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      Aquibacillus rhizosphaerae sp. nov., an Indole Acetic Acid (IAA)-producing Halotolerant Bacterium Isolated from the Rhizosphere Soil of Kalidium cuspidatum

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      Current Microbiology
      Springer Science and Business Media LLC

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          Abstract

          A bacterium (named strain LR5S19T) was isolated from the rhizosphere soil of the halophyte Kalidium cuspidatum in Baotou, Inner Mongolia, China. Strain LR5S19T was Gram-stain-positive, motile with a polar flagellum, rod shaped, and spore forming at the terminal position in swollen sporangia, and it grew at 10-40 ℃ (optimum 30 ℃), pH 6.0-9.0 (optimum pH 7.0), and in the presence of 1.0-15.0% (w/v) NaCl (optimum 2.0%). The phylogenetic analysis of the 16S rRNA gene showed that strain LR5S19T shared the highest similarity (96.7%) with A. koreensis JCM 12387T, followed by A. kalidii HU2P27T (96.2%), A. sediminis BH258T (96.1%), and 'A. salsiterrae' 3ASR75-54T (96.0%). The ANIb, AAI and dDDH values between strain LR5S19T and its closely related type strains were 69.3-73.8%, 65.4-72.4% and 19.2-20.3%, respectively. The major polar lipids in strain LR5S19T consisted of diphosphatidylglycerol, phosphatidylglycerol, and three unidentified phospholipids, while MK-7 was the major respiratory quinone. The major fatty acids of the strain were anteiso-C15:0 and iso-C15:0. Based on phylogenomic and phenotypic results, strain LR5S19T should be classified as a novel species within the genus Aquibacillus, for which Aquibacillus rhizosphaerae sp. nov. is proposed. The type strain is LR5S19T (= CGMCC 1.62028T = KCTC 43434T). The comparative genomic analysis revealed that all eight members of Aquibacillus could utilize D-glucose via the glycolysis-gluconeogenesis pathway or the pentose phosphate pathway and use the tricarboxylic acid cycle as the metabolic center. The potassium ion transport proteins and compatible solute synthesis pathways in all the members likely also help them cope with hypersaline environments.

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          MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.

          We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.
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            CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes

            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.
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              Prodigal: prokaryotic gene recognition and translation initiation site identification

              Background The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals. Results With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives. Conclusion We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/. Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.
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                Author and article information

                Journal
                Current Microbiology
                Curr Microbiol
                Springer Science and Business Media LLC
                0343-8651
                1432-0991
                December 2023
                November 06 2023
                December 2023
                : 80
                : 12
                Article
                10.1007/s00284-023-03543-2
                37930394
                d9b18530-685f-4ecf-b9f0-9bb790b6d351
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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