Real-time identification of epistatic interactions in SARS-CoV-2 from large genome collections
Innocenti G, Obara M, Costa B, Jacobsen H, Katzmarzyk M, Cicin-Sain L, Kalinke U, Galardini M
By applying statistical genetics methods to pathogen genome sequences, we aim to identify and validate genetic determinants of phenotypes such as pathogenicity, virulence and antibiotic resistance, e.g. in E. coli and P. aeruginosa.
Population genetic studies show that genetic variability between bacterial strains can influence the evolution of antimicrobial resistance. Using automated laboratory evolution (ALE), we are investigating how genetic backgrounds control AMR evolution.
Thanks to high-throughput sequencing, genome sequences of hundreds of bacterial strains can be analyzed efficiently, revealing differences of up to 60 % in gene content, as in E. coli. With the help of machine learning, we want to better predict the functions of accessory genes and decipher their contribution to survival in specialized niches.
Innocenti G, Obara M, Costa B, Jacobsen H, Katzmarzyk M, Cicin-Sain L, Kalinke U, Galardini M
D'Amato R, Taxiarchi C, Galardini M, Trusso A, Minuz R, Grilli S, Somerville A, Shittu D, Khalil A, Galizi R, Crisanti A, Simoni A, Müller R
Sommer H, Djamalova D, Galardini M
RESIST professorship for Marco Galardini
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