The main ambition of our computational biology lab is to predict the regulatory evolution of gene expression and its causal association with trait evolvability. Linking genotype and phenotype to make predictive claims about trait evolvability and speciation has always been a grand aim of genetics, molecular biology and interfacing disciplines. While in the past much has been learned about individual molecular examples able to induce phenotypic or morphological change, a system-scale understanding of causal genotype-phenotype associations remains an open challenge. Recent advancements in computer science (HPC/cloud-computing, advances in machine learning and artificial intelligence), high-throughput sequencing technologies (massive genome sequencing and assembly efforts, massive sequencing read archives), and new methodologies in scientific software development (software containers, workflow/pipeline architectures) can now be combined to address this classic genotype-phenotype problem using data-driven methodologies at an unprecedented scale. We approach this challenge by developing ultra-fast and sensitive DNA and protein aligners to leverage the comparative genomics method for functional genotype-phenotype association inference tasks. For example, we maintain and extend the protein aligner DIAMOND(https://github.com/bbuchfink/diamond), the genome-wide dNdS inference software OrthologR(https://github.com/drostlab/orthologr), and the evolutionary transcriptomics software myTAI(https://github.com/drostlab/myTAI). In addition, we collaborate with Susana Coelho’s EvoDevo department to establish sensitive gene age estimation implemented in the software GenEra(https://github.com/josuebarrera/GenEra). On the causal genotype-phenotype side of our research line, we establish new machine learning methodologies and experimental designs to capture causal links between genes during development.  Our homepage: www.drostlab.com  Our software: https://drostlab.com/software/ and https://github.com/drostlab