Biology subjects

Schmidt, B.

Publications and source records attributed to Schmidt, B..

At least 19 recordsLinked to original sources

bioRxiv2026

SNPstar: A Web Server Linking Allelic Variation to Protein Structure and Function in Arabidopsis thaliana

Understanding how natural genetic variants affect protein structure and function is central to plant biology. The 1001 Genomes Project has catalogued millions of single nucleotide polymorphisms (SNPs) across more than a thousand Arabidopsis thaliana accessions, offering an unprecedented opportunity to relate sequence variation to three-dimensional protein structure and population context. Yet, realizing it requires tools that integrate these scales in one accessible framework. Here we present SNPstar, a web server that links allelic variation in A. thaliana to AlphaFold3-predicted structures through a gene-centric, interactive interface. SNPstar annotates each variant with descriptive features, thermodynamic stability estimates, protein domain context, and genome-wide association results, and computes haplotypes and proteotypes that group accessions by shared DNA or protein sequence. Researchers can characterize variants, visualize their structural context, map their geographic distribution, and prioritize accessions for experimental validation without local computational infrastructure. We demonstrate SNPstar with two case studies. The first recapitulates known loss-of-function variation in the cadmium transporter HMA3, validating that SNPstar prioritizes functionally consequential alleles. The second uses SNPstar-defined proteotypes to identify an N-terminal SNP combination in ARGONAUTE 2 that distinguishes accessions differing in in vitro siRNA-directed target cleavage, linking protein-coding variation to a measurable molecular phenotype. SNPstar thus helps translate natural variation into mechanistic insight.

plant biology↗

bioRxiv2026

Efficient protein structure prediction fromcompact computers to datacenters withOpenFold-TRT

We introduce accelerations for deep learning inference with OpenFold and TensorRT that, combined with MMseqs2-GPU on an x86 system with one NVIDIA RTX PRO 6000 Blackwell Server Edition GPU, reach up to 131x faster inference compared to AlphaFold2. ARM-optimizations enable homology search on power efficient DGX Spark, and allow search beyond available GPU RAM on NVIDIA Grace Hopper Superchip at comparable overall folding speed. These accelerations enable high-throughput protein structure inference at no accuracy cost using familiar tools.

bioinformatics↗

bioRxiv2026

Systematic functional drug testing in patient-derived models reveals ex vivo sensitivities associated with clinical outcome in rare solid tumors

Rare cancers are individually uncommon but collectively represent a substantial share of cancer burden, with limited systemic treatment options for many entities. Molecular profiling identifies targetable alterations, but actionable findings are limited and responses can vary despite a matched target. This motivates complementary approaches that directly assess tumor drug response. Here, we establish a biopsy-compatible ex vivo drug sensitivity testing platform optimized for low input and reproducibility. Patient-derived material was tested either directly or following ex vivo expansion. Functional profiling was performed within clinically relevant timelines across models from 126 patients with rare advanced solid tumors. Drug responses were consistent between model types. In most samples, we identified at least one potentially active compound, supporting feasibility at biopsy-scale. High in vitro sensitivity was associated with clinical benefit and progression-free survival. These findings support functional drug sensitivity testing as a complementary component in precision oncology for adults with rare cancers. Statement of SignificanceThis study presents a biopsy-compatible drug sensitivity testing platform for phenotype-based therapy stratification in rare cancers. It identifies actionable ex vivo drug responses and shows associations with clinical outcome in patients treated with screened therapies. These findings support functional testing as a complementary additional layer of stratification for therapeutic prioritization.

cancer biology↗

bioRxiv2025

Improved Metagenomic Analysis for All-Food-Sequencing with AFS-MetaCache2: Illumina vs. Nanopore

BackgroundAll-Food-Sequencing (AFS) is a method for untargeted metagenomic analysis that allows for the detection and quantification of food ingredients. While this approach avoids some of the shortcomings of targeted PCR-based methods, its performance depends on sequencing technologies, taxonomic classification tools, and genomic reference databases. ResultsAFS-MetaCache2 implements an improved reference database construction mechanism compared to prior approaches. To demonstrate the effectiveness to AFS, we sequenced sausages composed of mammalian and avian species using both short-read (Illumina) and long-read (Oxford Nanopore Technologies) platforms. While both approaches reliably detect the main components, our comparison shows that long-read sequencing is superior in terms of both quantification accuracy and false positive rates. The evaluation of representative metagenomic tools (Kraken2+Bracken, KrakenUniq, AFS-MetaCache1) demonstrates that AFS-MetaCache2 yields the best accuracy and fastest database build times, while reducing peak main memory consumption. It thus allows for efficient scaling to large reference genome sets. ConclusionOur study suggests that deep sequencing of total genomic DNA from samples with heterogeneous taxon composition, using 3rd generation sequencing technology followed by metagenomic analysis with AFS-MetaCache2, is a valuable approach for bio-surveillance of food ingredients. Our software is available at https://github.com/muellan/metacache.

bioinformatics↗

bioRxiv2025

Accelign: a GPU-based Library for Accelerating Pairwise Sequence Alignment

BackgroundThe continually increasing volume of sequence data results in a growing demand for fast implementations of core algorithms. Computation of pairwise alignments based on dynamic programming is an important part in many bioinformatics pipelines and a major contributor to overall runtime due to the associated quadratic time complexity. This motivates the need for a library of efficient implementations on modern GPUs for a variety of alignment algorithms for different types of sequence data including DNA, RNA, and proteins. ResultsAccelign is a library of accelerated pairwise sequence alignment algorithms for CUDA-enabled GPUs. Its parallelization strategy is based on a common wavefront design that can be adapted to support a variety of dynamic programming algorithms: local, global, and semi-global alignment of genomic and protein sequences with a variety of commonly used scoring schemes supporting one-to-one, one-to-many or all-to-all pairwise sequence alignments. This leads to a peak performance between 16.1 TCUPS and 9.1 TCUPS for computing optimal global alignment scores with linear gaps and affine gap penalties on a single RTX PRO 6000 Blackwell GPU, respectively. In addition, our library demonstrates significant speedups in several real-world case studies over prior CPU-based (SeqAn, Parasail, BSalign) and GPU-based libraries (ADEPT, GASAL2), and can even outperform highly customized algorithms (WFA-GPU, CUDASW++4.0). Furthermore, the performance of our approach scales linearly with the number of employed GPUs, which makes it feasible to exploit multi-GPU nodes for increased processing speeds. ConclusionAccelign provides significant speedups for commonly used pairwise alignment algorithms compared to prior implementations. It is freely available at https://github.com/fkallen/Accelign.

bioinformatics↗

bioRxiv2025

Ubiquitin-dependent signal amplification in lipid saturation sensing

Cellular membranes are dynamic platforms whose composition and biophysical properties are surveyed by sensor proteins to maintain homeostasis. Failure to preserve membrane homeostasis, however, results in cellular stress and organelle dysfunction. Using the prototypical lipid saturation sensor Mga2, we explored how weak physical cues that modulate rotational movements in the transmembrane region are converted into decisive biochemical outputs that ultimately control the production of unsaturated fatty acids. Quantitative in vitro ubiquitylation assays and kinetic modeling reveal vastly distinct rates of Mga2 ubiquitylation controlled by the membrane environment. Mga2 ubiquitylation dominates in tightly packed, saturated membranes, while loosely packed environments favor an inhibitory autoubiquitylation of the cognate E3 ligase Rsp5. This mechanism provides a means of signal amplification, which can function even in the absence of deubiquitylating enzymes. Our findings provide a mechanistic framework for how membrane property sensors convert weak, fluctuating physical cues into robust biochemical outcomes, and put a spotlight on the regulatory potential of E3 ligase autoubiquitylation in cellular surveillance.

biochemistry↗

bioRxiv2025

QutRNA2: Robust tRNA modification discovery from Nanopore direct tRNA sequencing

Transfer RNAs (tRNAs) are essential for protein synthesis and are extensively modified to ensure their structure and function. Direct RNA sequencing with Oxford Nanopore Technologies enables positional modification analysis but is challenged by tRNAs short length, redundancy, and dense modifications. We present QutRNA2, a scalable workflow featuring GPU-accelerated local alignment, statistical filtering, pairwise error profile comparison, and customizable visualization. Achieving up to 25-fold speed gains over CPU methods, QutRNA2 identifies enzyme-dependent modifications in nuclear- and mitochondrial-encoded tRNAs, demonstrated in human and mouse samples. This open-source solution provides a comprehensive, multiplexing-compatible framework for tRNA analysis, addressing a key gap in current tools.

bioinformatics↗

bioRxiv2025

Wound healing and angiogenic profiling of dermal endothelial cells isolated from people with type 2 diabetes.

Impaired wound healing in type 2 diabetes (T2D) is associated with microvascular dysfunction and remains a significant clinical challenge. We aimed to determine whether primary human dermal microvascular endothelial cells (HDMVECs) from individuals with T2D exhibit abnormal cellular functions, and whether exposure to T2D serum impacts healthy endothelial function. In Experiment 1, T2D-HDMVECs displayed paradoxically higher migratory and angiogenic capacities than their healthy counterparts, despite markedly reduced eNOS expression and disrupted endothelial-identity gene expression. In Experiments 2 and 3, healthy HDMVECs showed decreased tube formation, nitric oxide production, and Notch/angiogenesis-related gene expression after exposure to both healthy and T2D serum, suggesting the presence of serum-derived factors that inhibit these pathways. However, T2D-HDMVECs remained largely unresponsive to these serum-driven effects, reinforcing an "intrinsic" reprogramming of T2D endothelial cells. Overall, our data reveal a complex interplay between cell-autonomous alterations and extrinsic signals in diabetic endothelial dysfunction. Therapeutic strategies targeting both intrinsic cellular programs (e.g., eNOS, Notch signaling) and the circulating milieu may represent promising avenues for enhancing wound repair in patients with T2D.

physiology↗

bioRxiv2024

GPU-accelerated homology search with MMseqs2

Rapidly growing protein databases demand faster sensitive sequence similarity detection. We present GPU-accelerated search utilizing intra-query parallelization delivering 6x faster single-protein searches compared to state-of-the-art CPU methods on 2x64 cores--speeds previously requiring large protein batches. It is most cost effective, including in large-batches at 0.45x MMseqs2-CPU speed (8 GPUs delivering 2.4x). It accelerates ColabFold structure prediction 31.8x compared to AlphaFold2 and Foldseek search 4-27x. MMseqs2-GPU is open-source at mmseqs.com.

bioinformatics↗

bioRxiv2024

Partial Fitch Graphs: Characterization, Satisfiability and Complexity

Horizontal gene transfer is an important contributor to evolution. Following Walter M. Fitch, two genes are xenologs if at least one HGT separates them. More formally, the directed Fitch graph has a set of genes as its vertices, and directed edges (x, y) for all pairs of genes x and y for which y has been horizontally transferred at least once since it diverged from the last common ancestor of x and y. Subgraphs of Fitch graphs can be inferred by comparative sequence analysis. In many cases, however, only partial knowledge about the "full" Fitch graph can be obtained. Here, we characterize Fitch-satisfiable graphs that can be extended to a biologically feasible "full" Fitch graph and derive a simple polynomial-time recognition algorithm. We then proceed to show that several versions of finding the Fitch graph with total maximum (confidence) edge-weights are NP-hard. In addition, we provide a greedy-heuristic for "optimally" recovering Fitch graphs from partial ones. Somewhat surprisingly, even if [~] 80% of information of the underlying input Fitch-graph G is lost (i.e., the partial Fitch graph contains only [~] 20% of the edges of G), it is possible to recover [~] 90% of the original edges of G on average.

bioinformatics↗

bioRxiv2023

CUDASW++4.0: Ultra-fast GPU-based Smith-Waterman Protein Sequence Database Search

BackgroundThe maximal sensitivity for local pairwise alignment makes the Smith-Waterman algorithm a popular choice for protein sequence database search. However, its quadratic time complexity makes it compute-intensive. Unfortunately, current state-of-the-art software tools are not able to leverage the massively parallel processing capabilities of modern GPUs with close-to-peak performance. This motivates the need for more efficient implementations. ResultsCUDASW++4.0 is a fast software tool for scanning protein sequence databases with the Smith-Waterman algorithm on CUDA-enabled GPUs. Our approach achieves high efficiency for dynamic programming-based alignment computation by minimizing memory accesses and instructions. We provide both efficient matrix tiling, and sequence database partitioning schemes, and exploit next generation floating point arithmetic and novel DPX instructions. This leads to close-to-peak performance on modern GPU generations (Ampere, Ada, Hopper) with throughput rates of up to 1.94 TCUPS, 5.01 TCUPS, 5.71 TCUPS on an A100, L40S, and H100, respectively. Evaluation on the Swiss-Prot, UniRef50, and TrEMBL databases shows that CUDASW++4.0 gains over an order-of-magnitude performance improvements over previous GPU-based approaches (CUDASW++3.0, ADEPT). In addition, our algorithm demonstrates significant speedups over top-performing CPU-based tools (BLASTP, SWIPE, SWIMM2.0), can exploit multi-GPU nodes with linear scaling, and features an impressive energy efficiency of up to 15.7 GCUPS/Watt. ConclusionCUDASW++4.0 changes the standing of GPUs in protein sequence database search with Smith-Waterman alignment by providing close-to-peak performance on modern GPUs. It is freely available at https://github.com/asbschmidt/CUDASW4.

bioinformatics↗

bioRxiv2023

TALLSorts: a T-cell acute lymphoblastic leukaemia subtype classifier using RNA-seq expression data

T-cell acute lymphoblastic leukaemia (T-ALL) is an aggressive and heterogenous haematological malignancy affecting both children and adults. T-ALL subtype identification is an emerging area of active research, with several recent studies proposing potential subtypes based on transcriptomic and genomic analyses. Here we present TALLSorts, a machine-learning bioinformatic tool which classifies T-ALL samples by using bulk RNA sequencing (RNA-seq) data. Trained on four international cohorts totalling 264 samples, TALLSorts exhibits excellent accuracy when tested on holdout and independent test sets. TALLSorts is publicly available for use and will be constantly updated as the field of T-ALL classification further develops.

bioinformatics↗

bioRxiv2023

IgG4 serum levels are not elevated in cases of Post-COVID syndrome

Recently, unexpectedly high virus-specific IgG4 levels were reported after more than two mRNA vaccinations. Class switch towards IgG4 occurs after long-term antigen exposure, downregulates immune responses and is associated with several autoimmune diseases. Here, we examined differences in antigen-specific IgG subtypes in serum samples from 64 Post-COVID patients and an equally sized cohort of convalescent controls. In both cohorts, the relative amounts of spike protein-specific IgG subtypes were comparable. IgG1 was the most frequent, followed by IgG3, IgG2, and IgG4. A difference between cohorts was observed only for IgG2, which was significantly lower in the Post-COVID cohort. Further analysis of the reactive IgG4 revealed a small but significant difference for the spike protein receptor-binding domain but not for the spike ectodomain. Since the total IgG4 levels are very low, we do not expect a biologically relevant role in Post-COVID syndrome. However, reduced virus-specific IgG2 levels could contribute to the persistence of SARS-CoV-2, causing chronic inflammation in the setting of Post-COVID syndrome.

immunology↗

bioRxiv2023

RabbitVar: ultra-fast and accurate somatic small-variant calling on multi-core architectures

The continuous development of next-generation sequencing (NGS) technology has led to extensive and frequent use of genomic analysis in cancer research. The associated production of large-scale NGS datasets establishes the need for high-precision somatic variant calling methods that are highly optimized on commonly used hardware platforms. We present RabbitVar (https://github.com/LeiHaoa/RabbitVar), a scalable variant caller that can detect small somatic variants from paired tumor/normal NGS data on modern multi-core CPUs. Our approach combines candidate-finding and machine-learning-based filtering strategies with optimized data structures and multi-threading to achieve both high accuracy and efficiency. We have compared the performance of RabbitVar to leading state-of-the-art callers (Strelka2, Mutect2, NeuSomatic, VarDict, VarScan2) on real-world HCC1395 breast cancer datasets under different sequencing conditions and contamination rates. The evaluation results demonstrate that RabbitVar achieves highly competitive F1-scores when calling SNVs. Moreover, when calling the more challenging indel variants, it consistently achieves the highest F1-scores. RabbitVar is able to process a paired tumor and normal whole human genome sequencing datasets with 80x depth in less than 20 minutes on a 48-core workstation outperforming all other tested variant callers in terms of efficiency.

bioinformatics↗

bioRxiv2022

RabbitTClust: enabling fast clustering analysis of millions bacteria genomes with MinHash sketches

We present RabbitTClust, a fast and memory-efficient genome clustering tool based on sketch-based distance estimation. Our approach enables efficient processing of large-scale datasets by combining dimensionality reduction techniques with streaming and parallelization on modern multi-core platforms. 113,674 complete bacterial genome sequences (RefSeq: 455 GB in FASTA format) can be clustered within less than 6 minutes and 1,009,738 GenBank assembled bacterial genomes (4.0 TB in FASTA format) within only 34 minutes on a 128-core workstation. Our results further identify 1,269 repetitive genomes (identical nucleotide content) in RefSeq bacterial genomes.

bioinformatics↗

bioRxiv2021

Sunk cost sensitivity in mice, rats, and humans on the Restaurant Row and WebSurf tasks cannot be explained by attrition biases alone

In a recent bioRxiv preprint, Ott et al. argue that sensitivities to sunk costs that have been reported in two serial foraging tasks (the Restaurant Row task in mice and rats, and the Web-Surf task in humans) may be due to simple consequences of the way that subjects perform these tasks and not due to an actual sensitivity to sunk costs. However, several variants of these tasks have been studied, in which the sensitivity to sunk costs changes. In order to test the Ott et al. model against these experimental observations, we simulated the model under these additional experimental conditions. We find that it is incompatible with the actual data. While we applaud the simplicity of the Ott et al. model, we must reject it as an explanation for the observed sensitivity to sunk costs seen in these tasks. We thus conclude that the alternative explanation - that mice, rats, and humans are sensitive to actual sunk costs in these tasks - is a better explanation for the data.

neuroscience↗

bioRxiv2021

Early post-infection treatment of SARS-CoV-2 infected macaques with human convalescent plasma with high neutralizing activity reduces lung inflammation

Early in the SARS-CoV-2 pandemic, there was a high level of optimism based on observational studies and small controlled trials that treating hospitalized patients with convalescent plasma from COVID-19 survivors (CCP) would be an important immunotherapy. However, as more data from controlled trials became available, the results became disappointing, with at best moderate evidence of efficacy when CCP with high titers of neutralizing antibodies was used early in infection. To better understand the potential therapeutic efficacy of CCP, and to further validate SARS-CoV-2 infection of macaques as a reliable animal model for testing such strategies, we inoculated 12 adult rhesus macaques with SARS-CoV-2 by intratracheal and intranasal routes. One day later, 8 animals were infused with pooled human CCP with a high titer of neutralizing antibodies (RVPN NT50 value of 3,003), while 4 control animals received normal human plasma. Animals were monitored for 7 days. Animals treated with CCP had detectable levels of antiviral antibodies after infusion. In comparison to the control animals, they had similar levels of virus replication in the upper and lower respiratory tract, but had significantly reduced interstitial pneumonia, as measured by comprehensive lung histology. By highlighting strengths and weaknesses, data of this study can help to further optimize nonhuman primate models to provide proof-of-concept of intervention strategies, and guide the future use of convalescent plasma against SARS-CoV-2 and potentially other newly emerging respiratory viruses. Author summaryThe results of treating SARS-CoV-2 infected hospitalized patients with COVID-19 convalescent plasma (CCP), collected from survivors of natural infection, have been disappointing. The available data from various studies indicate at best moderate clinical benefits only when CCP with high titer of neutralizing antibodies was infused early in infection. The macaque model of SARS-CoV-2 infection can be useful to gain further insights in the value of CCP therapy. In this study, animals were infected with SARS-CoV-2 and the next day, were infused with pooled human convalescent plasma, selected to have a very high titer of neutralizing antibodies. While administration of CCP did not result in a detectable reduction in virus replication in the respiratory tract, it significantly reduced lung inflammation. These data, combined with the results of monoclonal antibody studies, emphasize the need to use products with high titers of neutralizing antibodies, and guide the future development of CCP-based therapies.

immunology↗

bioRxiv2021

Locality-sensitive hashing enables signal classification in high-throughput mass spectrometry raw data at scale

Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: First, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Existing approaches for signal detection are usually not well suited for processing large amounts of data in parallel or rely on strong assumptions concerning the signals properties. In this study, it is shown that locality-sensitive hashing enables signal classification in mass spectrometry raw data at scale. Through appropriate choice of algorithm parameters it is possible to balance false-positive and false-negative rates. On synthetic data, a superior performance compared to an intensity thresholding approach was achieved. The implementation scaled out up to 88 threads on real data. Locality-sensitive hashing is a desirable approach for signal classification in mass spectrometry raw data. Generated data and code are available at https://github.com/hildebrandtlab/mzBucket. Raw data is available at https://zenodo.org/record/5036526.

bioinformatics↗