The molecular mechanisms of plant recognition, colonization, and nutrient exchange between diazotrophic endophytes and plants are scarcely known. Herbaspirillum seropedicae is an endophytic bacterium capable of colonizing intercellular spaces of grasses such as rice and sugar cane. The genome of H. seropedicae strain SmR1 was sequenced and annotated by The Paraná State Genome Programme—GENOPAR. The genome is composed of a circular chromosome of 5,513,887 bp and contains a total of 4,804 genes. The genome sequence revealed that H. seropedicae is a highly versatile microorganism with capacity to metabolize a wide range of carbon and nitrogen sources and with possession of four distinct terminal oxidases. The genome contains a multitude of protein secretion systems, including type I, type II, type III, type V, and type VI secretion systems, and type IV pili, suggesting a high potential to interact with host plants. H. seropedicae is able to synthesize indole acetic acid as reflected by the four IAA biosynthetic pathways present. A gene coding for ACC deaminase, which may be involved in modulating the associated plant ethylene-signaling pathway, is also present. Genes for hemagglutinins/hemolysins/adhesins were found and may play a role in plant cell surface adhesion. These features may endow H. seropedicae with the ability to establish an endophytic life-style in a large number of plant species. In this work we describe the genome of H. seropedicae SmR1, a bacterium capable of fixing nitrogen and promoting the growth of important plant crops such as maize, rice, and sugar cane. Several investigations have shown that H. seropedicae supplies fixed nitrogen to the associated plant and increases grain productivity up to 50%. In the genome of H. seropedicae, we identified all the genes involved in the nitrogen fixation process and its regulation and, in addition, genes potentially involved in the establishment of efficient interaction with the host plant. Our analyses also revealed that this bacterium has a highly versatile metabolism capable of synthesizing and degrading a large number of organic and inorganic compounds. We believe that the knowledge of the genome of this bacterium will direct research to a better understanding of this important endophytic organism and allow the construction of new strains with enhanced agronomic efficiency.

Nowadays defying homology relationships among sequences is essential for biological research. Within homology the analysis of orthologs sequences is of great importance for computational biology, annotation of genomes and for phylogenetic inference. Since 2007, with the increase in the number of new sequences being deposited in large biological databases, researchers have begun to analyse computerized methodologies and tools aimed at selecting the most promising ones in the prediction of orthologous groups. Literature in this field of research describes the problems that the majority of available tools show, such as those encountered in accuracy, time required for analysis (especially in light of the increasing volume of data being submitted, which require faster techiques) and the automatization of the process without requiring manual intervention. Conducting our search through BMC, Google Scholar, NCBI PubMed and Expasy, we examined more than 600 articles pursuing the most recent techniques and tools developed to solve most the problems still existing in orthology detection. We listed the main computational tools created and developed between 2011 and 2017, taking into consideration the differences in the type of orthology analysis, outlining the main features of each tool and pointing to the problems that each one tries to address. We also observed that several tools still use as their main algorithm the BLAST ‘‘all-against-all’’ methodology, which entails some limitations, such as limited number of queries, computational cost and high processing time to complete the analysis. However, new promising tools are being developed, like OrthoVenn (which uses the Venn diagram to show the relationship of ortholog groups generated by its algorithm); or proteinOrtho (which improves the accuracy of ortholog groups); or ReMark (tackling the integration of the pipeline to turn the entry process automatic); or OrthAgogue (using algorithms developed to minimize processing time); and proteinOrtho (developed for dealing with large amounts of biological data). We made a comparison among the main features of four tool and tested them using four for prokaryotic genomas. We hope that our review can be useful for researchers and will help them in selecting the most appropriate tool for their work in the field of orthology.

The fast reduction of prices of DNA sequencing allowed rapid accumulation of genome data. However, the process of obtaining complete genome sequences is still very time consuming and labor demanding. In addition, data produced from various sequencing technologies or alternative assemblies remain underexplored to improve assembly of incomplete genome sequences.

Despite the development in DNA sequencing technology, improving the number and the length of reads, the process of reconstruction of complete genome sequences, the so called genome assembly, is still complex. Only 13% of the prokaryotic genome sequencing projects have been completed. Draft genome sequences deposited in public databases are fragmented in contigs and may lack the full gene complement. The aim of the present work is to identify assembly errors and improve the assembly process of bacterial genomes. The biological patterns observed in genomic sequences and the application of a priori information can allow the identification of misassembled regions, and the reorganization and improvement of the overall de novo genome assembly. GFinisher starts generating a Fuzzy GC skew graphs for each contig in an assembly and follows breaking down the contigs in critical points in order to reassemble and close them using jFGap. This has been successfully applied to dataset from 96 genome assemblies, decreasing the number of contigs by up to 86%. GFinisher can easily optimize assemblies of prokaryotic draft genomes and can be used to improve the assembly programs based on nucleotide sequence patterns in the genome. The software and source code are available at http://gfinisher.sourceforge.net/.

Tools for genomic island prediction use strategies for genomic comparison analysis and sequence composition analysis. The goal of comparative analysis is to identify unique regions in the genomes of related organisms, whereas sequence composition analysis evaluates and relates the composition of specific regions with other regions in the genome. The goal of this study was to qualitatively and quantitatively evaluate extant genomic island predictors. We chose tools reported to produce significant results using sequence composition prediction, comparative genomics, and hybrid genomics methods. To maintain diversity, the tools were applied to eight complete genomes of organisms with distinct characteristics and belonging to different families. Escherichia coli CFT073 was used as a control and considered as the gold standard because its islands were previously curated in vitro. The results of predictions with the gold standard were manually curated, and the content and characteristics of each predicted island were analyzed. For other organisms, we created GenBank (GBK) files using Artemis software for each predicted island. We copied only the amino acid sequences from the coding sequence and constructed a multi-FASTA file for each predictor. We used BLASTp to compare all results and generate hits to evaluate similarities and differences among the predictions. Comparison of the results with the gold standard revealed that GIPSy produced the best results, covering ~91% of the composition and regions of the islands, followed by Alien Hunter (81%), IslandViewer (47.8%), Predict Bias (31%), GI Hunter (17%), and Zisland Explorer (16%). The tools with the best results in the analyzes of the set of organisms were the same ones that presented better performance in the tests with the gold standard.

Among other attributes, the Betaproteobacterial genus Azoarcus has biotechnological importance for plant growth-promotion and remediation of petroleum waste-polluted water and soils. It comprises at least two phylogenetically distinct groups. The “plant-associated” group includes strains that are isolated from the rhizosphere or root interior of the C4 plant Kallar Grass, but also strains from soil and/or water; all are considered to be obligate aerobes and all are diazotrophic. The other group (now partly incorporated into the new genus Aromatoleum) comprises a diverse range of species and strains that live in water or soil that is contaminated with petroleum and/or aromatic compounds; all are facultative or obligate anaerobes. Some are diazotrophs. A comparative genome analysis of 32 genomes from 30 Azoarcus-Aromatoleum strains was performed in order to delineate generic boundaries more precisely than the single gene, 16S rRNA, that has been commonly used in bacterial taxonomy. The origin of diazotrophy in Azoarcus-Aromatoleum was also investigated by comparing full-length sequences of nif genes, and by physiological measurements of nitrogenase activity using the acetylene reduction assay. Based on average nucleotide identity (ANI) and whole genome analyses, three major groups could be discerned: (i) Azoarcus comprising Az. communis, Az. indigens and Az. olearius, and two unnamed species complexes, (ii) Aromatoleum Group 1 comprising Ar. anaerobium, Ar. aromaticum, Ar. bremense, and Ar. buckelii, and (iii) Aromatoleum Group 2 comprising Ar. diolicum, Ar. evansii, Ar. petrolei, Ar. toluclasticum, Ar. tolulyticum, Ar. toluolicum, and Ar. toluvorans. Single strain lineages such as Azoarcus sp. KH32C, Az. pumilus, and Az. taiwanensis were also revealed. Full length sequences of nif-cluster genes revealed two groups of diazotrophs in Azoarcus-Aromatoleum with nif being derived from Dechloromonas in Azoarcus sensu stricto (and two Thauera strains) and from Azospira in Aromatoleum Group 2. Diazotrophy was confirmed in several strains, and for the first time in Az. communis LMG5514, Azoarcus sp. TTM-91 and Ar. toluolicum T^T. In terms of ecology, with the exception of a few plant-associated strains in Azoarcus (s.s.), across the group, most strains/species are found in soil and water (often contaminated with petroleum or related aromatic compounds), sewage sludge, and seawater. The possession of nar, nap, nir, nor, and nos genes by most Azoarcus-Aromatoleum strains suggests that they have the potential to derive energy through anaerobic nitrate respiration, so this ability cannot be usefully used as a phenotypic marker to distinguish genera. However, the possession of bzd genes indicating the ability to degrade benzoate anaerobically plus the type of diazotrophy (aerobic vs. anaerobic) could, after confirmation of their functionality, be considered as distinguishing phenotypes in any new generic delineations. The taxonomy of the Azoarcus-Aromatoleum group should be revisited; retaining the generic name Azoarcus for its entirety, or creating additional genera are both possible outcomes.