Jukka Corander

This paper curates a high-quality collection of over 10,000 E. coli and Shigella genomes to facilitate analysis of genetic diversity and lineage differences.

The paper estimates molecular clock rates in Campylobacter using a birthday problem analogy to quantify lineage turnover and diversification in environmental bacteria.

This paper uses genome-wide co-evolution analysis to identify genomic rearrangements in Enterococcus faecium, linking them to adaptation to hospital environments.

Panaroo's graph-based pangenome clustering uses gene adjacency across genomes to correct annotation errors, shrinking spurious accessory genomes and enlarging core genomes in simulated and real bacteria.

gplas separates plasmid contigs from bacterial genomes using short-read data to enable plasmid epidemiology analysis.

This study traces the evolutionary divergence of Neisseria gonorrhoeae by analyzing global isolates, linking antimicrobial use to the emergence of two distinct lineages with different resistance strategies.

This paper introduces SpydrPick, a model-free method to detect co-selection and epistasis in bacterial genomes, revealing both known and novel targets in human pathogens.

The paper introduces mSWEEP, a method for high-resolution bacterial lineage quantification from plate sweeps, showing improved accuracy in detecting and estimating abundances of bacterial lineages.

fastbaps provides a fast Bayesian clustering method for genetic data, outperforming existing methods in speed while maintaining comparable accuracy on real and simulated datasets.

rPinecone defines sub-lineages in low-variant bacterial populations using phylogenetic trees to improve outbreak and endemic disease analysis.

PopPUNK provides fast, scalable clustering of bacterial genomes using k-mer analysis, enabling real-time outbreak detection and stable cluster naming across species and datasets.

This study analyzes the genomes of Enterococcus faecium strains to understand their adaptive evolution, identifying plasmids as key indicators of host specificity through population-wide genomic analysis.

This paper uses Approximate Bayesian Computation to improve parameter estimation in stochastic birth-death models for disease outbreaks, showing better identifiability of key parameters like R and infectious population size.

This study identifies negative frequency-dependent selection in Escherichia coli ST131 as a key driver of its evolutionary success and adaptation to different ecological niches.

pyseer is a tool for microbial pangenome-wide association studies that integrates various methods, offers flexible input data handling, and provides new ways to interpret results.

SuperDCA enables genome-wide epistasis analysis in bacteria by efficiently modeling interactions among polymorphisms, revealing novel biological insights in Streptococcus pneumoniae.

Bacmeta simulates genomic evolution in bacterial metapopulations with adjustable connectivity and stochastic events, enabling large-scale simulations and likelihood-free inference.

This paper introduces genomeDCA, a statistical method to identify epistatic interactions in Streptococcus pneumoniae genomes, revealing networks of resistance, virulence, and core genes.

The paper introduces fastGEAR, an algorithm for detecting both recent and ancestral recombination in bacterial populations, demonstrating its effectiveness on Streptococcus pneumoniae genomes.

The paper analyzes genome sequences of Campylobacter jejuni isolates to assess genetic similarity and spatial-temporal patterns within a clonal complex, finding monomorphic clones with global dispersion potential.

This paper introduces SEER, a statistical method to identify sequence elements associated with bacterial phenotypes, demonstrating its application in discovering antibiotic resistance and invasiveness factors in Streptococcus species.

The paper assesses the identifiability of a tuberculosis transmission model using IS6110 fingerprinting data, showing that key parameters like reproductive value cannot be reliably inferred from the data.

The paper models how host metapopulation structure affects bacterial genetic diversity, showing migration can increase or decrease strain relatedness depending on microepidemic rates.