Biology subjects

Schuster, J.

Publications and source records attributed to Schuster, J..

12 recordsLinked to original sources

bioRxiv2025

Powerful read processing with matchbox

The wide variety of protocols and applications for DNA and RNA sequencing makes flexible tools for read processing an important step in sequence analysis. Beyond trimming and demultiplexing, custom read-level processing is commonly required for data exploration, QC and analysis. Existing tools are often task-specific and dont generalise to new bioinformatic problems. Thus, there is a need for a tool flexible enough to handle the full variety of read processing tasks, and fast and scalable enough to retain high performance on growing sequencing datasets. We introduce matchbox, a read processor that enables fluent manipulation and analysis of FASTA/FASTQ/SAM/BAM files. With a lightweight scripting language designed around error-tolerant pattern-matching, users can write their own matchbox scripts to tackle a wide variety of bioinformatic problems, and incorporate them into existing pipelines and work-flows. We demonstrate matchboxs versatility in a number of contexts: demultiplexing long-read scRNA-seq data with 10X or SPLiT-seq barcodes; restranding RNA-seq reads; assessing CRISPR editing efficiency; and haplotyping macrosatel-lite repeat regions. matchbox achieves a computational performance comparable to existing tools, while addressing a broader range of bioinformatic needs, representing a new state-of-the-art in sequence processing. matchbox is implemented in Rust and available open-source at https://github.com/jakob-schuster/matchbox.

bioinformatics↗

bioRxiv2025

alpseq: an open-source workflow to turbocharge nanobody discovery with high-throughput sequencing

Nanobodies have emerged as promising tools for many biotechnological applications due to their small size, high stability, and remarkable binding specificity. Next-Generation Sequencing (NGS) enables deep profiling of large nanobody libraries and panning campaigns, however the scale and diversity of nanobody NGS datasets presents a significant bioinformatic challenge. To this end, we have developed alpseq, an optimised, open-source software pipeline designed specifically for the efficient and accurate processing of NGS data from nanobody libraries and panning campaigns. alpseq is also paired with a PCR-free sequencing library preparation protocol to allow researchers to easily generate their own data while avoiding biases. The alpseq software pipeline is composed of two parts: a pre-processing module written in Nextflow efficiently handles raw nanobody reads in a single line of code. These results are then fed into the analysis module, which contains a comprehensive suite of functions for quality control, diversity analysis, identification of enriched sequences and clustering. alpseq also creates a user-friendly interactive report which empowers scientists to explore their data without the need for extensive bioinformatic experience. Sophisticated panning campaign designs are supported, such as replicates and comparisons between different pans to find cross-binding leads. alpseq thus generates insights into the nanobody selection process and delivers a list of lead candidates for further experimental validation and downstream applications. alspeq is available at https://github.com/kzeglinski/alpseq.

bioinformatics↗

bioRxiv2025

Igniting full-length isoform analysis in single-cell and spatial RNA-seq data with FLAMESv2

Long-read single-cell RNA-sequencing enables the profiling of RNA isoform expression and alternative splicing at single cell resolution. However, diverse single-cell technologies and sparse isoform data demand flexible and accurate analysis tools. We introduce FLAMESv2, a highly modular and protocol-agnostic R/Bioconductor package for long-read single-cell RNA-seq data analysis. FLAMESv2 supports a wide range of single-cell and spatial protocols, is highly configurable, scales to allow multi-sample analysis and provides versatile visualisation and analysis outputs. We demonstrate its compatibility with both droplet-based and combinatorial barcoding single-cell methods, as well as spatial transcriptomics workflows. Benchmarking confirms FLAMESv2 achieves field-leading performance across key analysis tasks. Applying FLAMESv2 to in vitro differentiation of stem cells into neurons, we identify cell-types, differentiation trajectories, expression of annotated and novel isoforms and isoform expression diversity and heterogeneity within individual cells. FLAMESv2 provides a comprehensive, flexible approach to analysing long-read single-cell RNA-sequencing, unlocking this powerful methodology for RNA isoform characterisation.

bioinformatics↗

bioRxiv2024

Return of intracranial beta oscillations and traveling waves with recovery from traumatic brain injury

Traumatic brain injury (TBI) remains a pervasive clinical problem associated with significant morbidity and mortality. However, TBI remains clinically and biophysically ill-defined, and prognosis remains difficult even with the standardization of clinical guidelines and advent of multimodality monitoring. Here we leverage a unique data set from TBI patients implanted with either intracranial strip electrodes during craniotomy or quad-lumen intracranial bolts with depth electrodes as part of routine clinical practice. By extracting spectral profiles of this data, we found that the presence of narrow-band oscillatory activity in the beta band (12-30 Hz) closely corresponds with the neurological exam as quantified with the standard Glasgow Coma Scale (GCS). Further, beta oscillations were distributed over the cortical surface as traveling waves, and the evolution of these waves corresponded to recovery from coma, consistent with the putative role of waves in perception and cognitive activity. We consequently propose that beta oscillations and traveling waves are potential biomarkers of recovery from TBI. In a broader sense, our findings suggest that emergence from coma results from recovery of thalamo-cortical interactions that coordinate cortical beta rhythms.

neuroscience↗

bioRxiv2024

Variability in drought gene expression datasets highlight the need for community standardization

Physiologically relevant drought stress is difficult to apply consistently, and the heterogeneity in experimental design, growth conditions, and sampling schemes make it challenging to compare water deficit studies in plants. Here, we re-analyzed hundreds of drought gene expression experiments across diverse model and crop species and quantified the variability across studies. We found that drought studies are surprisingly uncomparable, even when accounting for differences in genotype, environment, drought severity, and method of drying. Many studies, including most Arabidopsis work, lack high-quality phenotypic and physiological datasets to accompany gene expression, making it impossible to assess the severity or in some cases the occurrence of water deficit stress events. From these datasets, we developed supervised learning classifiers that can accurately predict if RNA-seq samples have experienced a physiologically relevant drought stress, and suggest this can be used as a quality control for future studies. Together, our analyses highlight the need for more community standardization, and the importance of paired physiology data to quantify stress severity for reproducibility and future data analyses.

plant biology↗

bioRxiv2023

Convergent evolution of desiccation tolerance in grasses

Desiccation tolerance has evolved repeatedly in plants as an adaptation to survive extreme environments. Plants use similar biophysical and cellular mechanisms to survive life without water, but convergence at the molecular, gene, and regulatory levels remains to be tested. Here, we explore the evolutionary mechanisms underlying the recurrent evolution of desiccation tolerance across grasses. We observed substantial convergence in gene duplication and expression patterns associated with desiccation. Syntenic genes of shared origin are activated across species, indicative of parallel evolution. In other cases, similar metabolic pathways are induced, but using different gene sets, pointing towards phenotypic convergence. Species-specific mechanisms supplement these shared core mechanisms, underlining the complexity and diversity of evolutionary adaptations to drought. Our findings provide insight into the evolutionary processes driving desiccation tolerance and highlight the roles of parallel and convergent evolution in response to environmental challenges.

plant biology↗

bioRxiv2023

Epigenetic insights into GABAergic development in Dravet Syndrome iPSC and therapeutic implications

Dravet syndrome (DS) is a devastating early onset refractory epilepsy syndrome caused by variants in the SCN1A gene. A disturbed GABAergic interneuron function is implicated in the progression to DS but the underlying developmental and pathophysiological mechanisms remain elusive, in particularly at the chromatin level. In this study, we utilized induced pluripotent stem cells (iPSCs) derived from DS cases and healthy donors to model disease- associated epigenetic abnormalities of GABAergic development. Employing the ATAC-Seq technique, we assessed chromatin accessibility at multiple time points (Day 0, Day 19, Day 35, and Day 65) of GABAergic differentiation. Additionally, we elucidated the effects of the commonly used anti-seizure drug valproic acid (VPA) on chromatin accessibility in GABAergic cells. The distinct dynamics in chromatin profile of DS iPSC predicted accelerated early GABAergic development, evident at D19, and diverged further from the pattern in control iPSC with continued differentiation, indicating a disrupted GABAergic maturation. Exposure to VPA at D65 reshaped the chromatin landscape at a variable extent in different iPSC-lines and rescued the observed dysfunctional development in some DS iPSC-GABA. This study provides the first comprehensive investigation on the chromatin landscape of GABAergic differentiation in DS-patient iPSC, offering valuable insights into the epigenetic dysregulations associated with interneuronal dysfunction in DS. Moreover, our detailed analysis of the chromatin changes induced by VPA in iPSC-GABA holds the potential to improve development of personalized and targeted anti-epileptic therapies.

cell biology↗

bioRxiv2023

Restrander: rapid orientation and QC of long-read cDNA data

In transcriptomic analyses, it is helpful to keep track of the strand of the RNA molecules. However, the Oxford Nanopore long-read cDNA sequencing protocols generate reads that correspond to either the first or second-strand cDNA, therefore the strandedness of the initial transcript has to be inferred bioinformatically. Reverse transcription and PCR can also introduce artefacts which should be flagged in data pre-processing. Here we introduce Restrander, a lightning-fast and highly accurate tool for restranding and quality checking long-read cDNA sequencing data. Thanks to its C++ implementation, Restrander was faster than Oxford Nanopore Technologies existing tool Pychopper, and correctly restranded more reads due to its strategy of searching for polyA/T tails in addition to primer sequences from the reverse transcription and template-switch steps. We found that restranding improved the process of visualising and exploring data, and increased the number of novel isoforms discovered by bambu, particularly in regions where sense and antisense transcripts co-occur. The artefact detection implemented in Restrander quantifies reads which do not have the correct 5 and 3 ends, a feature which is useful in quality control for library preparation. Restrander is pre-configured for all major cDNA protocols, and can be customised with user-defined primers. Restrander is available at https://github.com/jakob-schuster/restrander

bioinformatics↗

bioRxiv2021

Conformational clamping by a membrane ligand activates the EphA2 receptor

The EphA2 receptor is a promising drug target for cancer treatment, since EphA2 activation can inhibit metastasis and tumor progression. It has been recently described that the TYPE7 peptide activates EphA2 using a novel mechanism that involves binding to the single transmembrane domain of the receptor. TYPE7 is a conditional transmembrane (TM) ligand, which only inserts into membranes at neutral pH in the presence of the TM region of EphA2. However, how membrane interactions can activate EphA2 is not known. We systematically altered the sequence of TYPE7 to identify the binding motif used to activate EphA2. With the resulting six peptides, we performed biophysical and cell migration assays that identified a new potent peptide variant. We also performed a mutational screen that determined the helical interface that mediates dimerization of the TM domain of EphA2 in cells. These results, together with molecular dynamic simulations, allowed to elucidate the molecular mechanism that TYPE7 uses to activate EphA2, where the membrane peptide acts as a molecular clamp that wraps around the TM dimer of the receptor. We propose that this binding mode stabilizes the active conformation of EphA2. Our data, additionally, provide clues into the properties that TM ligands need to have in order to achieve activation of membrane receptors.

biophysics↗

bioRxiv2019

Combined mRNA and protein single cell analysis in a dynamic cellular system using SPARC

Combined measurements of mRNA and protein expression in single cells enables in-depth analysis of cellular states. We present single-cell protein and RNA co-profiling (SPARC), an approach to simultaneously measure global mRNA and large sets of intracellular protein in individual cells. Using SPARC, we show that mRNA expression fails to accurately reflect protein abundance at the time of measurement in human embryonic stem cells, although the direction of changes of mRNA and protein expression are in agreement during cellular differentiation. Moreover, protein levels of transcription factors better predict their downstream effects than do the corresponding transcripts. We further show that changes of the balance between protein and mRNA expression levels can be applied to infer expression kinetic trajectories, revealing future states of individual cells. Finally, we highlight that mRNA expression may be more varied among cells than levels of the corresponding proteins. Overall, our results demonstrate that mRNA and protein measurements in single cells provide different and complementary information regarding cell states. Accordingly, SPARC can offer valuable insights in gene expression programs of single cells.

cell biology↗

bioRxiv2019

VIVA (VIsualization of VAriants): A VCF file visualization tool

The volume and pace of data accumulation from high-throughput sequencing studies have been amplified by recent rapid technological advances in biological sciences. Visualization of genomic data is essential for quality control, exploration, and interpretation. Here, we describe a user-friendly visualization tool for variant call format (VCF) files with which the users can interactively evaluate and share genomic data, as well as create publication quality graphics.

bioinformatics↗

bioRxiv2019

Proteinarium: Multi-Sample Protein-Protein Interaction Visualization and Analysis Tool

BackgroundData analysis has become crucial in the post genomic era where the accumulation of genomic information is mounting exponentially. Analyzing protein-protein interactions in the context of the interactome is a powerful approach to understanding disease phenotypes. ResultsWe describe Proteinarium, a multi-sample protein-protein interaction network analysis and visualization tool. Proteinarium can be used to analyze data for samples with dichotomous phenotypes, multiple samples from a single phenotype or a single sample. Then, by similarity clustering, the network-based relations of samples are identified and clusters of related samples are presented as a dendrogram. Each branch of the dendrogram is built based on network similarities of the samples. The protein-protein interaction networks can be analyzed and visualized on any branch of the dendrogram. Proteinariums input can be derived from transcriptome analysis, whole exome sequencing data or any high-throughput screening approach. Its strength lies in use of gene lists for each sample as a distinct input which are further analyzed through protein interaction analyses. Proteinarium output includes the gene lists of visualized networks and PPI interaction files where users can analyze the network(s) on other platforms such as Cytoscape. In addition, since the dendrogram is written in Newick tree format, users can visualize it in other software platforms like Dendroscope, ITOL. ConclusionsProteinarium, through the analysis and visualization of PPI networks, allows researchers to make important observations on high throughput data for a variety of research questions. Proteinarium identifies significant clusters of patients based on their shared network similarity for the disease of interest and the associated genes. Proteinarium is a command-line tool written in Java with no external dependencies and it is freely available at https://github.com/Armanious/Proteinarium.

bioinformatics↗