Züchtungsinformatik

Research

Algorithmic aspects of bioinformatics and breeding informatics in animal and plant breeding

The research field of breeding informatics is the connection between classical breeding research and informatic concepts. Those concepts are combined from a theoretical and practical point of view. Huge amounts of data (Big Data) from animal science as well as from plant science is used.
Therefore the following topics and areas can be defined as research field for breeding informatics:
  • Basic concepts and methods of bioinformatics/breeding informatics
  • DNA Sequencing
  • Variant calling
  • Genomic selection and prediction
  • Signal detection in DNA sequences
  • Genome analyses
  • Analysis of multi-omics data (Genomics, Transcriptomics, Proteomics, Metabolomics)
  • Databases in bioinformatics

Research Projects

Transparency and Quality Optimisation in the Pig Value Chain


The project focuses on the qualitative and sustainable management of the pig value chain – from breeding selection and farm management to slaughtering. Through the implementation of seamless UHF-RFID technology and the integration of existing data systems into a central platform, a transparent flow of information is established between all stakeholders. A key priority is the collection of physical parameters, such as intramuscular fat content (IMF), to enable data-driven optimization of meat quality and to maximize interoperability throughout the entire value chain.

  • Running time: 15.11.2024 - 14.11.2027
  • Funded by: BLE

Stability Optimisation of Random Forest Prediction Models by Determining the Optimal Number of Decision Trees


Random forest is a particularly prominent machine learning method used for predictions and prediction based decision-making processes. Although random forest is known to have many advantages, one aspect that is often overseen is that it is a non-deterministic method that can produce different models using the same input data. This can have severe consequences on decision-making processes. The R package optRF models the non-linear relationship between the number of trees and the prediction stability and uses this model to determine the optimal number of trees for any given data set.

Regulatory SNP Databases for Agricultural Animal and Plant Species


The project focuses on the identification and functional annotation of regulatory single nucleotide polymorphisms (rSNPs) within the promoter regions of agriculturally relevant animal and plant species. Using a standardized bioinformatic workflow, the database quantifies the impact of SNPs on transcription factor binding affinity. By categorizing SNP effects into the gain, loss, or score-change of binding sites, agReg-SNPdb provides a comprehensive resource for investigating the molecular causality of complex traits and diseases in livestock animals and crop plants.