Computational protocol: Assembling networks of microbial genomes using linear programming

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[…] Simulated datasets were constructed using EvolSimulator 2.1.0 []. EvolSimulator allows replicated analyses to be carried out, with most parameters of a run including species tree topology and gene mutation and substitution parameters kept constant across multiple runs, but with variable rates and regimes of gene gain, gene loss, and LGT. Identical random number seeds and similar configuration files were used for all runs in a given replicate: for example, the '-s' and '-z' seeds which control stochastic events in gene and genome evolution respectively, were set to 1248706227 and 1649402786 for the first replicate. The configuration files used for all 11 EvolSimulator runs are available as supporting text in Additional File . Each simulation was performed for 2500 iterations, with the speciation/extinction probability set to 0.7. The maximum number of lineages was set to 50. Genomes in each run were assigned to one of five habitats, with the probability of habitat migration set to 0.0015 per iteration, with the consequence that most genomes had between 0 and 5 habitat changes in their history.All simulated genes had a length of 900 nucleotides, with an average mutation rate of 0.01 substitutions per site per iteration. In the noLGT-noLoss simulation, genome size was kept constant at 800 genes, whereas in the other 10 simulations genome size was allowed to drift (via gene gains, losses, and LGT where appropriate) between 500 and 1000 genes. Genome size could increase or decrease by a minimum of 0 and a maximum of 5 genes per iteration. Three underlying rates (25, 250, and 2500 LGT events per iteration) and three regimes (unconstrained, divergence-restricted, and habitat-restricted LGT) were combined to yield a total of nine runs with simulated LGT events. The rate of LGT specifies the average (drawn from a Poisson distribution) number of proposed LGT events between donor-recipient pairs for a given iteration: this is potentially problematic because all proposed events are successful in the unconstrained regime, whereas proposed events may be rejected due to excessive genome divergence or habitat incompatibility in the other two regimes. To avoid mismatches in the number of successful events for a given underlying rate of LGT, we used the '-retryLGT = 100' argument to EvolSimulator, which proposes another donor/recipient pair if a proposed LGT event is rejected due to regime-specific constraints, up to a maximum of 100 times. Consequently the number of LGT events should be comparable across regimes for a given fixed rate, but the distribution of donor/recipient pairs will differ. In the case of divergence-restricted LGT, the probability of success for a proposed transfer decreased linearly with increasing number of iterations separating two genomes (i.e., # of iterations since their last common ancestor) up to 500, and LGT forbidden between genomes whose common ancestor was > 500 generations in the past.Gene sequences and habitat histories were output from EvolSimulator in GenBank and FASTA-formatted files, which were used for the subsequent analyses described below. Node distances used in the treeness and habitat scores (see Results) were computed from Newick-formatted files using the Bio::Phylo library of BioPerl All statistical analyses were carried out using version 2.7.1 of the R software package 865 sequenced genomes used in this analysis were obtained from the National Center for Biotechnology Information (NCBI) via rsync on 28 November, 2008. The set included the genomes of 46 eukaryotes, 55 archaea and 764 bacteria. Conceptually translated open reading frames from each retrieved file were stored in a local relational database, and subjected to BLAST analysis as described in the following section. OGT data were retrieved from NCBI or from the primary literature if OGT data were unavailable at NCBI. [...] Simulated or predicted proteomes were subjected to all-versus-all BLASTP analysis using version 2.2.18 of the NCBI 'blastall' program. Parameters of the BLAST runs were chosen according to the recommendations of Moreno-Hagelsieb et al. [], particularly the soft filtering of low-information segments using the -F "m S" option. The BLOSUM62 matrix was used to score local alignments, with gap opening and extension costs of 1 during the dynamic programming phase. The default choice of composition-based score adjustments (-T) was used.All-versus-all BLASTP results were stored in database tables showing all query/subject pairs having an e-value of 0.001 or less. These were converted into the necessary format by identifying, for each gene Xi in genome X, the best-matching protein j in genome yj, for all y in the set of genomes Y. The resulting table of e-values α contained one row for each Xi, and one column for each yj, with αi,j the e-value of the best-matching protein. This matrix was transformed to matrix A as in equation (1). Any proteins that were identical across all genomes were removed from matrix A, since the presence of such proteins would lead to all possible LP solutions being equally good. This removal was performed on the Pseudomonas dataset; a total of 62 identical homologous sets of proteins were removed prior to the LP step.Matrix A was used to construct the constraints in a LP problem, either by dividing the rows by the row sums (rLP), applying a threshold number to the matrix (tLP-T), or by removing all dominated rows (ndLP). The constraints were implemented in the GNU MathProg modeling language, described by equation (5), where the comparison genomes were variables to be optimized. The LP problems were solved using GNU linear programming kit (GLPK: Edge weights of the genome networks were determined by the activity on the genomes from the GLPK solution files. The total activity for each test genome was constrained to 1. All graphs were visualized in Cytoscape 2.7.0 []. Different layout algorithms were used for different visualizations: EvolSimulator reference trees were organized using the hierarchical layout algorithm, graphs centered on A. aeolicus with a force-directed layout, and more-highly-connected graphs (thermophiles and Pseudomonas) with an organic layout. [...] We elected to use the RNA polymerase subunit beta (rpoB) gene as the reference for relationships among Pseudomonas genomes. rpoB has been used in such a way for other groups [,], and is expected to be less prone to LGT due to its participation in a multisubunit complex. Sixteen annotated rpoB gene sequences, one for each genome, were retrieved, and we retrieved the rpoB sequence from Cellvibrio japonicus, a member of the family Pseudomonadaceae, to serve as a putative outgroup. A multiple sequence alignment was built using FSA 1.14.5 [], with default parameters and the '-nucprot' flag to align conceptual translations of the rpoB genes, rather than the raw DNA sequence, in order to preserve the correct reading frame in all sequences. A phylogenetic tree was constructed with RAxML 7.2.5 [], with a general time reversible model of nucleotide substitution, eight discrete gamma rate categories, and an invariant category. One hundred fast bootstrap replicates were performed to assess the support for each internal node. The resulting tree was visualized using Dendroscope 2.7.4 []. […]

Pipeline specifications

Software tools EvolSimulator, Bio::Phylo, BioPerl, BLASTP, FSA, RAxML, Dendroscope
Applications Phylogenetics, Population genetic analysis
Organisms Aquifex aeolicus