Identifying transcription factor-DNA interactions using machine learning
Ontology highlight
ABSTRACT:
ORGANISM(S): Glycine max
PROVIDER: GSE193400 | GEO | 2023/06/01
REPOSITORIES: GEO
Similar Datasets
Project description:Auxin critically regulates nearly every aspect of plant growth and development. Auxin-driven transcriptional responses are mediated through the AUXIN RESPONSE FACTOR (ARF) family of transcription factors. Although ARF protein stability is regulated via the 26S proteasome, molecular mechanisms underlying ARF stability and turnover are unknown. Here, we report the identification and functional characterization of an F-Box E3 ubiquitin ligase, which we have named AUXIN RESPONSE FACTOR F-BOX1 (AFF1). AFF1 directly interacts with ARF19 and regulates its accumulation. Mutants defective in AFF1 display ARF19 protein hyperaccumulation, mild auxin resistance, and developmental defects. Together, our data suggest a new mechanism, namely control of ARF protein stability, in regulating auxin responsiveness.
2022-06-09 | GSE172353 | GEO
Project description:Identifying transcription factor-DNA interactions using machine learning
| PRJNA796028 | ENA
Project description:The phytohormone auxin plays crucial roles in nearly every aspect of plant growth and development. The AUXIN RESPONSE FACTOR (ARF) family of transcription factors regulates auxin-responsive gene expression and exhibit nuclear localization in regions of high auxin responsiveness. Here we show that activating ARF7 and ARF19 proteins accumulate in micron-sized assemblies within the cytoplasm of tissues with attenuated auxin responsiveness. The intrinsically disordered middle region and the folded PB1 interaction domain of ARFs drive protein assembly formation. Mutation of a single lysine within the PB1 domain abrogates cytoplasmic assemblies, promotes ARF nuclear localization, and results in an altered transcriptome and morphological defects. Our data suggest a model in which ARF nucleo-cytoplasmic partitioning regulates auxin responsiveness, thus providing a mechanism for cellular competence for auxin signaling.
2019-07-24 | GSE104385 | GEO
Project description:Plants depend on the signalling of the phytohormone auxin for their development and for responding to environmental perturbations. The associated biomolecular signalling network involves a negative feedback on Aux/IAA proteins which mediate the influence of auxin (the signal) on the auxin response factor (ARF) transcription factors (the drivers of the response). To probe the role of this feedback, we consider alternative in silico signalling networks implementing different operating principles. By a comparative analysis, we find that the presence of a negative feedback allows the system to have a far larger sensitivity in its dynamical response to auxin and that this sensitivity does not prevent the system from being highly resilient. Given this insight, we build a new biomolecular signalling model for quantitatively describing such Aux/IAA and ARF responses.
2022-05-10 | MODEL2003060002 | BioModels
Project description:Plants depend on the signalling of the phytohormone auxin for their development and for responding to environmental perturbations. The associated biomolecular signalling network involves a negative feedback on Aux/IAA proteins which mediate the influence of auxin (the signal) on the auxin response factor (ARF) transcription factors (the drivers of the response). To probe the role of this feedback, we consider alternative in silico signalling networks implementing different operating principles. By a comparative analysis, we find that the presence of a negative feedback allows the system to have a far larger sensitivity in its dynamical response to auxin and that this sensitivity does not prevent the system from being highly resilient. Given this insight, we build a new biomolecular signalling model for quantitatively describing such Aux/IAA and ARF responses.
2024-09-02 | BIOMD0000000927 | BioModels
Project description:Soybean cultivars RCAT-Angora & Harovinton, comparison of embryos at 30,40,50,60 & 70 days after pollination (dap) and seed development within each cultivar" Keywords: time course
2007-01-01 | GSE4194 | GEO
Project description:We have developed a high throughput, next-generation DNA sequencing assay for rapid transcription factor binding site (TFBS) discovery in a genomic context. DNA affinity purification sequencing (DAP-seq), which uses affinity-purified transcription factors (TFs) to capture genomic DNA fragments, was applied to all 1,725 Arabidopsis thaliana TFs. High confidence TFBS motifs for 529 TFs and genome-wide enrichment maps for 349 factors were identified. In total,~ 2.7 million TFBS were identified which predict thousands of TF target genes enriched for known and novel functions.. Comparison of TF-binding using cytosine-methylated and -unmethylated genomic DNA revealed a 2-50 fold inhibition at methylated motifs for ~82% (264) of factors tested while 4.6% (15) showed stronger binding to methylated motifs. Finally, we describe how binding of Arabidopsis and maize Auxin Response Factors (ARFs) at phased motif repeats is highly enriched at ARF target gene promoters and how this architecture may allow for stabilization of dimers/multimers.
2016-05-25 | GSE60141 | GEO
Project description:The genes that are required for organismal survival are annotated as 'essential genes'. Identifying all the essential genes of an animal species can reveal critical functions that are needed during the development of the organism. To inform studies on mouse development, we developed a supervised machine learning classifier based on phenotype data from mouse knockout experiments. We used this classifier to predict the essentiality of mouse genes lacking experimental data. Validation of our predictions against a blind test set of recent mouse knockout experimental data indicated a high level of accuracy (>80%). We also validated our predictions for other mouse mutagenesis methodologies, demonstrating that the predictions are accurate for lethal phenotypes isolated in random chemical mutagenesis screens and embryonic stem cell screens. The biological functions that are enriched in essential and non-essential genes have been identified, showing that essential genes tend to encode intracellular proteins that interact with nucleic acids. The genome distribution of predicted essential and non-essential genes was analysed, demonstrating that the density of essential genes varies throughout the genome. A comparison with human essential and non-essential genes was performed, revealing conservation between human and mouse gene essentiality status. Our genome-wide predictions of mouse essential genes will be of value for the planning of mouse knockout experiments and phenotyping assays, for understanding the functional processes required during mouse development, and for the prioritisation of disease candidate genes identified in human genome and exome sequence datasets.
| S-EPMC6307915 | biostudies-literature
Project description:Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and Bombyx mori and validated predictions experimentally yielding similar performances. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.
| S-EPMC8634067 | biostudies-literature
Project description:A quantitative understanding of the worldwide plastics distribution is required not only to assess the extent and possible impact of plastic litter on the environment but also to identify possible counter measures. A systematic collection of data characterizing amount and composition of plastics has to be based on two crucial components: (i) An experimental approach that is simple enough to be accessible worldwide and sensible enough to capture the diversity of plastics; (ii) An analysis pipeline that is able to extract the relevant parameters from the vast amount of experimental data. In this study, we demonstrate that such an approach could be realized by a combination of photoluminescence spectroscopy and a machine learning-based theoretical analysis. We show that appropriate combinations of classifiers with dimensional reduction algorithms are able to identify specific material properties from the spectroscopic data. The best combination is based on an unsupervised learning technique making our approach robust to alternations of the input data.
| S-EPMC9637756 | biostudies-literature