Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

Abstract

Background:The identification of genomic biomarkers is a key step towards improving diagnostic tests and therapies. We present a reference-free method for this task that relies on a k-mer representation of genomes and a machine learning algorithm that produces intelligible models. The method is computationally scalable and well-suited for whole genome sequencing studies. Results: The method was validated by generating models that predict the antibiotic resistance of C. difficile, M. tuberculosis, P. aeruginosa, and S. pneumoniae for 17 antibiotics. The obtained models are accurate, faithful to the biological pathways targeted by the antibiotics, and they provide insight into the process of resistance acquisition. Moreover, a theoretical analysis of the method revealed tight statistical guarantees on the accuracy of the obtained models, supporting its relevance for genomic biomarker discovery. Conclusions: Our method allows the generation of accurate and interpretable predictive models of phenotypes, which rely on a small set of genomic variations. The method is not limited to predicting antibiotic resistance in bacteria and is applicable to a variety of organisms and phenotypes. Kover, an efficient implementation of our method, is open-source and should guide biological efforts to understand a plethora of phenotypes (http://github. com/aldro61/kover/).

Predicting Antimicrobial Resistance Using Conserved Genes

“…The average very major error rate (VME), which is defined as resistant genomes that are erroneously predicted to be susceptible, and the average major error rate (ME), which is defined as susceptible genomes that are erroneously predicted to be resistant, tend to go down as gene set size increases. Although the core gene set models described in Figure 1 have lower F1 scores and higher error rates than full-genome models that have been published previously [21][22][23][24]27,29 , their accuracies are striking given the small sizes of the input data sets and the removal of well-annotated AMR genes.…”

“…These predictions are typically made using either rules-based or machine learning models [16][17][18][19][20] . Several studies have also built machine learning models for predicting AMR phenotypes by using assembled genomes or pan genomes as training sets [21][22][23][24][25][26][27] . In these cases, the machine learning algorithm detects the most discriminating features (typically short nucleotide k-mers) from a training set with laboratory-derived AMR phenotypes.…”

Interpretable genotype-to-phenotype classifiers with performance guarantees

“…Several thousand bacterial genomes and their susceptibility to antimicrobial agents are publicly available 10 and make for an ideal study set. The use of machine learning to predict AMR phenotypes has previously been investigated using two approaches: 1) considering only known resistance genes and mutations [11][12][13][14] , 2) considering whole genomes with no prior knowledge of resistance mechanisms [15][16][17][18][19][20] . Accuracies of CART b and SCM b on the validation data of each dataset, grouped by species.…”

“…Three properties are illustrated for each rule in the models: 1) the locus at which the corresponding k-mer can be found, 2) a measure of rule importance, and 3) the number of equivalent rules. The first is the region of the genome in which the k-mer is located and was determined using the Basic Local Alignment Search Tool 27 17 for SCM b , and were normalized to sum to one. The third results from k-mers that are equally predictive of the phenotype.…”

“…For instance, k-mers located on the same gene may always be present or absent simultaneously, resulting in several rules that are equally predictive for the model. Equivalent rules were shown to be indicative of the nature of genomic variations 17 . A small number of equivalent rules, with k-mers overlapping a certain position of the genome, suggests a point mutation, whereas a large number, targeting multiple contiguous k-mers, indicates large scale genomic rearrangements, such as gene insertions and deletions.…”