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Publikation

Gago-Zachert, S.; Schuck, J.; Weinholdt, C.; Knoblich, M.; Pantaleo, V.; Grosse, I.; Gursinsky, T.; Behrens, S.-E.; Highly efficacious antiviral protection of plants by small interfering RNAs identified in vitro Nucleic Acids Res. 47, 9343-9357, (2019) DOI: 10.1093/nar/gkz678

In response to a viral infection, the plant’s RNA silencing machinery processes viral RNAs into a huge number of small interfering RNAs (siRNAs). However, a very few of these siRNAs actually interfere with viral replication. A reliable approach to identify these immunologically effective siRNAs (esiRNAs) and to define the characteristics underlying their activity has not been available so far. Here, we develop a novel screening approach that enables a rapid functional identification of antiviral esiRNAs. Tests on the efficacy of such identified esiRNAs of a model virus achieved a virtual full protection of plants against a massive subsequent infection in transient applications. We find that the functionality of esiRNAs depends crucially on two properties: the binding affinity to Argonaute proteins and the ability to access the target RNA. The ability to rapidly identify functional esiRNAs could be of great benefit for all RNA silencing-based plant protection measures against viruses and other pathogens.
Publikation

Nettling, M.; Treutler, H.; Cerquides, J.; Grosse, I.; Unrealistic phylogenetic trees may improve phylogenetic footprinting Bioinformatics 33, 1639-1646, (2017) DOI: 10.1093/bioinformatics/btx033

MotivationThe computational investigation of DNA binding motifs from binding sites is one of the classic tasks in bioinformatics and a prerequisite for understanding gene regulation as a whole. Due to the development of sequencing technologies and the increasing number of available genomes, approaches based on phylogenetic footprinting become increasingly attractive. Phylogenetic footprinting requires phylogenetic trees with attached substitution probabilities for quantifying the evolution of binding sites, but these trees and substitution probabilities are typically not known and cannot be estimated easily.ResultsHere, we investigate the influence of phylogenetic trees with different substitution probabilities on the classification performance of phylogenetic footprinting using synthetic and real data. For synthetic data we find that the classification performance is highest when the substitution probability used for phylogenetic footprinting is similar to that used for data generation. For real data, however, we typically find that the classification performance of phylogenetic footprinting surprisingly increases with increasing substitution probabilities and is often highest for unrealistically high substitution probabilities close to one. This finding suggests that choosing realistic model assumptions might not always yield optimal predictions in general and that choosing unrealistically high substitution probabilities close to one might actually improve the classification performance of phylogenetic footprinting.
Publikation

Nettling, M.; Treutler, H.; Cerquides, J.; Grosse, I.; Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies BMC Bioinformatics 18, 141, (2017) DOI: 10.1186/s12859-017-1495-1

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.ResultsHere, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.ConclusionCombining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.
Preprints

Drost, H.-G.; Gabel, A.; Domazet-Lošo, T.; Quint, M.; Grosse, I.; Capturing Evolutionary Signatures in Transcriptomes with myTAI bioRxiv (2016) DOI: 10.1101/051565

Combining transcriptome data of biological processes or response to stimuli with evolutionary information such as the phylogenetic conservation of genes or their sequence divergence rates enables the investigation of evolutionary constraints on these processes or responses. Such phylotranscriptomic analyses recently unraveled that mid-developmental transcriptomes of fly, fish, and cress were dominated by evolutionarily conserved genes and genes under negative selection and thus recapitulated the developmental hourglass on the transcriptomic level. Here, we present a protocol for performing phylotranscriptomic analyses on any biological process of interest. When applying this protocol, users are capable of detecting different evolutionary constraints acting on different stages of the biological process of interest in any species. For each step of the protocol, modular and easy-to-use open-source software tools are provided, which enable a broad range of scientists to apply phylotranscriptomic analyses to a wide spectrum of biological questions.
Publikation

Drost, H.-G.; Bellstädt, J.; Ó'Maoiléidigh, D. S.; Silva, A. T.; Gabel, A.; Weinholdt, C.; Ryan, P. T.; Dekkers, B. J. W.; Bentsink, L.; Hilhorst, H. W. M.; Ligterink, W.; Wellmer, F.; Grosse, I.; Quint, M.; Post-embryonic Hourglass Patterns Mark Ontogenetic Transitions in Plant Development Mol. Biol. Evol. 33, 1158-1163, (2016) DOI: 10.1093/molbev/msw039

The historic developmental hourglass concept depicts the convergence of animal embryos to a common form during the phylotypic period. Recently, it has been shown that a transcriptomic hourglass is associated with this morphological pattern, consistent with the idea of underlying selective constraints due to intense molecular interactions during body plan establishment. Although plants do not exhibit a morphological hourglass during embryogenesis, a transcriptomic hourglass has nevertheless been identified in the model plant Arabidopsis thaliana. Here, we investigated whether plant hourglass patterns are also found postembryonically. We found that the two main phase changes during the life cycle of Arabidopsis, from embryonic to vegetative and from vegetative to reproductive development, are associated with transcriptomic hourglass patterns. In contrast, flower development, a process dominated by organ formation, is not. This suggests that plant hourglass patterns are decoupled from organogenesis and body plan establishment. Instead, they may reflect general transitions through organizational checkpoints.
Publikation

Nettling, M.; Treutler, H.; Cerquides, J.; Grosse, I.; Detecting and correcting the binding-affinity bias in ChIP-seq data using inter-species information BMC Genomics 17, 347, (2016) DOI: 10.1186/s12864-016-2682-6

BackgroundTranscriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. ChIP-seq has become the major technology to uncover genomic regions containing those binding sites, but motifs predicted by traditional computational approaches using these data are distorted by a ubiquitous binding-affinity bias. Here, we present an approach for detecting and correcting this bias using inter-species information.ResultsWe find that the binding-affinity bias caused by the ChIP-seq experiment in the reference species is stronger than the indirect binding-affinity bias in orthologous regions from phylogenetically related species. We use this difference to develop a phylogenetic footprinting model that is capable of detecting and correcting the binding-affinity bias. We find that this model improves motif prediction and that the corrected motifs are typically softer than those predicted by traditional approaches.ConclusionsThese findings indicate that motifs published in databases and in the literature are artificially sharpened compared to the native motifs. These findings also indicate that our current understanding of transcriptional gene regulation might be blurred, but that it is possible to advance this understanding by taking into account inter-species information available today and even more in the future.
Preprints

Drost, H.-G.; Bellstädt, J.; Ó’Maoiléidigh, D. S.; Silva, A. T.; Gabel, A.; Weinholdt, C.; Ryan, P. T.; Dekkers, B. J. W.; Bentsink, L.; Hilhorst, H.; Ligterink, W.; Wellmer, F.; Grosse, I.; Quint, M.; Post-embryonic hourglass patterns mark ontogenetic transitions in plant development bioRxiv (2015) DOI: 10.1101/035527

The historic developmental hourglass concept depicts the convergence of animal embryos to a common form during the phylotypic period. Recently, it has been shown that a transcriptomic hourglass is associated with this morphological pattern, consistent with the idea of underlying selective constraints due to intense molecular interactions during body plan establishment. Although plants do not exhibit a morphological hourglass during embryogenesis, a transcriptomic hourglass has nevertheless been identified in the model plant Arabidopsis thaliana. Here, we investigated whether plant hourglass patterns are also found post-embryonically. We found that the two main phase changes during the life cycle of Arabidopsis, from embryonic to vegetative and from vegetative to reproductive development, are associated with transcriptomic hourglass patterns. In contrast, flower development, a process dominated by organ formation, is not. This suggests that plant hourglass patterns are decoupled from organogenesis and body plan establishment. Instead, they may reflect general transitions through organizational checkpoints.
Publikation

Nettling, M.; Treutler, H.; Grau, J.; Keilwagen, J.; Posch, S.; Grosse, I.; DiffLogo: a comparative visualization of sequence motifs BMC Bioinformatics 16, 387, (2015) DOI: 10.1186/s12859-015-0767-x

BackgroundFor three decades, sequence logos are the de facto standard for the visualization of sequence motifs in biology and bioinformatics. Reasons for this success story are their simplicity and clarity. The number of inferred and published motifs grows with the number of data sets and motif extraction algorithms. Hence, it becomes more and more important to perceive differences between motifs. However, motif differences are hard to detect from individual sequence logos in case of multiple motifs for one transcription factor, highly similar binding motifs of different transcription factors, or multiple motifs for one protein domain.ResultsHere, we present DiffLogo, a freely available, extensible, and user-friendly R package for visualizing motif differences. DiffLogo is capable of showing differences between DNA motifs as well as protein motifs in a pair-wise manner resulting in publication-ready figures. In case of more than two motifs, DiffLogo is capable of visualizing pair-wise differences in a tabular form. Here, the motifs are ordered by similarity, and the difference logos are colored for clarity. We demonstrate the benefit of DiffLogo on CTCF motifs from different human cell lines, on E-box motifs of three basic helix-loop-helix transcription factors as examples for comparison of DNA motifs, and on F-box domains from three different families as example for comparison of protein motifs.ConclusionsDiffLogo provides an intuitive visualization of motif differences. It enables the illustration and investigation of differences between highly similar motifs such as binding patterns of transcription factors for different cell types, treatments, and algorithmic approaches.
Publikation

Moreno, P.; Beisken, S.; Harsha, B.; Muthukrishnan, V.; Tudose, I.; Dekker, A.; Dornfeldt, S.; Taruttis, F.; Grosse, I.; Hastings, J.; Neumann, S.; Steinbeck, C.; BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology BMC Bioinformatics 16, 56, (2015) DOI: 10.1186/s12859-015-0486-3

BackgroundOntology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis.ResultsWe describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology.ConclusionsBiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.
Publikation

Trutschel, D.; Schmidt, S.; Grosse, I.; Neumann, S.; Joint analysis of dependent features within compound spectra can improve detection of differential features Front. Bioeng. Biotechnol. 3, 129, (2015) DOI: 10.3389/fbioe.2015.00129

Mass spectrometry is an important analytical technology in metabolomics. After the initial feature detection and alignment steps, the raw data processing results in a high-dimensional data matrix of mass spectral features, which is then subjected to further statistical analysis. Univariate tests like Student’s t-test and Analysis of Variances (ANOVA) are hypothesis tests, which aim to detect differences between two or more sample classes, e.g., wildtype-mutant or between different doses of treatments. In both cases, one of the underlying assumptions is the independence between metabolic features. However, in mass spectrometry, a single metabolite usually gives rise to several mass spectral features, which are observed together and show a common behavior. This paper suggests to group the related features of metabolites with CAMERA into compound spectra, and then to use a multivariate statistical method to test whether a compound spectrum (and thus the actual metabolite) is differential between two sample classes. The multivariate method is first demonstrated with an analysis between wild-type and an over-expression line of the model plant Arabidopsis thaliana. For a quantitative evaluation data sets with a simulated known effect between two sample classes were analyzed. The spectra-wise analysis showed better detection results for all simulated effects.
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