Biology subjects

Heyl, F.

Publications and source records attributed to Heyl, F..

8 recordsLinked to original sources

bioRxiv2025

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects tissue and organ function. Since 2020, more than 50 spatially aware clustering (SAC) methods have been developed for this purpose. However, the reliability of current benchmarks is undermined by their narrow focus on Visium and brain tissue datasets, as well as incorrect interpretation of manual annotation as ground truth. Here, we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration, and metric evaluation, and is designed to rapidly incorporate new methods and datasets. SACCELERATOR currently includes 22 SAC methods applied to 15 datasets spanning 9 technologies and diverse tissue types. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods across tissues and platforms. We also demonstrate that anatomical labels commonly used as ground truths are often biased, potentially error-prone, and, in some cases, unsuitable for benchmarking efforts. Rather than scoring and comparing methods, we propose a consensus-guided workflow that aggregates clustering results to generate consensus representations. Descriptive spatial metrics highlight areas of high entropy where method disagreement is highest, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual methods and manual annotations. Our results underscore the need for iterative, expert-in-the-loop analysis and reveal that traditional evaluation metrics do not always capture the subjective qualities of results. By improving tissue annotation and addressing key benchmarking limitations, SACCELERATOR provides a robust foundation for advancing spatial omics research.

genomics↗

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

Galaxy Training: A Powerful Framework for Teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis and stewardship are still rarely taught in life science educational programs [1], resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org); an open access, community-driven framework for the collection of FAIR training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform [2]. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

bioinformatics↗

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

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

HRIBO - High-throughput analysis of bacterial ribosome profiling data

MotivationRibosome profiling (Ribo-seq) is a powerful approach based on ribosome-protected RNA fragments to explore the translatome of a cell, and is especially useful for the detection of small proteins (<=70 amino acids) that are recalcitrant to biochemical and in silico approaches. While pipelines are available to analyze Ribo-seq data, none are designed explicitly for the analysis of Ribo-seq data from prokaryotes, nor are they focused on the discovery of unannotated open reading frames (ORFs) in bacteria. ResultsWe present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing and quality control steps. Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary bacteria-focused tools, and integrates them with additional features. This facilitates the rapid discovery of novel ORFs and their prioritization for functional characterization. AvailabilityHRIBO is a free and open source project available under the GPL-3 license at: https://github.com/RickGelhausen/HRIBO

bioinformatics↗

bioRxiv2019

GraphProt2: A novel deep learning-based method for predicting binding sites of RNA-binding proteins

CLIP-seq is the state-of-the-art technique to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression which can be highly variable between conditions, and thus cannot provide a complete picture of the RBP binding landscape. This creates a demand for computational methods to predict missing binding sites. Here we present GraphProt2, a computational RBP binding site prediction framework based on graph convolutional neural networks (GCNs). In contrast to current CNN methods, GraphProt2 offers native support for the encoding of base pair information as well as variable length input, providing increased flexibility and the prediction of nucleotide-wise RBP binding profiles. We demonstrate its superior performance compared to GraphProt and two CNN-based methods on single as well as combined CLIP-seq datasets. Conceived as an end-to-end method, GraphProt2 includes all necessary functionalities, from dataset generation over model training to the evaluation of binding preferences and binding site prediction. Various input types and features are supported, accompanied by comprehensive statistics and visualizations to inform the user about datatset characteristics and learned model properties. All this makes GraphProt2 the most versatile and complete RBP binding site prediction method available so far.

bioinformatics↗

bioRxiv2019

StoatyDive: Evaluation and Classification of Peak Profiles for Sequencing Data

The prediction of binding sites (peak calling) is a common task in the data analysis of methods such as crosslinking or chromatin immunoprecipitation in combination with high-throughput sequencing (CLIP-Seq, ChIP-Seq). The predicted binding sites are often further analyzed to predict sequence motifs or structure patterns as an example. However, the obtained peak set can vary in their profile shapes because of the used peakcaller method, different binding domains of the protein, protocol biases, or other factors. Thus, a tool is missing that evaluates and classifies the predicted peaks based on their shapes. We hereby present StoatyDive, a tool that can be used to filter for specific peak profile shapes of sequencing data such as CLIP and ChIP. StoatyDive therefore fine tunes downstream analysis steps such as structure or sequence motif predictions and acts as a quality control.\n\nWith StoatyDive we were able to classify distinct peak profile shapes from CLIP-seq data of the histone stem-loop-binding protein (SLBP). We show the potential of StoatyDive, as a quality control tool and as a filter to pick different shapes based on biological or methodical questions.\n\nStoatyDive is open source and freely available under GLP-3 at https://github.com/BackofenLab/StoatyDive and at bioconda https://anaconda.org/bioconda/stoatydive.

bioinformatics↗