Buchan, D. | Search bioRxiv

bioRxiv2024

Designing minimal E. coli genomes using variational autoencoders

Designing minimal bacterial genomes remains a key challenge in synthetic biology. There is currently a lack of efficient tools for the rapid generation of streamlined bacterial genomes, limiting research in this area. Here, using a pangenome dataset for Escherichia coli, we show that variational autoencoders with modified loss functions can successfully create minimised genomes retaining the essential genes identified in the literature. We then sampled new genomes from our fitted model and performed computational validation using an E. coli whole-cell model. We found 6 out of 100 of the sampled genomes were viable in the computer model. These underwent a minimization routine starting from the MG1655 genome giving rise to six new minimal genomes with around a 40 % reduction in size. This study proposes a rapid, machine learning-based approach for bacterial sequence generation, that could accelerate the genomic design process.

synthetic biology↗

bioRxiv2022

Improving cell-type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning

Cell-type identification is an important task for single-cell RNA-seq (scRNA-seq) data analysis. In this work, we proposed a novel Gaussian noise augmented scRNA-seq contrastive learning framework (GsRCL) to learn a type of discriminative feature representations for cell-type prediction tasks. The experimental results suggest that the feature representations learned by GsRCL successfully improved the accuracy of cell-type prediction using scRNA-seq expression profiles.

bioinformatics↗