The draft genome of  Brucella abortus  strain Ba col-B012, isolated from a dairy farm in Nariño, Colombia, bring new insights into the epidemiology of biovar 4 strains

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Translated abstract

Brucellosis is a commonly diagnosed zoonosis that causes infertility and abortion in cattle, it is acquired from handling of infected animals or consuming contaminated milk or milk products. In Colombia, it belongs to the official notifiable disease list, despite its relevance little is known about the origin, epidemiology and the genetic constituents of the strains circulating in dairy farms. Here we present the draft genome of B. abortus Ba Col-B012, an isolate obtained from a female Holstein belonging to a dairy farm in Nariño, Colombia. This genome comprises 3,234,714 bp and 3018 predicted protein-encoding genes. Using comparative genomics and phylogenetic analysis, we found that the strain Ba Col-B012 clustered with known biovar 4 variants. The analysis of the core genes allowed the identification of polymorphisms only present in biovar 4 genomes, these regions are proposed as possible targets for identification by PCR. The sequencing of B. abortus Ba Col-B012 genome provides important insights to improve the diagnosis and the epidemiology of this disease and represents the first report of the biovar 4 in Colombia.

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Record: found
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Article: not found

GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.

J Besemer (2001)

Improving the accuracy of prediction of gene starts is one of a few remaining open problems in computer prediction of prokaryotic genes. Its difficulty is caused by the absence of relatively strong sequence patterns identifying true translation initiation sites. In the current paper we show that the accuracy of gene start prediction can be improved by combining models of protein-coding and non-coding regions and models of regulatory sites near gene start within an iterative Hidden Markov model based algorithm. The new gene prediction method, called GeneMarkS, utilizes a non-supervised training procedure and can be used for a newly sequenced prokaryotic genome with no prior knowledge of any protein or rRNA genes. The GeneMarkS implementation uses an improved version of the gene finding program GeneMark.hmm, heuristic Markov models of coding and non-coding regions and the Gibbs sampling multiple alignment program. GeneMarkS predicted precisely 83.2% of the translation starts of GenBank annotated Bacillus subtilis genes and 94.4% of translation starts in an experimentally validated set of Escherichia coli genes. We have also observed that GeneMarkS detects prokaryotic genes, in terms of identifying open reading frames containing real genes, with an accuracy matching the level of the best currently used gene detection methods. Accurate translation start prediction, in addition to the refinement of protein sequence N-terminal data, provides the benefit of precise positioning of the sequence region situated upstream to a gene start. Therefore, sequence motifs related to transcription and translation regulatory sites can be revealed and analyzed with higher precision. These motifs were shown to possess a significant variability, the functional and evolutionary connections of which are discussed.

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Record: found
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Article: not found

Prediction of lipoprotein signal peptides in Gram-negative bacteria.

Agnieszka S Juncker, Hanni Willenbrock, Gunnar von Heijne … (2003)

A method to predict lipoprotein signal peptides in Gram-negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII-cleaved proteins), SPaseI-cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI-cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram-positive lipoprotein signal peptides differ from Gram-negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram-positive test set. A genome search was carried out for 12 Gram-negative genomes and one Gram-positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network-based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/.

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List of new names and new combinations previously effectively, but not validly, published.

L Editor, LD Kuykendall (2006)

The purpose of this announcement is to effect the valid publication of the following new names and new combinations under the procedure described in the Bacteriological Code (1990 Revision). Authors and other individuals wishing to have new names and/or combinations included in future lists should send three copies of the pertinent reprint or photocopies thereof to the IJSEM Editorial Office for confirmation that all of the other requirements for valid publication have been met. It is also a requirement of IJSEM and the ICSP that authors of new species, new subspecies and new combinations provide evidence that types are deposited in two recognized culture collections in two different countries (i.e. documents certifying deposition and availability of type strains). It should be noted that the date of valid publication of these new names and combinations is the date of publication of this list, not the date of the original publication of the names and combinations. The authors of the new names and combinations are as given below, and these authors' names will be included in the author index of the present issue and in the volume author index. Inclusion of a name on these lists validates the publication of the name and thereby makes it available in bacteriological nomenclature. The inclusion of a name on this list is not to be construed as taxonomic acceptance of the taxon to which the name is applied. Indeed, some of these names may, in time, be shown to be synonyms, or the organisms may be transferred to another genus, thus necessitating the creation of a new combination.

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Author and article information

Contributors

Alejandro Caro-Quintero:

ORCID: http://orcid.org/0000-0001-8807-671X

acaro@corpoica.org.co

Journal

Journal ID (nlm-ta): Stand Genomic Sci

Journal ID (iso-abbrev): Stand Genomic Sci

Title: Standards in Genomic Sciences

Publisher: BioMed Central (London )

ISSN (Electronic): 1944-3277

Publication date (Electronic): 22 December 2017

Publication date PMC-release: 22 December 2017

Publication date Collection: 2017

Volume: 12

Electronic Location Identifier: 89

Affiliations

ISNI 0000 0001 1703 2808, GRID grid.466621.1, Corporación Colombiana de Investigación Agropecuaria - Corpoica. Centro de Investigación Tibaitatá, ; Mosquera - Bogotá, Cundinamarca Colombia

Author information

Alejandro Caro-Quintero http://orcid.org/0000-0001-8807-671X

Article

Publisher ID: 299

DOI: 10.1186/s40793-017-0299-2

PMC ID: 5741917

SO-VID: 77acd9f9-0378-4524-a03b-189a99b58346

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 20 March 2017

Date accepted : 5 December 2017

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100002906, Ministerio de Agricultura y Desarrollo Rural;

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: brucellosis,abortion,pathogen,zoonosis

Data availability:

ScienceOpen disciplines: Genetics

Keywords: brucellosis, abortion, pathogen, zoonosis

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