MLSTar: automatic multilocus sequence typing of bacterial genomes in R.

Multilocus sequence typing (MLST) is a standard tool in population genetics and bacterial epidemiology that assesses the genetic variation present in a reduced number of housekeeping genes (typically seven) along the genome. This methodology assigns arbitrary integer identifiers to genetic variations at these loci which allows us to efficiently compare bacterial isolates using allele-based methods. Now, the increasing availability of whole-genome sequences for hundreds to thousands of strains from the same bacterial species has allowed us to apply and extend MLST schemes by automatic extraction of allele information from the genomes. The PubMLST database is the most comprehensive resource of described schemes available for a wide variety of species. Here we present MLSTar as the first R package that allows us to (i) connect with the PubMLST database to select a target scheme, (ii) screen a desired set of genomes to assign alleles and sequence types, and (iii) interact with other widely used R packages to analyze and produce graphical representations of the data. We applied MLSTar to analyze more than 2,500 bacterial genomes from different species, showing great accuracy, and comparable performance with previously published command-line tools. MLSTar can be freely downloaded from http://github.com/iferres/MLSTar.

Draft genomes and reference transcriptomes extend the coding potential of the fish pathogen Piscirickettsia salmonis

Background: Draft and complete genome sequences from bacteria are key tools to understand genetic determinants involved in pathogenesis in several disease models. Piscirickettsia salmonis is a Gram-negative bacterium responsible for the Salmon Rickettsial Syndrome (SRS), a bacterial disease that threatens the sustainability of the Chilean salmon industry. In previous reports, complete and draft genome sequences have been generated and annotated. However, the lack of transcriptome data underestimates the genetic potential, does not provide information about transcriptional units and contributes to disseminate annotation errors. Results: Here we present the draft genome and transcriptome sequences of four P. salmonis strains. We have identified the transcriptional architecture of previously characterized virulence factors and trait-specific genes associated to cation uptake, metal efflux, antibiotic resistance, secretion systems and other virulence factors. Conclusions: This data has provided a refined genome annotation and also new insights on the transcriptional structures and coding potential of this fish pathogen.

Bacterial genomes lacking long-range correlations may not be modeled by low-order Markov chains: the role of mixing statistics and frame shift of neighboring genes.

We examine the relationship between exponential correlation functions and Markov models in a bacterial genome in detail. Despite the well known fact that Markov models generate sequences with correlation function that decays exponentially, simply constructed Markov models based on nearest-neighbor dimer (first-order), trimer (second-order), up to hexamer (fifth-order), and treating the DNA sequence as being homogeneous all fail to predict the value of exponential decay rate. Even reading-frame-specific Markov models (both first- and fifth-order) could not explain the fact that the exponential decay is very slow. Starting with the in-phase coding-DNA-sequence (CDS), we investigated correlation within a fixed-codon-position subsequence, and in artificially constructed sequences by packing CDSs with out-of-phase spacers, as well as altering CDS length distribution by imposing an upper limit. From these targeted analyses, we conclude that the correlation in the bacterial genomic sequence is mainly due to a mixing of heterogeneous statistics at different codon positions, and the decay of correlation is due to the possible out-of-phase between neighboring CDSs. There are also small contributions to the correlation from bases at the same codon position, as well as by non-coding sequences. These show that the seemingly simple exponential correlation functions in bacterial genome hide a complexity in correlation structure which is not suitable for a modeling by Markov chain in a homogeneous sequence. Other results include: use of the (absolute value) second largest eigenvalue to represent the 16 correlation functions and the prediction of a 10-11 base periodicity from the hexamer frequencies.

Buffet hypothesis for microbial nutrition at the rhizosphere.

An emphasis is made on the diversity of nutrients that rhizosphere bacteria may encounter derived from roots, soil, decaying organic matter, seeds, or the microbial community. This nutrient diversity may be considered analogous to a buffet and is contrasting to the hypothesis of oligotrophy at the rhizosphere. Different rhizosphere bacteria may have preferences for some substrates and this would allow a complex community to be established at the rhizosphere. To profit from diverse nutrients, root-associated bacteria should have large degrading capabilities and many transporters (seemingly inducible) that may be encoded in a significant proportion of the large genomes that root-associated bacteria have. Rhizosphere microbes may have a tendency to evolve toward generalists. We propose that many genes with unknown function may encode enzymes that participate in degrading diverse rhizosphere substrates. Knowledge of bacterial genes required for nutrition at the rhizosphere will help to make better use of bacteria as plant-growth promoters in agriculture.