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      ' Candidatus Ornithobacterium hominis': insights gained from draft genomes obtained from nasopharyngeal swabs

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          Abstract

          Candidatus Ornithobacterium hominis’ represents a new member of the Flavobacteriaceae detected in 16S rRNA gene surveys of people from South-East Asia, Africa and Australia. It frequently colonizes the infant nasopharynx at high proportional abundance, and we demonstrate its presence in 42 % of nasopharyngeal swabs from 12-month-old children in the Maela refugee camp in Thailand. The species, a Gram-negative bacillus, has not yet been cultured, but the cells can be identified in mixed samples by fluorescent hybridization. Here, we report seven genomes assembled from metagenomic data, two to improved draft standard. The genomes are approximately 1.9 Mb, sharing 62 % average amino acid identity with the only other member of the genus, the bird pathogen Ornithobacterium rhinotracheale . The draft genomes encode multiple antibiotic-resistance genes, competition factors, Flavobacterium johnsoniae - like gliding motility genes and a homologue of the Pasteurella multocida mitogenic toxin. Intra- and inter-host genome comparison suggests that colonization with this bacterium is both persistent and strain exclusive.

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          SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments

          Rapidly decreasing genome sequencing costs have led to a proportionate increase in the number of samples used in prokaryotic population studies. Extracting single nucleotide polymorphisms (SNPs) from a large whole genome alignment is now a routine task, but existing tools have failed to scale efficiently with the increased size of studies. These tools are slow, memory inefficient and are installed through non-standard procedures. We present SNP-sites which can rapidly extract SNPs from a multi-FASTA alignment using modest resources and can output results in multiple formats for downstream analysis. SNPs can be extracted from a 8.3 GB alignment file (1842 taxa, 22 618 sites) in 267 seconds using 59 MB of RAM and 1 CPU core, making it feasible to run on modest computers. It is easy to install through the Debian and Homebrew package managers, and has been successfully tested on more than 20 operating systems. SNP-sites is implemented in C and is available under the open source license GNU GPL version 3.
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            NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy

            The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of genomic, transcript and protein sequence records. These records are selected and curated from public sequence archives and represent a significant reduction in redundancy compared to the volume of data archived by the International Nucleotide Sequence Database Collaboration. The database includes over 16 000 organisms, 2.4 × 106 genomic records, 13 × 106 proteins and 2 × 106 RNA records spanning prokaryotes, eukaryotes and viruses (RefSeq release 49, September 2011). The RefSeq database is maintained by a combined approach of automated analyses, collaboration and manual curation to generate an up-to-date representation of the sequence, its features, names and cross-links to related sources of information. We report here on recent growth, the status of curating the human RefSeq data set, more extensive feature annotation and current policy for eukaryotic genome annotation via the NCBI annotation pipeline. More information about the resource is available online (see http://www.ncbi.nlm.nih.gov/RefSeq/).
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              Sample size calculation in medical studies

              Optimum sample size is an essential component of any research. The main purpose of the sample size calculation is to determine the number of samples needed to detect significant changes in clinical parameters, treatment effects or associations after data gathering. It is not uncommon for studies to be underpowered and thereby fail to detect the existing treatment effects due to inadequate sample size. In this paper, we explain briefly the basic principles of sample size calculations in medical studies.
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                Author and article information

                Journal
                Microb Genom
                Microb Genom
                mgen
                mgen
                Microbial Genomics
                Microbiology Society
                2057-5858
                February 2019
                5 February 2019
                5 February 2019
                : 5
                : 2
                : e000247
                Affiliations
                [ 1]Pathogen Genomics, Wellcome Sanger Institute , Hinxton, UK
                [ 2]Department of Biochemistry, University of Cambridge , Cambridge, UK
                [ 3]Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University , Mae Sot, Thailand
                [ 4]Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford , Oxford, UK
                [ 5]Cambodia-Oxford Medical Research Unit, Angkor Hospital for Children , Siem Reap, Cambodia
                [ ]Present address: Quadram Institute Bioscience, Norwich, UK.
                [ ]Present address: Illumina Cambridge Ltd, Little Chesterford, UK.
                Author notes
                *Correspondence: Susannah J. Salter, sb18@ 123456sanger.ac.uk
                Article
                mgen000247
                10.1099/mgen.0.000247
                6421346
                30720420
                c2115e81-7c4f-4acd-ae3c-f301c1a74fae
                © 2019 The Authors

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 May 2018
                : 30 November 2018
                Categories
                Research Article
                Microbial Evolution and Epidemiology: Communicable Disease Genomics
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                ornithobacterium,respiratory tract microbiome,pasteurella mitogenic toxin

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