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Completion of eukaryal genomes can be difficult task with the highly repetitive sequences along the chromosomes and short read lengths of secondgeneration sequencing. Saccharomyces cerevisiae strain CEN. PK113-7D, widely used as a model organism and a cell factory, was selected for this study to demonstrate the superior capability of very long sequence reads for de novo genome assembly. We generated long reads using two common third-generation sequencing technologies (Oxford Nanopore Technology (ONT) and Pacific Biosciences (PacBio)) and used short reads obtained using Illumina sequencing for error correction. Assembly of the reads derived from all three technologies resulted in complete sequences for all 16 yeast chromosomes, as well as themitochondrial chromosome, in one step. Further, we identified three types of DNA methylation (5mC, 4mC and 6mA). Comparison between the reference strain S288C and strain CEN. PK113-7D identified chromosomal rearrangements against a background of similar gene content between the two strains. We identified full-length transcripts through ONT direct RNA sequencing technology. This allows for the identification of transcriptional landscapes, including untranslated regions (UTRs) (5' UTR and 3' UTR) as well as differential gene expression quantification. About 91% of the predicted transcripts could be consistently detected across biological replicates grown either on glucose or ethanol. Direct RNA sequencing identified many polyadenylated non-coding RNAs, rRNAs, telomere-RNA, long non-coding RNA and antisense RNA. This work demonstrates a strategy to obtain complete genome sequences and transcriptional landscapes that can be applied to other eukaryal organisms.

Resistance of Staphylococcus species to quaternary ammonium compounds, frequently used as disinfectants and biocides, can be attributed to qac genes. Most qac gene products belong to the Small Multidrug Resistant (SMR) protein family, and are often encoded by rolling-circle (RC) replicating plasmids. Four classes of SMR-type qac gene families have been described in Staphylococcus species: qacC, qacG, qacJ, and qacH. Within their class, these genes are highly conserved, but qacC genes are extremely conserved, although they are found in variable plasmid backgrounds. The lower degree of sequence identity of these plasmids compared to the strict nucleotide conservation of their qacC means that this gene has recently spread. In the absence of insertion sequences or other genetic elements explaining the mobility, we sought for an explanation of mobilization by sequence comparison. Publically available sequences of qac genes, their flanking genes and the replication gene that is invariably present in RC-plasmids were compared to reconstruct the evolutionary history of these plasmids and to explain the recent spread of qacC. Here we propose a new model that explains how qacC is mobilized and transferred to acceptor RC-plasmids without assistance of other genes, by means of its location in between the Double Strand replication Origin (DSO) and the Single-Strand replication Origin (SSO). The proposed mobilization model of this DSO-qacC-SSO element represents a novel mechanism of gene mobilization in RC-plasmids, which has also been employed by other genes, such as lnuA (conferring lincomycin resistance). The proposed gene mobility has aided to the wide spread of clinically relevant resistance genes in Staphylococcus populations.

Microbial taxonomy should provide adequate descriptions of bacterial, archaeal, and eukaryotic microbial diversity in ecological, clinical, and industrial environments. Its cornerstone, the prokaryote species has been re-evaluated twice. It is time to revisit polyphasic taxonomy, its principles, and its practice, including its underlying pragmatic species concept. Ultimately, we will be able to realize an old dream of our predecessor taxonomists and build a genomic-based microbial taxonomy, using standardized and automated curation of high-quality complete genome sequences as the new gold standard., National Science Foundation (U.S.) (NSF Grant DEB-1046413), National Science Foundation (U.S.) (NSF Grant CNS-1305112), National Science Foundation (U.S.) (NSF Grant DEB 0918333), National Science Foundation (U.S.) (NSF grant OCE 1441943), Gordon and Betty Moore Foundation, United States. Dept. of Energy. Office of Science, United States. Dept. of Energy. Office of Biological and Environmental Research, Oak Ridge National Laboratory, Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro, Brazil. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (grant), Conselho Nacional de Pesquisas (Brazil)

Urinary tract infections (UTIs) are among the most common infectious diseases of humans, with Escherichia coli responsible for >80% of all cases. One extreme of UTI is asymptomatic bacteriuria (ABU), which occurs as an asymptomatic carrier state that resembles commensalism. To understand the evolution and molecular mechanisms that underpin ABU, the genome of the ABU E. coli strain VR50 was sequenced. Analysis of the complete genome indicated that it most resembles E. coli K-12, with the addition of a 94-kb genomic island (GI-VR50-pheV), eight prophages, and multiple plasmids. GI-VR50-pheV has a mosaic structure and contains genes encoding a number of UTI-associated virulence factors, namely, Afa (afimbrial adhesin), two autotransporter proteins (Ag43 and Sat), and aerobactin. We demonstrated that the presence of this island in VR50 confers its ability to colonize the murine bladder, as a VR50 mutant with GI-VR50-pheV deleted was attenuated in a mouse model of UTI in vivo. We established that Afa is the island-encoded factor responsible for this phenotype using two independent deletion (Afa operon and AfaE adhesin) mutants. E. coli VR50afa and VR50afaE displayed significantly decreased ability to adhere to human bladder epithelial cells. In the mouse model of UTI, VR50afa and VR50afaE displayed reduced bladder colonization compared to wild-type VR50, similar to the colonization level of the GI-VR50-pheV mutant. Our study suggests that E. coli VR50 is a commensal-like strain that has acquired fitness factors that facilitate colonization of the human bladder.

The 2014 Ebola outbreak in West Africa is the largest documented for this virus. To examine the dynamics of this genome, we compare more than 100 currently available ebolavirus genomes to each other and to other viral genomes. Based on oligomer frequency analysis, the family Filoviridae forms a distinct group from all other sequenced viral genomes. All filovirus genomes sequenced to date encode proteins with similar functions and gene order, although there is considerable divergence in sequences between the three genera Ebolavirus, Cuevavirus and Marburgvirus within the family Filoviridae. Whereas all ebolavirus genomes are quite similar (multiple sequences of the same strain are often identical), variation is most common in the intergenic regions and within specific areas of the genes encoding the glycoprotein (GP), nucleoprotein (NP) and polymerase (L). We predict regions that could contain epitope-binding sites, which might be good vaccine targets. This information, combined with glycosylation sites and experimentally determined epitopes, can identify the most promising regions for the development of therapeutic strategies. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Standards in Genomic Sciences 9 (2014): 599-601, doi:10.4056/sigs.5559680., The Genomic Standards Consortium (GSC) is an open-membership community working towards the development, implementation and harmonization of standards in the field of genomics. The mission of the GSC is to improve digital descriptions of genomes, metagenomes and gene marker sequences. The GSC started in late 2005 with the defined task of establishing what is now termed the “Minimum Information about any Sequence” (MIxS) standard [1,2]. As an outgrowth of the activities surrounding the creation and implementation of the MixS standard there are now 18 projects within the GSC [3]. These efforts cover an ever widening range of standardization activities. Given the growth of projects and to promote transparency, participation and adoption the GSC has developed a “GSC Project Description Template”. A complete set of GSC Project Descriptions and the template are available on the GSC website. The GSC has an open policy of participation and continues to welcome new efforts. Any projects that facilitate the standard descriptions and exchange of data are potential candidates for inclusion under the GSC umbrella. Areas that expand the scope of the GSC are encouraged. Through these collective activities we hope to help foster the growth of the ‘bioinformatics standards’ community. For more information on the GSC and its range of projects, please see http://gensc.org/.

Background: Prediction of the optimal habitat conditions for a given bacterium, based on genome sequence alone would be of value for scientific as well as industrial purposes. One example of such a habitat adaptation is the requirement for oxygen. In spite of good genome data availability, there have been only a few prediction attempts of bacterial oxygen requirements, using genome sequences. Here, we describe a method for distinguishing aerobic, anaerobic and facultative anaerobic bacteria, based on genome sequence-derived input, using naive Bayesian inference. In contrast, other studies found in literature only demonstrate the ability to distinguish two classes at a time. Results: The results shown in the present study are as good as or better than comparable methods previously described in the scientific literature, with an arguably simpler method, when results are directly compared. This method further compares the performance of a single-step naive Bayesian prediction of the three included classifications, compared to a simple Bayesian network with two steps. A two-step network, distinguishing first respiring from non-respiring organisms, followed by the distinction of aerobe and facultative anaerobe organisms within the respiring group, is found to perform best. Conclusions: A simple naive Bayesian network based on the presence or absence of specific protein domains within a genome is an effective and easy way to predict bacterial habitat preferences, such as oxygen requirement.