 10x Genomics datasets. C_LIO_LIGalaxy provides rich and reproducible scRNA-seq workflows with a wide range of robust tools. C_LIO_LIThe Galaxy Training Network provides tutorials for the processing of both 10x and non-10x datasets. C_LI

bioinformatics↗