Biology subjects

Gruening, B. A.

Publications and source records attributed to Gruening, B. A..

9 recordsLinked to original sources

bioRxiv2026

Machine learning-based prediction of memory requirements for metagenomic assembly in high-performance computing environments

Metagenomic assembly can be a computationally intensive step in microbiome analysis, with memory requirements that vary widely depending on input data characteristics. In workflow systems like Galaxy and large-scale platforms like MGnify, which run thousands of heterogeneous jobs, inaccurate memory allocation drives job failures and costly retries when underestimated, and reduces throughput when overestimated. Current approaches rely primarily on heuristic rules based on input file size or sample metadata, which often fail to generalize across diverse datasets. In this study, we present a machine learning-based framework for predicting memory requirements of metagenomic assembly using metaSPAdes. We analyzed 300 assembly jobs from diverse biomes and evaluated 18 predictive models using combinations of input file size, biome classification, and sequence-derived k-mer features. K-mer profiles were computed from raw sequencing data and summarized into statistical descriptors capturing sequence complexity and diversity. Model performance was assessed using both conventional regression metrics and a production-oriented cost function that accounts for retry policies and resource waste in high-performance computing environments. Our results show that machine learning models can outperform commonly used heuristics. In particular, models incorporating biome information achieved the best performance and can be tuned to favor conservative predictions that reduce job failure rates. Simpler models based solely on input file size also performed competitively, offering a practical alternative for systems with limited feature availability. When evaluated under realistic workload distributions, predictive approaches reduced total memory waste by several million gigabyte-hours per 1,000 jobs compared to static allocation strategies. These findings demonstrate that data-driven resource prediction can substantially improve efficiency in metagenomic workflows. The proposed framework is adaptable to different computational environments and provides a foundation for integrating predictive resource allocation into large-scale bioinformatics platforms beyond Galaxy.

microbiology↗

bioRxiv2020

Training Infrastructure as a Service

BackgroundHands-on training, whether it is in Bioinformatics or other scientific domains, requires significant resources and knowledge to setup and run. Trainers must have access to infrastructure that can support the sudden spike in usage, with classes of 30 or more trainees simultaneously running resource intensive tools. For efficient classes, the jobs must run quickly, without queuing delays, lest they disrupt the timetable set out for the class. Often times this is achieved via running on a private server where there is no contention for the queue, and therefore no or minimal waiting time. However, this requires the teacher or trainer to have the technical knowledge to manage compute infrastructure, in addition to their didactic responsibilities. This presents significant burdens to potential training events, in terms of infrastructure cost, person-hours of preparation, technical knowledge, and available staff to manage such events. FindingsGalaxy Europe has developed Training Infrastructure as a Service (TIaaS) which we provide to the scientific commnuity as a service built on top of the Galaxy Platform. Training event organisers request a training and Galaxy administrators can allocate private queues specifically for the training. Trainees are transparently placed in a private queue where their jobs run without delay. Trainers access the dashboard of the TIaaS Service and can remotely follow the progress of their trainees without in-person interactions. ConclusionsTIaaS on Galaxy Europe provides reusable and fast infrastructure for Galaxy training. The instructor dashboard provides visibility into class progress, making in-person trainings more efficient and remote training possible. In the past 24 months, > 110 trainings with over 3000 trainees have used this infrastructure for training, across scientific domains, all enjoying the accessibility and reproducibility of Galaxy for training the next generation of bioinformaticians. TIaaS itself is an extension to Galaxy which can be deployed by any Galaxy administrator to provide similar benefits for their users. https://galaxyproject.eu/tiaas

bioinformatics↗

bioRxiv2020

The landscape of SARS-CoV-2 RNA modifications

In 2019 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the first documented cases of severe lung disease COVID-19. Since then, SARS-CoV-2 has been spreading around the globe resulting in a severe pandemic with over 500.000 fatalities and large economical and social disruptions in human societies. Gaining knowledge on how SARS-Cov-2 interacts with its host cells and causes COVID-19 is crucial for the intervention of novel therapeutic strategies. SARS-CoV-2, like other coronaviruses, is a positive-strand RNA virus. The viral RNA is modified by RNA-modifying enzymes provided by the host cell. Direct RNA sequencing (DRS) using nanopores enables unbiased sensing of canonical and modified RNA bases of the viral transcripts. In this work, we used DRS to precisely annotate the open reading frames and the landscape of SARS-CoV-2 RNA modifications. We provide the first DRS data of SARS-CoV-2 in infected human lung epithelial cells. From sequencing three isolates, we derive a robust identification of SARS-CoV-2 modification sites within a physiologically relevant host cell type. A comparison of our data with the DRS data from a previous SARS-CoV-2 isolate, both raised in monkey renal cells, reveals consistent RNA modifications across the viral genome. Conservation of the RNA modification pattern during progression of the current pandemic suggests that this pattern is likely essential for the life cycle of SARS-CoV-2 and represents a possible target for drug interventions.

genetics↗

bioRxiv2020

Intuitive, reproducible high-throughput molecular dynamics in Galaxy: a tutorial

This paper is a tutorial developed for the data analysis platform Galaxy. The purpose of Galaxy is to make high-throughput computational data analysis, such as molecular dynamics, a structured, reproducible and transparent process. In this tutorial we focus on 3 questions: How are protein-ligand systems parameterized for molecular dynamics simulation? What kind of analysis can be carried out on molecular trajectories? How can high-throughput MD be used to study multiple ligands? After finishing you will have learned about force-fields and MD parameterization, how to conduct MD simulation and analysis for a protein-ligand system, and understand how different molecular interactions contribute to the binding affinity of ligands to the Hsp90 protein.Competing Interest StatementThe authors have declared no competing interest.View Full Text

scientific communication and education↗

bioRxiv2020

User-friendly, scalable tools and workflows for single-cell analysis

Single-cell RNA-Seq (scRNA-Seq) data analysis requires expertise in command-line tools, programming languages and scaling on compute infrastructure. As scRNA-Seq becomes widespread, computational pipelines need to be more accessible, simpler and scalable. We introduce an interactive analysis environment for scRNA-Seq, based on Galaxy, with ~70 functions from major single-cell analysis tools, which can be run on compute clusters, cloud providers or single machines, to bring compute to the data in scRNA-Seq.

bioinformatics↗

bioRxiv2020

Pou5f3 and Sox19b select gene expression repertoire at Zygotic Genome Activation

Awakening of zygotic transcription in animal embryos relies on maternal pioneer transcription factors. The interplay of global and specific functions of these proteins remains poorly understood. Here, we analyze chromatin accessibility and time-resolved transcription in single and double mutant zebrafish embryos lacking pluripotency factors Pou5f3 and Sox19b. We show that two factors modify chromatin in a largely independent manner. We distinguish four types of direct enhancers by differential requirements for Pou5f3 or Sox19b. We demonstrate that changes in chromatin accessibility of enhancers underlie the changes in zygotic expression repertoire in the double mutants. Pou5f3 or Sox19b promote chromatin accessibility of enhancers linked to the genes involved in gastrulation and ventral fate specification. The genes regulating mesendodermal and dorsal fates are primed for activation independently of Pou5f3 and Sox19b. Strikingly, simultaneous loss of Pou5f3 and Sox19b leads to premature expression of genes, involved in regulation of organogenesis and differentiation.

developmental biology↗

bioRxiv2020

Survey of metaproteomics software tools for functional microbiome analysis.

To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional role of expressed microbial genes/proteins. This can be accomplished through metaproteomics, which characterizes the protein complement of the microbiome. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, so that researchers can make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental carie plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! Tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN6, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the Universal Protein Knowledgebase revealed that the sensitivity and specificity of functional annotation differed between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated the most precise GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.

bioinformatics↗

bioRxiv2017

Distinct epigenetic programs regulate cardiac myocyte development and disease in the human heart in vivo

Epigenetic mechanisms and transcription factor networks essential for differentiation of cardiac myocytes have been uncovered. However, reshaping of the epigenome of these terminally differentiated cells during fetal development, postnatal maturation and in disease remains unknown. Thus, the aim of this study was to determine the dynamics of the cardiac myocyte epigenome during development and in chronic heart failure. Prenatal development and postnatal maturation are characterized by a cooperation of active CpG methylation and histone marks at cis-regulatory and genic regions to shape the cardiac myocyte transcriptome. In contrast, pathological gene expression in terminal heart failure is accompanied by changes in active histone marks without major alterations in CpG methylation and repressive chromatin marks. Notably, cis-regulatory regions in cardiac myocytes are significantly enriched for cardiovascular disease-associated variants. This study uncovers distinct layers of epigenetic regulation not only during prenatal development and postnatal maturation but also in diseased human cardiac myocytes.

genomics↗

bioRxiv2017

High-resolution TADs reveal DNA sequences underlying genome organization in flies

Eukaryotic chromatin is partitioned into domains called TADs that are broadly conserved between species and virtually identical among cell types within the same species. Previous studies in mammals have shown that the DNA binding protein CTCF and cohesin contribute to a fraction of TAD boundaries. Apart from this, the molecular mechanisms governing this partitioning remain poorly understood. Using our new software, HiCExplorer, we annotated high-resolution (570 bp) TAD boundaries in flies and identified eight DNA motifs enriched at boundaries. Known insulator proteins bind five of these motifs while the remaining three motifs are novel. We find that boundaries are either at core promoters of active genes or at non-promoter regions of inactive chromatin and that these two groups are characterized by different sets of DNA motifs. Most boundaries are present at divergent promoters of constitutively expressed genes and the gene expression tends to be coordinated within TADs. In contrast to mammals, the CTCF motif is only present on 2% of boundaries in flies. We demonstrate that boundaries can be accurately predicted using only the motif sequences, along with open chromatin, suggesting that DNA sequence encodes the 3D genome architecture in flies. Finally, we present an interactive online database to access and explore the spatial organization of fly, mouse and human genomes, available at http://chorogeome.ie-freiburg.mpg.de.

genomics↗