Whole Genome Transformer for Gene Interaction Effects in Microbiome Habitat Specificity
This work addresses the problem of understanding microbial adaptation to diverse environments for researchers in microbiology and genomics, representing an incremental advance by applying existing methods to new data with attribution techniques.
The researchers tackled the challenge of predicting complex phenotypes from microbiome genomic data by proposing a framework that uses large models for gene vectorization to predict habitat specificity from entire microbial genome sequences, achieving solid predictive performance and identifying gene associations underlying complex phenotypes.
Leveraging the vast genetic diversity within microbiomes offers unparalleled insights into complex phenotypes, yet the task of accurately predicting and understanding such traits from genomic data remains challenging. We propose a framework taking advantage of existing large models for gene vectorization to predict habitat specificity from entire microbial genome sequences. Based on our model, we develop attribution techniques to elucidate gene interaction effects that drive microbial adaptation to diverse environments. We train and validate our approach on a large dataset of high quality microbiome genomes from different habitats. We not only demonstrate solid predictive performance, but also how sequence-level information of entire genomes allows us to identify gene associations underlying complex phenotypes. Our attribution recovers known important interaction networks and proposes new candidates for experimental follow up.