Project proposal details
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Inference of signatures of natural selection from genomes using deep learning
Project based at
Silwood Park (Imperial)
Our team has developed a wide range of applications of machine learning, specifically convolutional neural networks, to infer evolutionary events from genomic data (e.g. Torada et al. 2019 BMC Bioinformatics). In this project, the student will apply, and modify if necessary, our current deep learning algorithms to detect natural selection from population genomic data: ImaGene (https://github.com/mfumagalli/ImaGene) or BaSe (https://github.com/ulasisik/balancing-selection). Applications will relate on ongoing projects, for instance for the understanding the evolutionary history of human susceptibility to long QT syndrom (LQTS) or to detect any adaptive evolution in ACE and ACE2 genes in humans and primates.
good programming in python and bash/linux
additional help in population genetics will be offered
Selection and eligibility
good attitude to coding is essential