Machine learning for pangenomics

Abstract

Finding genotype-phenotype associations is an important task in biology. Most of the the existing reference-based methods introduce biases because they use a single genome from an individual as the reference sequence. So, these biases can lead to limitations in inferred genotype-phenotype associations. Advances in sequencing techniques have enabled access to a large number of sequenced genomes from multiple organisms from different species. These can be used to create a pangenome, which represents a collection of genetic information from multiple organisms. Using a pangenome can effectively reduce those limitation issues as it does not require a reference. Recently, machine learning techniques are emerging as effective methods for problems involving genomics and pangenomics data. Kernel methods are used as a part of machine learning models to compute similarities between instances. Kernels can map the given set of data into a different feature space that can help distinguish the data into corresponding classes. In this work, we develop supervised machine learning models using a set of features gathered using pangenomic graphs, and the effectiveness of those features is evaluated in predicting yeast phenotypes. We first evaluated the effectiveness of the features using a a traditional supervised machine learning model and, then compared it to novel custom kernels that incorporate the information from the pangenomic graphical structure. Experimental results using yeast phenotypes indicate that the developed machine learning models that use reference-free features and novel kernels outperform models based on traditional reference-based features. This work has implications for bioinformaticians and computational biologists working with pangenomes as well as computer scientists developing predictive models for genomic data.