Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-Seq or protein data, respectively, but not both. A further significant performance improvement was made by the recently released GeneMark-ETP integrating all three data types.We present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-Seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperformed BRAKER1 and BRAKER2. The average transcript-level F1-score was increased by ~20 percentage points on average, while the difference was most pronounced for species with large and complex genomes. BRAKER3 also outperformed other existing tools, MAKER2, Funannotate and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.