Genome representation and comparison using embeddingsKopač, Tilen (Avtor) Curk, Tomaž (Mentor) Kern, Roman (Komentor) bioinformaticsautoencoderembeddingThe rise of modern DNA sequencing methods and tools has led to an abundance of readily available genomic data. Since identifying the locations of genes and coding regions in novel organisms is a time-intensive process, we endeavored to create a pipeline, which produces informative embeddings from raw DNA sequences. Salient features are learned using autoencoder neural networks. Models with different parameter values and combinations of layer types were trained and evaluated. The autoencoders transform a given genome into a point cloud in the latent space. We implemented and evaluated various sampling methods, which compress this point cloud into a compact representation. The quality of the embeddings was validated on a downstream task of taxonomic realm prediction of novel organisms from their raw DNA sequences. Furthermore, we propose several embedding visualizations for intuitive genome understanding and comparison.20212021-12-06 14:15:00Magistrsko delo/naloga133630VisID: 28521COBISS_ID: 88505603sl