We used the PanCGHweb web-tool to find presence/absence of OGs in these strains [37]. Visualizing and identifying presence or absence of a genomic segment Presence or absence of contiguously located genes (i.e. a gene cluster) in a query strain indicates that the whole genomic region encompassing these see more genes is present or absent in this particular strain. Therefore presence or absence of a genomic segment in a query strain compared to a reference strain was identified. To this end,

probes aligning to a genomic region of interest in a reference strain were identified. The log ratio of probe signals in a query strain to the reference strain was visualized to identify presence or absence of a genomic region in a query strain. Data AMN-107 purchase pre-processing In PhenoLink, genotype and phenotype data are pre-processed before using them in genotype-phenotype matching analysis.

PhenoLink is based on the Random Forest algorithm [38]. In random forest classification, trees are trained based on random selections of genes and strains, genes with the same occurrence pattern could get different contribution scores [39]. This score is an estimate of how important a gene is to correctly classify a certain strain. Additionally, genes that are either present or absent in (almost) all queried strains have negligible impacts to separate strains of differing phenotypes [40]. Thus we did not use genes with homogeneous occurrence patterns and used only one of the highly correlated genes in further analysis. Prior to classification, phenotypes with continuous measurements were grouped into 3 bins, where each bin represents a different category. Strains that belong to the middle category were not used in genotype-phenotype

matching to improve the classification accuracy. Additionally, in some experiments most of the strains exhibited a single phenotype such as the capability to grow on a certain sugar. Such an imbalance often leads to biased classification. 4-Aminobutyrate aminotransferase Therefore imbalance in the number of strains per phenotype was decreased by creating 100 bags [22]. Genotype-phenotype matching Genes related to phenotypes were identified using PhenoLink mostly with default parameter settings. To decrease effects of random selection, the same genotype and phenotype data were classified 3 times and only genes consistently relating to phenotypes were selected. Additionally, only genes with a positive contribution score for at least a few (in this study 3) strains of a phenotype were used for further classification, which decreases spurious relations between genes and phenotypes. This iterative removal of genes continued until no more than a few (in this study 5) genes were removed [22].