Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes

Lees, John A.; Vehkala, Minna; Välimäki, Niko; Harris, Simon R.; Chewapreecha, Claire; Croucher, Nicholas J.; Marttinen, Pekka; Davies, Mark R.; Steer, Andrew C.; Tong, Steven Y. C.; Honkela, Antti; Parkhill, Julian; Bentley, Stephen D.; Corander, Jukka

doi:10.1038/ncomms12797

Cited by 183 publications

(160 citation statements)

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“…Finally, we used k-mer distances 30 , mash 28 and andi 29 to create distance matrices. andi counts the number of mismatches between equally spaced maximal exact matches between a pair of sequences.…”

Section: Resultsmentioning

confidence: 99%

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Evaluation of phylogenetic reconstruction methods using bacterial whole genomes: a simulation based study

Lees¹,

Kendall²,

Parkhill³

et al. 2018

Wellcome Open Res

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Background: Phylogenetic reconstruction is a necessary first step in many analyses which use whole genome sequence data from bacterial populations. There are many available methods to infer phylogenies, and these have various advantages and disadvantages, but few unbiased comparisons of the range of approaches have been made. Methods: We simulated data from a defined “true tree” using a realistic evolutionary model. We built phylogenies from this data using a range of methods, and compared reconstructed trees to the true tree using two measures, noting the computational time needed for different phylogenetic reconstructions. We also used real data from Streptococcus pneumoniae alignments to compare individual core gene trees to a core genome tree. Results: We found that, as expected, maximum likelihood trees from good quality alignments were the most accurate, but also the most computationally intensive. Using less accurate phylogenetic reconstruction methods, we were able to obtain results of comparable accuracy; we found that approximate results can rapidly be obtained using genetic distance based methods. In real data we found that highly conserved core genes, such as those involved in translation, gave an inaccurate tree topology, whereas genes involved in recombination events gave inaccurate branch lengths. We also show a tree-of-trees, relating the results of different phylogenetic reconstructions to each other. Conclusions: We recommend three approaches, depending on requirements for accuracy and computational time. Quicker approaches that do not perform full maximum likelihood optimisation may be useful for many analyses requiring a phylogeny, as generating a high quality input alignment is likely to be the major limiting factor of accurate tree topology. We have publicly released our simulated data and code to enable further comparisons.

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Section: Resultsmentioning

confidence: 99%

“…Hamming distance between informative k-mers using a subsample of 1% of counted k-mers from assemblies 30 .…”

Section: Methodsmentioning

confidence: 99%

Evaluation of phylogenetic reconstruction methods using bacterial whole genomes: a simulation based study

Lees¹,

Kendall²,

Parkhill³

et al. 2018

Wellcome Open Res

Self Cite

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“…[33, 34]). However, as for association methods [28, 35], the true impact of genetic features is confounded by their phylogenetic distribution and population structure. Therefore, approaches to distinguish causal resistance genes from all correlated markers require additional experimental study.…”

Section: Discussionmentioning

confidence: 99%

Precise prediction of antibiotic resistance in Escherichia coli from full genome sequences

Moradigaravand

Palm

Farewell

et al. 2018

Preprint

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The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The diagnosis is typically made by measuring a few known determinants previously identified from whole genome sequencing, and thus is restricted to existing information on biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average F1 score of 0.88 on held-out data (range 0.66-0.96). While the best models most frequently employed all inputs, an average F1 score of 0.73 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and failed to improve prediction for ten out of 11 antibiotics. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data are informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.SummaryOne of the major health threats of 21st century is emergence of antibiotic resistance. To manage its economic impact, efforts are made to develop novel diagnostic tools that rapidly detect resistant strains in clinical settings. In our study, we employed a range machine learning tools to predict antibiotic resistance from whole genome sequencing data for E. coli. We used the presence or absence of genes, population structure and isolation year of isolates as predictors, and could attain average precision of 0.93 and recall of 0.83, without prior knowledge about the causal mechanisms. These results demonstrate the potential application of machine learning methods as a diagnostic tool in healthcare settings.

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“…Such correlations can indicate that the genes involved have epistatic effects on fitness or that their presence or absence is a result of similar selective pressures. Finally, the output of Panaroo seamlessly interfaces with pyseer (v1.3.0), a bacterial GWAS package (24,50). pyseer includes a wide range of methods for performing association studies allowing for phenotypic associations to be found with gene or structural presence/absence patterns.…”

Section: Structural Variationmentioning

confidence: 99%

Producing Polished Prokaryotic Pangenomes with the Panaroo Pipeline

Tonkin‐Hill

MacAlasdair

Ruis

et al. 2020

Preprint

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Population-level comparisons of prokaryotic genomes must take into account the substantial differences in gene content, resulting from frequent horizontal gene transfer, gene duplication and gene loss. However, the automated annotation of prokaryotic genomes is imperfect, and errors due to fragmented assemblies, contamination, diverse gene families and mis-assemblies accumulate over the population, leading to profound consequences when analysing the set of all genes found in a species. Here we introduce Panaroo, a graph based pangenome clustering tool that is able to account for many of the sources of error introduced during the annotation of prokaryotic genome assemblies. We verified our approach through extensive simulations of de novo assemblies using the infinitely many genes model and by analysing a number of publicly available large bacterial genome datasets. Using a highly clonal Mycobacterium tuberculosis dataset as a negative control case, we show that failing to account for annotation errors can lead to pangenome estimates that are dominated by error. We additionally demonstrate the utility of the improved graphical output provided by Panaroo by performing a pan-genome wide association study in Neisseria gonorrhoeae and by analysing gene gain and loss rates across 51 of the major global pneumococcal sequence clusters. Panaroo is freely available under an open source MIT licence at https://github.com/gtonkinhill/panaroo.

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Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes

Cited by 183 publications

References 50 publications

Evaluation of phylogenetic reconstruction methods using bacterial whole genomes: a simulation based study

Evaluation of phylogenetic reconstruction methods using bacterial whole genomes: a simulation based study

Precise prediction of antibiotic resistance in Escherichia coli from full genome sequences

Producing Polished Prokaryotic Pangenomes with the Panaroo Pipeline

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