Vergangene Veranstaltungen 2021

Gastgeber: Franziska Turck

Aline Probst: Chromatin dynamics during the seed-to-seedling transition

Wednesday Seminar
The organization of DNA with histone proteins into chromatin is fundamental to the regulation of gene expression. Incorporation of different histone variants into the nucleosome, together with post-translational modifications of these histone variants, allows modulation of chromatin accessibility and contributes to the regulation of gene expression necessary for the organism to respond to developmental and environmental cues. We are interested in the changes in chromatin organization and gene expression that occur during developmental transitions. I will discuss our recent data investigating the concerted changes in the transcriptome, histone variant repertoire, and nuclear organization during the seed-to-seedling transition.

Christine Queitsch: Learning the grammar of plant regulator DNA with MPRAs and long reads

Wednesday Seminar

Fredy Barneche: Transcription intensification during Arabidopsis photomorphogenesis, a chromatin perspective

Alisandra Denton: Annotating Eukaryotic genomes with Deep Learning

Special Seminar
Gene calling, or structural gene annotation, is critical to extracting biological knowledge from a genome, yet existing methods for gene calling in Eukaryotes lag far behind genome sequencing and assembly in quality, ease and speed. However, we have every reason to believe gene calling is a tractable problem. The information about what is or is not a gene is encoded in the raw DNA sequence. Hence, we need to update our modeling. Deep Learning is a new and transformative technology that can model extraordinarily complex and non-linear relationships—like those found in biology—and has the potential to ‘decode’ the information in DNA. We have previously demonstrated the applicability of Deep Learning to gene calling in our project Helixer, which showed ground-breaking performance in classifying genic categories. Here we establish usability by post-processing the base-wise predictions from Helixer into full primary gene models with a Hidden Markov Model. Preliminary results in selected plant species indicate the resulting gene models redefine state-of-the-art for a de novo gene caller and—on some species—even approach reference quality as compared to RNAseq data. In the future, we will expand applicability across Eukaryotes and expand our annotation targets to include additional genomic features such as promotors. The improved annotations will support research methods from cloning to ‘omics analyses and a wide variety of applications including biomedical research and crop bioengineering. The code is available at https://github.com/weberlab-hhu/Helixer [mehr]

Frank Johannes: Molecular properties of epimutation hotspots

Vanessa Wahl: Impact of Nutrient Availability on Plant Development

Stephanie Panier: When there is damage in the genome: Dissecting the molecular mechanisms that connect genome instability to ageing and disease

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