2.19 was obtained from the NCBI BLAST website [45]. Using default parameters, blastp was used to align the wBm protein sequences against the protein sequences contained in DEG. To produce the multi-hit score, the selleck chemical negative log 10 of the e-values of the highest scoring alignments to each of the DEG organisms were normalized between 0 and 1, squared, then averaged for all DEG organisms. E-values greater than 1 were truncated at 1. Where N = the number of DEG organisms and 1 × 10-200 is the smallest e-value reported by BLAST. Jackknife Analysis Complete Refseq protein sequences for the 15 organisms contained within DEG were downloaded from the NCBI Refseq
ftp site ftp://ftp.ncbi.nlm.nih.gov/genomes/Bacteria. For each organism, a filtered version of DEG was prepared, removing just the
proteins from that organism. The full protein complement of that organism was then subjected to MHS analysis using the filtered version of DEG, and ranked based on MHS. Moving through the ranked genome from highest prediction of essentiality to lowest, the cumulative sum of DEG genes encountered was calculated. The area under the curve (AUC) of the cumulative sum describes the effectiveness of the ranking. The upper bound of the AUC is defined by an ideal sorting which places all see more DEG genes at the top of the list. The mean and standard deviation of the AUC for the null hypothesis of no sorting was determined by randomly permuting the genome sorting 1000 times. The AUCs for the random assortments Tau-protein kinase was assumed to represent a normal distribution with the observed mean and standard deviation. The p-value of the MHS sorting versus the null hypothesis was calculated using the probability density for a normal distribution. For the calculation of percent sorting, the AUC for the unsorted diagonal was one-half of the total area of the graph, calculated
as the total number genes in the genome multiplied by the number of DEG genes, divided by two. Gene Conservation Across Rickettsiales Refseq protein sequences were downloaded from the NCBI Refseq ftp site for the 27 sequenced organisms in the order Rickettsiales (Table 3). The standalone version 1.4 of the OrthoMCL ortholog prediction program was downloaded http://www.orthomcl.org/common/downloads/software/[38]. OrthoMCL was used with default settings and an inflation value of 1.5 to predict orthologs among the protein sequences of the 27 genomes. Briefly, OrthoMCL begins by using an all-versus-all BLAST search to identify learn more reciprocal best BLAST hits among the genomes as putative orthologs, and reciprocal best BLAST hits within genomes as putative in-paralogs. These interconnections are used to form a similarity graph that is used by the MCL clustering algorithm to break mega-clusters into suitable sub-clusters of orthologs [46]. For each cluster of orthologous genes the minimum spanning tree (MST) distance was calculated based on the phylogenetic distances among the member genomes.