Lars Gabriel
Lars Gabriel
Postdoctoral Researcher in Computational Biology
I develop computational methods and research software for eukaryotic genome annotation, with a focus on deep learning-based gene prediction and practical tools that work on real genomic data.
Genome Annotation · Machine Learning · Bioinformatics · Scientific Software
Areas of Work
Genome Annotation
Gene structure prediction for complex eukaryotic genomes.
Deep Learning
Sequence-based models guided by biological structure and evidence.
Scientific Software
Open-source tools that run on real datasets and research infrastructure.
Current Focus
Research Questions
Learning Gene Structure
How can sequence-based models learn eukaryotic gene structure across diverse clades, and where should they be combined with RNA-seq, protein evidence, or classical annotation methods?
Practical Annotation
Tools That Scale
Scaling accurate annotation toward the diversity of eukaryotic species, while keeping tools inspectable and usable on common research computing infrastructure.
Selected Work
Tool
Tiberius
A deep learning-based gene prediction tool that end-to-end integrates a Hidden Markov Model for eukaryotic genome annotation.
Pipeline
BRAKER3
An automated genome annotation pipeline integrating RNA-seq and protein evidence for scalable eukaryotic annotation.
Tool
TSEBRA
A transcript selector for BRAKER that combines alternative predictions into a final annotation set.
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Collaboration
Research & Collaboration
I am open to collaborations involving genome annotation, bioinformatics workflow design, annotation quality assessment, research software, and machine learning methods for biological sequence analysis.
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