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Sugarcane (Saccharum officinarum, Poaceae) is cultivated on a large scale in (sub)tropical regions such as Brazil and has considerable economic value for sugar and biofuel production. The plant is a rich substrate for endo- and epiphytic fungi. Black yeasts in the family Herpotrichiellaceae (Chaetothyriales) are colonizers of human-dominated habitats, particularly those rich in toxins and hydrocarbon pollutants, and may cause severe infections in susceptible human hosts. The present study assessed the diversity of Herpotrichiellaceae associated with sugarcane, using in silico identification and selective isolation. Using metagenomics, we identified 5833 fungal sequences, while 639 black yeast-like isolates were recovered in vitro. In both strategies, the latter fungi were identified as members of the genera Cladophialophora, Exophiala, and Rhinocladiella (Herpotrichiellaceae), Cyphellophora (Cyphellophoraceae), and Knufia (Trichomeriaceae). In addition, we discovered new species of Cladophialophora and Exophiala from sugarcane and its rhizosphere. The first environmental isolation of Cladophialophora bantiana is particularly noteworthy, because this species up to now is exclusively known from the human host where it mostly causes fatal brain disease in otherwise healthy patients., (© 2023. International Mycological Association.)

Brazil was the epicenter of worldwide pandemics at the peak of its second wave. The genomic/proteomic perspective of the COVID-19 pandemic in Brazil could provide insights to understand the global pandemics behavior. In this study, we track SARS-CoV-2 molecular information in Brazil using real-time bioinformatics and data science strategies to provide a comparative and evolutive panorama of the lineages in the country. SWeeP vectors represented the Brazilian and worldwide genomic/proteomic data from Global Initiative on Sharing Avian Influenza Data (GISAID) between February 2020 and August 2021. Clusters were analyzed and compared with PANGO lineages. Hierarchical clustering provided phylogenetic and evolutionary analyses of the lineages, and we tracked the P.1 (Gamma) variant origin. The genomic diversity based on Chao's estimation allowed us to compare richness and coverage among Brazilian states and other representative countries. We found that epidemics in Brazil occurred in two moments with different genetic profiles. The P.1 lineages emerged in the second wave, which was more aggressive. We could not trace the origin of P.1 from the variants present in Brazil. Instead, we found evidence pointing to its external source and a possible recombinant event that may relate P.1 to a B.1.1.28 variant subset. We discussed the potential application of the pipeline for emerging variants detection and the PANGO terminology stability over time. The diversity analysis showed that the low coverage and unbalanced sequencing among states in Brazil could have allowed the silent entry and dissemination of P.1 and other dangerous variants. This study may help to understand the development and consequences of variants of concern (VOC) entry., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Perico, De Pierri, Neto, Fernandes, Pedrosa, de Souza and Raittz.)

Aeromonas are Gram-negative rods widely distributed in the environment. They can cause severe infections in fish related to financial losses in the fish industry, and are considered opportunistic pathogens of humans causing infections ranging from diarrhea to septicemia. The objective of this study was to determine in silico the contribution of genomic islands to A. hydrophila . The complete genomes of 17 A. hydrophila isolates, which were separated into two phylogenetic groups, were analyzed using a genomic island (GI) predictor. The number of predicted GIs and their characteristics varied among strains. Strains from group 1, which contains mainly fish pathogens, generally have a higher number of predicted GIs, and with larger size, than strains from group 2 constituted by strains recovered from distinct sources. Only a few predicted GIs were shared among them and contained mostly genes from the core genome. Features related to virulence, metabolism, and resistance were found in the predicted GIs, but strains varied in relation to their gene content. In strains from group 1, O Ag biosynthesis clusters OX1 and OX6 were identified, while strains from group 2 each had unique clusters. Metabolic pathways for myo-inositol, L-fucose, sialic acid, and a cluster encoding QueDEC, tgtA5, and proteins related to DNA metabolism were identified in strains of group 1, which share a high number of predicted GIs. No distinctive features of group 2 strains were identified in their predicted GIs, which are more diverse and possibly better represent GIs in this species. However, some strains have several resistance attributes encoded by their predicted GIs. Several predicted GIs encode hypothetical proteins and phage proteins whose functions have not been identified but may contribute to Aeromonas fitness. In summary, features with functions identified on predicted GIs may confer advantages to host colonization and competitiveness in the environment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 da Silva Filho, Marchaukoski, Raittz, De Pierri, de Jesus Soares Machado, Fadel-Picheth and Picheth.)

Nonulosonic acids (NulOs) are a group of nine-carbon monosaccharides with different functions in nature. N -acetylneuraminic acid (Neu5Ac) is the most common NulO. It covers the membrane surface of all human cells and is a central molecule in the process of self-recognition via SIGLECS receptors. Some pathogenic bacteria escape the immune system by copying the sialylation of the host cell membrane. Neu5Ac production in these bacteria is catalysed by the enzyme NeuB. Some bacteria can also produce other NulOs named pseudaminic and legionaminic acids, through the NeuB homologues PseI and LegI, respectively. In Opisthokonta eukaryotes, the biosynthesis of Neu5Ac is catalysed by the enzyme NanS. In this study, we used publicly available data of sequences of NulOs synthases to investigate its distribution within the three domains of life and its relationship with pathogenic bacteria. We mined the KEGG database and found 425 NeuB sequences. Most NeuB sequences (58.74 %) from the KEGG orthology database were classified as from environmental bacteria; however, sequences from pathogenic bacteria showed higher conservation and prevalence of a specific domain named SAF. Using the HMM profile we identified 13 941 NulO synthase sequences in UniProt. Phylogenetic analysis of these sequences showed that the synthases were divided into three main groups that can be related to the lifestyle of these bacteria: (I) predominantly environmental, (II) intermediate and (III) predominantly pathogenic. NeuB was widely distributed in the groups. However, LegI and PseI were more concentrated in groups II and III, respectively. We also found that PseI appeared later in the evolutionary process, derived from NeuB. We use this same methodology to retrieve sialic acid synthase sequences from Archaea and Eukarya. A large-scale phylogenetic analysis showed that while the Archaea sequences are spread across the tree, the eukaryotic NanS sequences were grouped in a specific branch in group II. None of the bacterial NanS sequences grouped with the eukaryotic branch. The analysis of conserved residues showed that the synthases of Archaea and Eukarya present a mutation in one of the three catalytic residues, an E134D change, related to a Neisseria meningitidis reference sequence. We also found that the conservation profile is higher between NeuB of pathogenic bacteria and NanS of eukaryotes than between NeuB of environmental bacteria and NanS of eukaryotes. Our large-scale analysis brings new perspectives on the evolution of NulOs synthases, suggesting their presence in the last common universal ancestor.

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.

Melanized fungi and black yeasts in the family Herpotrichiellaceae (order Chaetothyriales) are important agents of human and animal infectious diseases such as chromoblastomycosis and phaeohyphomycosis. The oligotrophic nature of these fungi enables them to survive in adverse environments where common saprobes are absent. Due to their slow growth, they lose competition with common saprobes, and therefore isolation studies yielded low frequencies of clinically relevant species in environmental habitats from which humans are thought to be infected. This problem can be solved with metagenomic techniques which allow recognition of microorganisms independent from culture. The present study aimed to identify species of the family Herpotrichiellaceae that are known to occur in Brazil by the use of molecular markers to screen public environmental metagenomic datasets from Brazil available in the Sequence Read Archive (SRA). Species characterization was performed with the BLAST comparison of previously described barcodes and padlock probe sequences. A total of 18,329 sequences was collected comprising the genera Cladophialophora, Exophiala, Fonsecaea, Rhinocladiella and Veronaea, with a focus on species related to the chromoblastomycosis. The data obtained in this study demonstrated presence of these opportunists in the investigated datasets. The used techniques contribute to our understanding of environmental occurrence and epidemiology of black fungi.

Vectoral and alignment-free approaches to biological sequence representation have been explored in bioinformatics to efficiently handle big data. Even so, most current methods involve sequence comparisons via alignment-based heuristics and fail when applied to the analysis of large data sets. Here, we present "Spaced Words Projection (SWeeP)", a method for representing biological sequences using relatively small vectors while preserving intersequence comparability. SWeeP uses spaced-words by scanning the sequences and generating indices to create a higher-dimensional vector that is later projected onto a smaller randomly oriented orthonormal base. We constructed phylogenetic trees for all organisms with mitochondrial and bacterial protein data in the NCBI database. SWeeP quickly built complete and accurate trees for these organisms with low computational cost. We compared SWeeP to other alignment-free methods and Sweep was 10 to 100 times quicker than the other techniques. A tool to build SWeeP vectors is available at https://sourceforge.net/projects/spacedwordsprojection/.

Background: Herbaspirillum seropedicae is an environmental β-proteobacterium that is capable of promoting the growth of economically relevant plants through biological nitrogen fixation and phytohormone production. However, strains of H. seropedicae have been isolated from immunocompromised patients and associated with human infections and deaths. In this work, we sequenced the genomes of two clinical strains of H. seropedicae, AU14040 and AU13965, and compared them with the genomes of strains described as having an environmental origin., Results: Both genomes were closed, indicating a single circular chromosome; however, strain AU13965 also carried a plasmid of 42,977 bp, the first described in the genus Herbaspirillum. Genome comparison revealed that the clinical strains lost the gene sets related to biological nitrogen fixation (nif) and the type 3 secretion system (T3SS), which has been described to be essential for interactions with plants. Comparison of the pan-genomes of clinical and environmental strains revealed different sets of accessorial genes. However, antimicrobial resistance genes were found in the same proportion in all analyzed genomes. The clinical strains also acquired new genes and genomic islands that may be related to host interactions. Among the acquired islands was a cluster of genes related to lipopolysaccharide (LPS) biosynthesis. Although highly conserved in environmental strains, the LPS biosynthesis genes in the two clinical strains presented unique and non-orthologous genes within the genus Herbaspirillum. Furthermore, the AU14040 strain cluster contained the neuABC genes, which are responsible for sialic acid (Neu5Ac) biosynthesis, indicating that this bacterium could add it to its lipopolysaccharide. The Neu5Ac-linked LPS could increase the bacterial resilience in the host aiding in the evasion of the immune system., Conclusions: Our findings suggest that the lifestyle transition from environment to opportunist led to the loss and acquisition of specific genes allowing adaptations to colonize and survive in new hosts. It is possible that these substitutions may be the starting point for interactions with new hosts.

Background: Clustering methods are essential to partitioning biological samples being useful to minimize the information complexity in large datasets. Tools in this context usually generates data with greed algorithms that solves some Data Mining difficulties which can degrade biological relevant information during the clustering process. The lack of standardization of metrics and consistent bases also raises questions about the clustering efficiency of some methods. Benchmarks are needed to explore the full potential of clustering methods - in which alignment-free methods stand out - and the good choice of dataset makes it essentials., Results: Here we present a new approach to Data Mining in large protein sequences datasets, the Rapid Alignment Free Tool for Sequences Similarity Search to Groups (RAFTS 3 G), a method to clustering aiming of losing less biological information in the processes of generation groups. The strategy developed in our algorithm is optimized to be more astringent which reflects increase in accuracy and sensitivity in the generation of clusters in a wide range of similarity. RAFTS 3 G is the better choice compared to three main methods when the user wants more reliable result even ignoring the ideal threshold to clustering., Conclusion: In general, RAFTS 3 G is able to group up to millions of biological sequences into large datasets, which is a remarkable option of efficiency in clustering. RAFTS 3 G compared to other "standard-gold" methods in the clustering of large biological data maintains the balance between the reduction of biological information redundancy and the creation of consistent groups. We bring the binary search concept applied to grouped sequences which shows maintaining sensitivity/accuracy relation and up to minimize the time of data generated with RAFTS 3 G process.

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.

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 techniques) 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.

Fonsecaea and Cladophialophora are genera of black yeast-like fungi harboring agents of a mutilating implantation disease in humans, along with strictly environmental species. The current hypothesis suggests that those species reside in somewhat adverse microhabitats, and pathogenic siblings share virulence factors enabling survival in mammal tissue after coincidental inoculation driven by pathogenic adaptation. A comparative genomic analysis of environmental and pathogenic siblings of Fonsecaea and Cladophialophora was undertaken, including de novo assembly of F. erecta from plant material. The genome size of Fonsecaea species varied between 33.39 and 35.23 Mb, and the core genomes of those species comprises almost 70% of the genes. Expansions of protein domains such as glyoxalases and peptidases suggested ability for pathogenicity in clinical agents, while the use of nitrogen and degradation of phenolic compounds was enriched in environmental species. The similarity of carbohydrate-active vs. protein-degrading enzymes associated with the occurrence of virulence factors suggested a general tolerance to extreme conditions, which might explain the opportunistic tendency of Fonsecaea sibling species. Virulence was tested in the Galleria mellonella model and immunological assays were performed in order to support this hypothesis. Larvae infected by environmental F. erecta had a lower survival. Fungal macrophage murine co-culture showed that F. erecta induced high levels of TNF-α contributing to macrophage activation that could increase the ability to control intracellular fungal growth although hyphal death were not observed, suggesting a higher level of extremotolerance of environmental species.

Nitrogen is needed for the biosynthesis of biomolecules including proteins and nucleic acids. In the absence of fixed nitrogen prokaryotes such as E. coli immediately ceases growth. Ammonium is the preferred nitrogen source for E. coli supporting the fastest growth rates. Under conditions of ammonium limitation, E. coli can use alternative nitrogen sources to supply ammonium ions and this reprogramming is led by the induction of the NtrC regulon. Here we used label free proteomics to determine the dynamics of E. coli proteins expression in response to ammonium starvation in both the short (30min) and the longer (60min) starvation. Protein abundances and post-translational modifications confirmed that activation of the NtrC regulon acts as the first line of defense against nitrogen starvation. The ribosome inactivating protein Rmf was induced shortly after ammonium exhaustion and this was preceded by induction of other ribosome inactivating proteins such as Hpf and RaiA supporting the hypothesis that ribosome shut-down is a key process during nitrogen limitation stress. The proteomic data revealed that growth arrest due to nitrogen starvation correlates with the accumulation of proteins involved in DNA condensation, RNA and protein catabolism and ribosome hibernation. Collectively, these proteome adaptations will result in metabolic inactive cells which are likely to exhibit multidrug tolerance., (Copyright © 2016 Elsevier B.V. All rights reserved.)

A draft genome sequence of type strain Fonsecaea multimorphosa CBS 980.96 T was obtained. This species was first isolated from a cat with cerebral phaeohyphomycosis in Queensland, Australia., (Copyright © 2017 Leao et al.)

Background: Azopirillum brasilense is a plant-growth promoting nitrogen-fixing bacteria that is used as bio-fertilizer in agriculture. Since nitrogen fixation has a high-energy demand, the reduction of N 2 to NH 4 + by nitrogenase occurs only under limiting conditions of NH 4 + and O 2 . Moreover, the synthesis and activity of nitrogenase is highly regulated to prevent energy waste. In A. brasilense nitrogenase activity is regulated by the products of draG and draT. The product of the draB gene, located downstream in the draTGB operon, may be involved in the regulation of nitrogenase activity by an, as yet, unknown mechanism., Results: A deep in silico analysis of the product of draB was undertaken aiming at suggesting its possible function and involvement with DraT and DraG in the regulation of nitrogenase activity in A. brasilense. In this work, we present a new artificial intelligence strategy for protein classification, named ProClaT. The features used by the pattern recognition model were derived from the primary structure of the DraB homologous proteins, calculated by a ProClaT internal algorithm. ProClaT was applied to this case study and the results revealed that the A. brasilense draB gene codes for a protein highly similar to the nitrogenase associated NifO protein of Azotobacter vinelandii., Conclusions: This tool allowed the reclassification of DraB/NifO homologous proteins, hypothetical, conserved hypothetical and those annotated as putative arsenate reductase, ArsC, as NifO-like. An analysis of co-occurrence of draB, draT, draG and of other nif genes was performed, suggesting the involvement of draB (nifO) in nitrogen fixation, however, without the definition of a specific function.

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/.

On the basis of multilocus phylogenetic data, Fonsecaea nubica was described in 2010 as a molecular sibling of F. monophora, an established agent of the human skin disease chomoblastomycosis in tropical zones. Genome analysis of these pathogens is mandatory to identify genes involved in the interaction with host and virulence., (Copyright © 2016 Costa et al.)

The black yeast Fonsecaea monophora is one of the main etiologic agents of chromoblastomycosis in humans. Its pathogenicity profile is more invasive than that of related Fonsecaea species, causing brain infection in addition to (sub)cutaneous infections., (Copyright © 2016 Bombassaro et al.)

Fast prediction of protein function is essential for high-throughput sequencing analysis. Bioinformatic resources provide cheaper and faster techniques for function prediction and have helped to accelerate the process of protein sequence characterization. In this study, we assessed protein function prediction programs that accept amino acid sequences as input. We analyzed the classification, equality, and similarity between programs, and, additionally, compared program performance. The following programs were selected for our assessment: Blast2GO, InterProScan, PANTHER, Pfam, and ScanProsite. This selection was based on the high number of citations (over 500), fully automatic analysis, and the possibility of returning a single best classification per sequence. We tested these programs using 12 gold standard datasets from four different sources. The gold standard classification of the databases was based on expert analysis, the Protein Data Bank, or the Structure-Function Linkage Database. We found that the miss rate among the programs is globally over 50%. Furthermore, we observed little overlap in the correct predictions from each program. Therefore, a combination of multiple types of sources and methods, including experimental data, protein-protein interaction, and data mining, may be the best way to generate more reliable predictions and decrease the miss rate.

Leptospirosis is caused by pathogenic bacteria of the genus Leptospira spp. This neglected re-emergent disease has global distribution and relevance in veterinary production. Here, we report the whole-genome sequence and annotation of Leptospira interrogans serovar Hardjo subtype Hardjoprajitno strain Norma, isolated from cattle in a livestock leptospirosis outbreak in Brazil., (Copyright © 2015 Cosate et al.)

We report the complete genome sequence of Herbaspirillum hiltneri N3 (DSM 17495), a member of the genus Herbaspirillum of the Betaproteobacteria. The genome is contained in a single chromosome, and analysis revealed that N3 lacks the whole nitrogen fixation (nif) gene cluster, confirming its inability to fix nitrogen., (Copyright © 2015 Guizelini et al.)

There are several guidelines for gene nomenclature, but they are not always applied to the names of newly identified genes. The lack of standardization in naming genes generates inconsistent databases with errors such as genes with the same function and different names, genes with different functions and the same name, and use of an abbreviated name. This paper presents a methodology for predicting synonyms in a given gene nomenclature, thereby detecting and minimizing naming redundancy and inconsistency and facilitating the annotation of new genes and data mining in public databases. To identify gene synonyms, i.e., gene ambiguity, the methodology proposed begins by grouping genes according to their names using a Kohonen self-organizing map artificial neural network. Afterwards, it identifies the groups generated employing the Matrix-U technique. The employment of such techniques allows one to infer the synonyms of genes, to predict probable hypothetical gene names and to point out possible errors in a database record. Many mistakes related to gene nomenclature were detected in this research, demonstrating the importance of predicting synonyms. The methodology developed is applicable for describing hypothetical, putative and other types of genes without a known function. Moreover, it can also indicate a possible function for genes after grouping them.

Background: 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., Findings: We have developed FGAP, a tool for closing gaps of draft genome sequences that takes advantage of different datasets. FGAP uses BLAST to align multiple contigs against a draft genome assembly aiming to find sequences that overlap gaps. The algorithm selects the best sequence to fill and eliminate the gap., Conclusions: FGAP reduced the number of gaps by 78% in an E. coli draft genome assembly using two different sequencing technologies, Illumina and 454. Using PacBio long reads, 98% of gaps were solved. In human chromosome 14 assemblies, FGAP reduced the number of gaps by 35%. All the inserted sequences were validated with a reference genome using QUAST. The source code and a web tool are available at http://www.bioinfo.ufpr.br/fgap/.

Here we report the one-scaffold draft genome of Herbaspirillum huttiense subsp. putei strain 7-2 T (IAM 15032), which was isolated from well water.

Herbaspirillum lusitanum strain P6-12 (DSM 17154) is, so far, the only species of Herbaspirillum isolated from plant root nodules. Here we report a draft genome sequence of this organism.

The draft sequence of the genome of Bradyrhizobium elkanii 587 is presented. This was obtained using Illumina Next-Gen DNA sequencing combined with Sanger sequencing. Genes for the pathways involved in biological nitrogen fixation (the nif gene cluster), nod genes including nodABC, and genes for the type III protein secretion system (T3SS) are present.

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., Competing Interests: The authors have declared that no competing interests exist.