SOURCE-LINKED INTELLIGENCE

Deep representational learning of the evolutionary DNA code in the vertebrate pallium

CORDIS · observation · Publication date unknown

ly learned from the genomic sequence and used to predict cell types. Recent advances in the field of single cell sequencing have made the generation of large epigenomic datasets possible, while novel machine learning models like DNA language models are providing unprecedented insights into the regulatory logic of the genome. In addition, for many non-model species high quality reference genomes are becoming available as there is an increased awareness for the need to preserve biodiversity. I will conduct single cell multiome sequencing, supplemented with low-cost single-cell ATAC-seq using the HyDrop platform - developed in the host-lab - to profile regulatory elements across cell types in the pallium from multiple species, including mammals, birds, lizards and fish. My experience in generating and annotating single cell data from the brain will aid in the analysis and alignment of the generated datasets. Next, I will use this data to study the relationships between the genome of a species and the identified cell types by training species-aware DNA language models and using these mo

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recordType

award

status

SIGNED

region

EU

value

200400

unit

EUR