In this study, we built machine learning classifiers for predicting the presence or absence of the variable genes occurring in 10-90% of all publicly available high-quality Escherichia coli genomes. The BV-BRC genus-specific protein families were used to define orthologs across the set of genomes, and a single binary classifier was built for predicting the presence or absence of each family in each genome. Each model was built using the nucleotide k-mers from a set of 100 conserved genes as features. The resulting set of 3,259 XGBoost classifiers had a per-genome average macro F1 score of 0.944 [0.943-0.945, 95% CI]. We show that the F1 scores are stable across MLSTs, and that the trend can be recapitulated through sampling with a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including hypothetical proteins, were easily predicted (F1 = 0.902 [0.898-0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions, including transposition- (F1 = 0.895 [0.882-0.907, 95% CI]), phage- (F1 = 0.872 [0.868-0.876, 95% CI]), and plasmid-related (F1 = 0.824 [0.814-0.834, 95% CI]) functions had slightly lower F1 scores, but were still accurate. Finally, we applied the models to a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources and observed an average per-genome F1 score of 0.880 [0.876-0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data.
BackgroundThere is increasing evidence of the efficacy of point-of-use water filters on diarrhea prevalence in numerous global settings, in both observational studies and randomized experiments. Most studies, however, are focused on rural locations. Methods We use self-report household surveys to monitor a set of approximately 10,000 households receiving point-of-use water filters and WASH training in Kibera, Kenya. Twenty-five drinking water sources throughout the 7 neighborhoods were also selected for testing of E. coli, total coliform, bacterial 16S rRNA community sequencing and metals. Albendazole was provided to all households at distribution as part of the standard filter distribution protocol, with a subset of 2,642 households not receiving Albendazole at distribution, instead receiving it at the second follow-up (approximately 5 weeks after filter distribution).ResultsAfter data cleaning, a sample of 6,795 households were analyzed using mixed effects generalized linear models to account for repeated household measurements, geospatial and temporal effects, interviewer and other household covariates. Models predicted self-reported, 2-week prevalence of diarrhea. After accounting for confounding factors, self-reported diarrhea rates dropped from 52.7% to 2.2% after approximately 70 days of filter use. Field testing characterized most water sources (18 out of 25) as unsafe for Total coliforms, many for E. coli (6 out of 25), and one source above WHO health guidelines for arsenic. There was no evidence of a difference in self-reported diarrhea prevalence between households receiving Albendazole at distribution vs. those that didn’t (p>0.05).ConclusionsThe introduction of Sawyer filters to households in a densely populated informal settlement reduced diarrhea and other health related problems. Representative water quality testing indicates a high frequency of drinking water source contamination with E. coli and Total Coliforms but a very low frequency of dissolved metals present, above WHO guidelines for drinking water. Anti-parasitic medication distribution had little to no impact on the results. Future randomized controlled studies with objective health measures are needed to ensure cause-effect impact of the filters, and study of filter longevity in the field continues to be a critical need.
Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs , and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%–90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943–0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including “hypothetical proteins” was accurately predicted (F1 = 0.902 [0.898–0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876–0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data. IMPORTANCE Having the ability to predict the protein-encoding gene content of a genome is important for assessing genome quality, binning genomes from shotgun metagenomic assemblies, and assessing risk due to the presence of antimicrobial resistance and other virulence genes. In this study, we built a set of binary classifiers for predicting the presence or absence of variable genes occurring in 10%–90% of all publicly available E. coli genomes. Overall, the results show that a large portion of the E. coli variable gene content can be predicted with high accuracy, including genes with functions relating to horizontal gene transfer. This study offers a strategy for predicting gene content using limited input sequence data.
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