De novogenomic analysis of Enterobacter asburiaeEBRJ12, a plant growth-promoting rhizobacteria isolated from the rhizosphere of Phaseolus vulgarisL

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Abstract

Aim

Environmental stresses such as water deficit induced stress are one of the major limiting factors in crop production. However, some plant growth-promoting rhizobacteria (PGPR) can promote plant growth in such adverse condition. Therefore, the objective was to isolate rhizospheric bacteria from Phaseolus vulgaris L. growing in a drought-affected soil and to analyze its plant growth promoting (PGP) efficacy to black gram (Vigna mungo L.) and Bhut jolokia (Capsicum chinense Jacq.). Whole-genome sequencing of the potential bacteria was targeted to analyze the genetic potential of the isolate as a plant growth-promoting agent.

Methods and results

The isolate Enterobacter asburiae EBRJ12 was selected based on its PGP efficacy, which significantly improved plant growth and development. The genomic analysis revealed the presence of one circular chromosome of size 4.8 Mb containing 16 genes for osmotic stress regulation including osmotically inducible protein osmY, outer membrane protein A precursor ompA, aquaporin Z, and an operon for osmoprotectant ABC transporter yehZYXW. Moreover, the genome has a complete genetic cluster for biosynthesis of siderophore Enterobactin and siderophore Aerobactin.

The PGP effects were verified with black gram and Bhut jolokia in pot experiments. The isolate significantly increased the shoot length by 35.0% and root length by 58.0% of black gram, while 41.0% and 57.0% of elevation in shoot and root length were observed in Bhut jolokia compared to non-inoculated plants.

Conclusions

The EBRJ12 has PGP features that could improve the growth in host plants, and the genomic characterization revealed the presence of genetic potential for plant growth promotion.

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RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

Alexandros Stamatakis (2014)

Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.

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MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.

K Katoh (2002)

A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.

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CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes

Donovan Parks, Michael Imelfort, Connor Skennerton … (2015)

Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of “marker” genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.