Biology subjects

Grosse, I.

Publications and source records attributed to Grosse, I..

14 recordsLinked to original sources

bioRxiv2026

EPIC: An open community challenge for sequence-based prediction of transcription initiation in five non-model metazoans

Predicting gene expression from DNA sequence is a central problem with critical biological and clinical implications. Recent sequence-to-function models are reported to achieve improved performance, yet it remains unclear how much of what they learn reflects genuine principles of eukaryotic transcription initiation and to what extent they are able to generalize beyond humans and other primary model species. A fair and blind benchmark has likewise been missing. Here we introduce EPIC, the Eukaryotic Promoter and transcription Initiation prediction Challenge. Teams receive strand-specific, single-nucleotide-resolution initiation profiles for 80-95% of the genome and are asked to predict the remainder from DNA sequence alone. To level the field, the challenge relies on understudied animals spanning three phyla: octopus, oyster, milkweed bug, Indian meal moth, and shark. EPIC is open to everyone and closes on December 31, 2026. All teams clearing the dinucleotide precision baseline are invited to join the consortium authorship of the post-challenge publication, and winners are invited for personal authorship.

bioinformatics↗

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

Fungal community composition links rhizosphere microbiome organization to plant phenotype in response to moderate warming

BackgroundGlobal warming increasingly challenges plant performance and ecosystem function, and plant responses to elevated temperature are shaped not only by intrinsic plasticity, but also by interactions with rhizosphere microbial communities. However, it remains unclear how warming-induced microbiome reorganization relates to plant performance, whether microbiome community composition predicts plant phenotypic responses to elevated temperature better than simple host-microbiome origin matching, and which bacterial or fungal community features contribute most strongly to this prediction. ResultsWe implemented a full factorial design manipulating plant genotype (P), microbial inoculum (M), and temperature (T) using natural Arabidopsis thaliana ecotypes and their corresponding rhizosphere microbiomes from contrasting climatic regions. By integrating high-throughput plant phenotyping, microbial community profiling, microbiome-phenotype coupling analyses, and predictive modeling, we found that elevated temperature induced a coherent thermomorphogenic shift in plant architecture, while plant phenotypic variation remained determined primarily by genotype over the 14-day vegetative growth period covered in this study. Overall, cold-origin genotypes showed larger temperature-induced trait shifts than warm-origin genotypes. Rhizosphere microbial communities were structured predominantly by inoculum origin, but were also affected by warming. Evidence for a increased thermomorphogenic growth when genotypes were combined with inoculum from their home site was limited. Instead, microbiome-phenotype associations were better captured by community composition than by matching status, with fungal community variation showing stronger and more consistent associations with plant phenotype than bacterial variation and providing more robust predictions of plant phenotypes across models. ConclusionsWarming reorganized plant-rhizosphere systems within persistent host- and inoculum-associated baselines. Plant phenotypic variation during vegetative development under elevated temperature was linked more closely to microbiome composition, especially in fungi, than to simple host-microbiome matching of origins. These findings provide a framework for identifying microbiome features associated with plant performance under climate warming.

plant biology↗

bioRxiv2025

Inferring binding specificities of human transcription factors with the wisdom of crowds

DNA motif discovery and, particularly, computational modeling of transcription factor binding motifs, has been a mecca of algorithmic bioinformatics for several decades. Here, we report the results of the largest open community challenge in Inferring BInding Specificities (IBIS), where participants all over the world were invited to construct binding specificity models from multi-assay experimental data for poorly studied human transcription factors. The submissions were rigorously tested against a rich held-out dataset. Benchmarking demonstrated a consistent advantage of properly designed deep learning models over traditional positional weight matrices and other machine learning methods. Yet, the positional weight matrices displayed a surprisingly strong performance out of the box, being only slightly behind the best deep learning models. A post-challenge assessment of a selection of other deep learning methods further solidified this finding. IBIS highlights the power of benchmarking in finding adequate DNA motif representations, emphasizes the pros and cons of various machine learning methods applied to DNA motif modeling, and establishes a rich dataset, benchmarking protocols, and computational framework for a fair cross-platform evaluation of future models of transcription factor binding motifs in DNA sequences. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/688692v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@1c6677corg.highwire.dtl.DTLVardef@b4124aorg.highwire.dtl.DTLVardef@1ce2b1org.highwire.dtl.DTLVardef@66e917_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Fast barcode calling based on k-mer distances

DNA barcodes, which are short DNA strings, are regularly used as tags in pooled sequencing experiments to enable the identification of reads originating from the same sample. A crucial task in the subsequent analysis of pooled sequences is barcode calling, where one must identify the corresponding barcode for each read. This task is computationally challenging when the probability of synthesis and sequencing errors is high, like in photolithographic microarray synthesis. Identifying the most similar barcode for each read is a theoretically attractive solution for barcode calling. However, an all-to-all exact similarity calculation is practically infeasible for applications with millions of barcodes and billions of reads. Hence, several computational approaches for barcode calling have been proposed, but the challenge of developing an efficient and precise computational approach remains. Here, we propose a simple, yet highly effective new barcode calling approach that uses a filtering technique based on precomputed k-mer lists. We find that this approach has a slightly higher accuracy than the state-of-the-art approach, is more than 500 times faster than that, and allows barcode calling for one million barcodes and one billion reads per day on a server GPU.

bioinformatics↗

bioRxiv2024

Cross-platform DNA motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors

A DNA sequence pattern, or "motif", is an essential representation of DNA-binding specificity of a transcription factor (TF). Any particular motif model has potential flaws due to shortcomings of the underlying experimental data and computational motif discovery algorithm. As a part of the Codebook/GRECO-BIT initiative, here we evaluated at large scale the cross-platform recognition performance of positional weight matrices (PWMs), which remain popular motif models in many practical applications. We applied ten different DNA motif discovery tools to generate PWMs from the "Codebook" data comprised of 4,237 experiments from five different platforms profiling the DNA-binding specificity of 394 human proteins, focusing on understudied transcription factors of different structural families. For many of the proteins, there was no prior knowledge of a genuine motif. By benchmarking-supported human curation, we constructed an approved subset of experiments comprising about 30% of all experiments and 50% of tested TFs which displayed consistent motifs across platforms and replicates. We present the Codebook Motif Explorer (https://mex.autosome.org), a detailed online catalog of DNA motifs, including the top-ranked PWMs, and the underlying source and benchmarking data. We demonstrate that in the case of high-quality experimental data, most of the popular motif discovery tools detect valid motifs and generate PWMs, which perform well both on genomic and synthetic data. Yet, for each of the algorithms, there were problematic combinations of proteins and platforms, and the basic motif properties such as nucleotide composition and information content offered little help in detecting such pitfalls. By combining multiple PMWs in decision trees, we demonstrate how our setup can be readily adapted to train and test binding specificity models more complex than PWMs. Overall, our study provides a rich motif catalog as a solid baseline for advanced models and highlights the power of the multi-platform multi-tool approach for reliable mapping of DNA binding specificities. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/619379v2_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@79561forg.highwire.dtl.DTLVardef@54c0aorg.highwire.dtl.DTLVardef@1c33f34org.highwire.dtl.DTLVardef@16a93ba_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

bioinformatics↗

bioRxiv2024

Perspectives on Codebook: sequence specificity of uncharacterized human transcription factors

Gene expression is regulated by transcription factors (TFs), which recognize specific DNA sequence motifs. Several hundred putative human TFs, identified mainly by an apparent DNA-binding domain, lack known binding motifs1, and even for well-characterized TFs, it remains controversial to what degree motifs accurately reflect binding sites in living cells2,3. Here, we describe a systematic effort ("Codebook") to determine the sequence specificity of 332 putative and poorly characterized human TFs. Over 4,000 independent experiments, encompassing multiple in vitro and in vivo assays, produced motifs for just over half (177, or 53%), of which most are unique to a single protein, thereby extending the vocabulary of sequence recognition encoded by human TFs by [~]100 distinct motifs. Moreover, binding motifs identified in vitro are strongly enriched within cellular binding sites. Collectively, the data reveal tens of thousands of previously unknown, conserved, and direct TF binding sites across the human genome. These sites are concentrated in promoter regions, and are predictive of gene expression, illustrating that this new data atlas provides an important step forward in decoding the human genome.

genomics↗

bioRxiv2024

Comprehensive annotations of alternative splicing, lncRNAs, and circRNAs in seven species of flowering plants

The successful occupation of terrestrial habitats by early plants was catalyzed by the adaptive evolution of new organs able to supply water and compensate for the gravitational impact under changing land conditions. Because plants are sessile, it has been proposed that the capacity of plants to innovate specialized organs is driven by a complex interplay between developmental gene expression and regulatory RNAs such as lncRNAs and circRNAs, evolving through sequence polymorphisms, genomic rearrangements, and expression divergence. Despite the importance of alternative and noncoding transcripts for enabling plant life, their accurate quantification causes major challenges as many of them are expressed at low levels and tissue-specific. Here, we describe the re-annotation of seven land plant genomes based on deep, organ-specific, ribo-depleted developmental RNA-Seq data. Using this comparative resource, we uncover 5,000 new lncRNAs and 2,000 new circular RNAs that are promising candidates for further functional investigation. Our annotation will become a reference catalog for studies on plant organ evolution and for uncovering tissue-specific patterns of transcript emergence.

plant biology↗

bioRxiv2024

Rapid evolution of gene expression patterns in flowering plants

Phenotypic differences between species are largely driven by changes in both protein-coding sequence and gene expression 1. The evolutionary history of angiosperms (flowering plants) is characterised by a highly accelerated rate of diversification, which Darwin referred to as an "abominable mystery" 2. Here we show, by analysing the transcriptomes from eight organs across seven species, that angiosperm protein-coding gene expression patterns evolve rapidly: within 45 million years, expression levels of orthologous genes diverged so strongly that they are more similar between different organs within a species than between what are considered homologous organs from different species. This finding differs from previous observations in mammals, which demonstrated that organ-dependent gene expression levels are largely conserved 3, 4, 5. Among the angiosperm organs, meristems and leaves show the highest degree of expression conservation, whereas stamen and pollen transcriptomes diverge rapidly. Examining changes in the expression level of functionally related genes, we found low rates for those involved in key cellular, metabolic and developmental processes. In contrast, particularly high rates were observed for genes that are involved in the response to endogenous and external stimuli, presumably reflecting an adaptive response of flowering plants to ever-changing environments. Our work reveals that the evolution of gene expression progresses at different rates in angiosperms and mammals, and provides a comprehensive resource to perform cross-kingdom comparative studies of transcriptome evolution.

plant biology↗

bioRxiv2020

epiGBS2: an improved protocol and automated snakemake workflow for highly multiplexed reduced representation bisulfite sequencing

epiGBS is an existing reduced representation bisulfite sequencing method to determine cytosine methylation and genetic polymorphisms de novo. Here, we present epiGBS2, an improved epiGBS laboratory protocol and user-friendly bioinformatics pipeline for a wide range of species with or without reference genome. epiGBS2 decreases costs and time investment and increases user-friendliness and reproducibility. The library protocol was adjusted to allow for a flexible choice of restriction enzymes and a double digest. Instead of fully methylated adapters, semi-methylated adapters are now used. The bioinformatics pipeline was improved in speed and integrated in the snakemake workflow management system, which now makes the pipeline easy to execute, modular, and parameter settings flexible. We also provide a detailed description of the laboratory protocol, an extensive manual of the bioinformatics pipeline, which is publicly accessible on github (https://github.com/nioo-knaw/epiGBS2) and zenodo (https://doi.org/10.5281/zenodo.3819996), and example output.

bioinformatics↗

bioRxiv2019

kASA: Taxonomic Analysis of Metagenomic Data on a Notebook

The taxonomic analysis of sequencing data has become important in many areas of life sciences. However, currently available software tools for that purpose either consume large amounts of RAM or yield an insufficient quality of the results. Here we present kASA, a k-mer based software capable of identifying and profiling metagenomic DNA sequences with high computational efficiency and a user-definable memory footprint. We ensure both high sensitivity and precision by using an amino acid-like encoding of k-mers with a dynamic length of multiple ks. Custom algorithms and data structures optimized for external memory storage enable for the first time a full-scale metagenomic analysis without compromise on a standard notebook.

bioinformatics↗

bioRxiv2017

Gene expression dynamics and inter-tissue recognition mechanisms during tissue fusion at the Arabidopsis graft junction

The ability for cut tissues to join together and form a chimeric organism is a remarkable property of many plants, however, grafting is poorly characterized at the molecular level. To better understand this process we monitored genome-wide temporal and spatial gene expression changes in grafted Arabidopsis thaliana hypocotyls. Tissues above and below the graft rapidly developed an asymmetry such that many genes were more highly expressed on one side than the other. This asymmetry correlated with sugar responsive genes and we observed an accumulation of starch above the graft that decreased along with asymmetry once the sugar-transporting vascular tissues reconnected. Despite the initial starvation response below the graft, many genes associated with vascular formation were rapidly activated in grafted tissues but not in cut and separated tissues indicating that a recognition mechanism activated that was independent of functional vascular connections. Auxin which is transported cell-to-cell, had a rapidly elevated response that was symmetric, suggesting that auxin was perceived by the root within hours of tissue attachment to activate the vascular regeneration process. A subset of genes were expressed only in grafted tissues, indicating that wound healing proceeded via different mechanisms depending on the presence or absence of adjoining tissues. Such a recognition process could have broader relevance for tissue regeneration, inter-tissue communication and tissue fusion events.

plant biology↗

bioRxiv2017

Ecological plant epigenetics: Evidence from model and non-model species, and the way forward

Growing evidence makes a strong case that epigenetic mechanisms contribute to complex traits, with implications across many fields of biology from dissecting developmental processes to understanding aspects of human health and disease. In ecology, recent studies have merged ecological experimental design with epigenetic analyses to elucidate the contribution of epigenetics to plant phenotypes, stress response, adaptation to habitat, or species range distributions. While there has been some progress in revealing the role of epigenetics in ecological processes, many studies with non-model species have so far been limited to describing broad patterns based on anonymous markers of DNA methylation. In contrast, studies with model species have benefited from powerful genomic resources, which allow for a more mechanistic understanding but have limited ecological realism. To understand the true significance of epigenetics for plant ecology and evolution, we must combine both approaches transferring knowledge and methods from model-species research to genomes of evolutionarily divergent species, and examining responses to complex natural environments at a more mechanistic level. This requires transforming genomics tools specifically for studying non-model species, which is challenging given the large and often polyploid genomes of plants. Collaboration between molecular epigeneticists, ecologists and bioinformaticians promises to enhance our understanding of the mutual links between genome function and ecological processes.

ecology↗

bioRxiv2017

Young Genes To The Front -- A Strategy For Future Resistance Against Powdery Mildew?

Nonhost resistance of a plant against a microbial pathogen can be the result of a long-lasting coevolutionary optimization of resource allocation in both host and pathogen. Although this has been suggested for years, coevolutionary aspects leading to nonhost resistance in plants are not fully understood yet. Instead, most studies focus on limited subsets of genes which are differentially expressed in infected plants to describe details of defense strategies and symptoms of diseases.\n\nHere, we exploit publicly available whole genome gene expression data and combine them with evolutionary characteristics of genes to uncover a mechanism of host-pathogen coevolution. Our results suggest that metabolic efficiency in gene regulation is a key aspect leading to nonhost resistance. In addition, we find that progressing host-pathogen coevolution is accompanied by subtle, but systematic overexpression of recently founded genes. In support of our plant-specific data, we observe similar effects in animal species.

evolutionary biology↗