All sequences
were analyzed to assess HB composition. HBs were identified using the VarDom Server [8]. A gathering cut-off of 9.97 was used as the threshold to define a match. Linkage analysis of HBs in genomic sequences Linkage analysis was based on the linkage disequilibrium coefficient, D, among HBs within the 53 genomic isolates. The statistical significance for D values is determined by the method described in [26]. Where noted, D is normalized to account for the fact that D is maximized for intermediate frequency HBs (Additional file 1: P005091 order Figure S3). Normalization is done by dividing D by (pq(1-p)(1-q))2, where p and q are the frequencies of the two HBs being analyzed for linkage. HB expression rate The HB expression rate for a given isolate was defined as follows: the number of HBs of a certain type found within the expressed sequences of a given isolate (the expressed sequences consist of each unique Batimastat chemical structure expressed sequence represented as many times as it is found within that isolate), divided by the total number of expressed sequences for that isolate. Phenotype association networks For the purposes of creating phenotype association networks, we analyzed the 217 symptomatic isolates
within the dataset. For continuous phenotypes, we included in the network any significant correlation or rank correlation between a phenotype and an HB/var type expression Ganetespib manufacturer rate or PC (p ≤ 0.05). For binary phenotypes, we included all associations where the mean expression rate or PC was found to be significantly different for the two phenotypic states (p ≤ 0.05 by Friedman Rank, Kruskal-Wallis and/or K-Sample T, where each test is applied only when appropriate). HBs that are linked to similar phenotypes can be defined by analyzing networks in which HBs are connected Ferroptosis inhibitor by edges to the phenotypes with which their expression is correlated. We do not correct for multiple hypothesis tests in determining these edges because the conclusions are based
on the consideration of many edges taken together, and a more lenient threshold allows the network to capture a greater number of meaningful biological signals. Transformation of expression rates and rosetting level Prior to performing all linear and logistic regression analyses, the expression rates for particular var types (i.e., cys2, A-like, group 1, group 2, group 3, BS1/CP6 and H3sub var genes), the HB expression rates (i.e. for all 29 HBs), and the rosetting rates were transformed as described in [10]. The transformation (which is an arcsine transformation with special treatment for extreme values) is a standard method, and makes the data appropriate for fitting with regression models. Principal component analysis A PCA was carried out on a dataset of the HB expression rate profiles for the 217 symptomatic isolates.