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Schwacke, R.

Publications and source records attributed to Schwacke, R..

3 recordsLinked to original sources

pamiR: INVESTIGATING PLANT CELLS ONE ORGANELLE AT A TIME

Functional genetic redundancy (FGR) within gene families limits the discovery of gene function in plants because single-gene perturbations often fail to produce informative phenotypes. Artificial microRNAs (amiRNAs) provide a strategy to silence multiple related genes simultaneously. However, the existing amiRNA-based libraries used for genetic gene function discovery in plants do not account for the subcellular localization of gene products, which can lead to pleiotropic or difficult-to-interpret phenotypes. Plastids are essential plant cell organelles that integrate central metabolic and signaling processes, including photosynthesis, hormone biosynthesis, and environmental responses. Here we introduce pamiR, a plastid-targeted amiRNA library designed to enable organelle-specific gene function discovery in Arabidopsis thaliana. Using plastid proteomic datasets, we identified high-confidence plastid-localized proteins and designed amiRNAs to target their gene(s) (families) minimizing FGR. This amiRNA library was introduced in a vector with fluorescence-accumulating seed technology enabling rapid, herbicide-free selection and screening in the first generation. Validation by next-generation sequencing, confirmed high representation and uniform distribution of amiRNAs within pamiR. Proof-of-concept screens recovered mutants affecting known and additional candidate genes involved in photosynthesis and abscisic acid biosynthesis. Therefore, the pamiR library provides a fast platform for plastid-focused genetic screens that is compatible with existing mutant collections. One-sentence summaryThe plastid amiRNA (pamiR) library enables organelle-specific forward genetics without functional genetic redundancy.

plant biology↗

PubPlant: a continuously updated online resource for sequenced and published plant genomes

Advances in next-generation sequencing technologies over the last decade have substantially reduced the cost and effort required to sequence plant genomes. Whereas early efforts focused primarily on economically important crops and model species, attention has now turned to a broader range of plants, including those with larger and more complex genomes. In 2024, the genomes of 500 plant species were published, including 370 sequenced for the first time. Tracking and providing access to published plant genomes (now covering more than 1800 species) is an invaluable service for plant researchers. PubPlant is an online resource that serves this purpose by cataloging published plant genome sequences and offering multiple visualizations (https://www.plabipd.de/pubplant_main.html). It includes a chronology of genome publications, and cladograms to display the phylogenetic relationships among the sequenced plants. An overview diagram for seed plants highlights taxonomic orders and families with sequenced species, and reveals those that have been overlooked thus far. As a use case for PubPlant, we evaluated the status of sequenced food crops. We found that the five plant families featuring the most food crops were those containing the most sequenced plant species.

plant biology↗

Helixer--de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model.

AO_SCPLOWBSTRACTC_SCPLOWGene structural annotation is a critical step in obtaining biological knowledge from genome sequences yet remains a major challenge in genomics projects. Current de novo Hidden Markov Models are limited in their capacity to model biological complexity; while current pipelines are resource-intensive and their results vary in quality with the available extrinsic data. Here, we build on our previous work in applying Deep Learning to gene calling to make a fully applicable, fast and user friendly tool for predicting primary gene models from DNA sequence alone. The quality is state-of-the-art, with predictions scoring closer by most measures to the references than to predictions from other de novo tools. Helixers predictions can be used as is or could be integrated in pipelines to boost quality further. Moreover, there is substantial potential for further improvements and advancements in gene calling with Deep Learning. Helixer is open source and available at https://github.com/weberlab-hhu/Helixer A web interface is available at https://www.plabipd.de/helixer_main.html

bioinformatics↗