Hajk-Georg Drost

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

Research lines

  • Comparative and Functional Genomics
  • Natural Variation
  • Evolution of Gene Regulation
  • Evolutionary Transcriptomics

Short vita

  • 2019 to present

    Computat ional Biology Group Leader at Department of Molecular Biology, Max-Planck Institute for Biology

  • 2018 to 2019

    Senior PostDoc at the University of Cambridge with Elliot Meyerowitz

  • 2015 to 2018

    PostDoc at the University of Cambridge with Jerzy Paszkowski

  • 2013 to 2015

    PhD Student at the Martin-Luther University Halle (Computer Science Department) with Ivo Grosse and Marcel Quint

    Milestone Content goes here

  • 2008 to 2013

    BSc and MSc in Bioinformatics at the Martin-Luther University Halle (Computer Science Department)

    Milestone Content goes here

Selected publications

Buchfink B, Reuter K, Drost HG. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nature Methods 18, 366–368 (2021)

Quint M, Drost HG, Gabel A, Ullrich KK, Boenn M, Grosse I. A transcriptomic hourglass in plant embryogenesis. Nature 490, 89-101 (2012).

Drost HG, Gabel A, Grosse I, Quint M. Evidence for active maintenance of phylotranscriptomic hourglass patterns in animal and plant embryogenesis. Molecular Biology and Evolution 32 (5), 1221-1231 (2015).

Sanchez DH*, Gaubert H*, Drost HG, Zabet NR, Paszkowski J. High-frequency recombination between members of an LTR retrotransposon family during transposition bursts. Nature Communications 8 (1), 1283 (2017). (* co-first)

Cho J, Benoit M, Catoni M, Drost HG, Brestovitsky A, Oosterbeek M, and Paszkowski J. Sensitive detection of preintegration intermediates of LTR retrotransposons in crop plants. Nature Plants 5, pages 26–33 (2019).