Author
Mário Ramirez
Other affiliations: Universidade Nova de Lisboa, University of Lisbon, University of Concepción ...read more
Bio: Mário Ramirez is an academic researcher from Instituto de Medicina Molecular. The author has contributed to research in topics: Serotype & Multilocus sequence typing. The author has an hindex of 40, co-authored 137 publications receiving 5723 citations. Previous affiliations of Mário Ramirez include Universidade Nova de Lisboa & University of Lisbon.
Papers published on a yearly basis
Papers
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TL;DR: GoeBURST is a globally optimized implementation of the eBURST algorithm, that identifies alternative patterns of descent for several bacterial species and can be applied to any multilocus typing data based on the number of differences between numeric profiles.
Abstract: Background: Multilocus Sequence Typing (MLST) is a frequently used typing method for the analysis of the clonal relationships among strains of several clinically relevant microbial species. MLST is based on the sequence of housekeeping genes that result in each strain having a distinct numerical allelic profile, which is abbreviated to a unique identifier: the sequence type (ST). The relatedness between two strains can then be inferred by the differences between allelic profiles. For a more comprehensive analysis of the possible patterns of evolutionary descent, a set of rules were proposed and implemented in the eBURST algorithm. These rules allow the division of a data set into several clusters of related strains, dubbed clonal complexes, by implementing a simple model of clonal expansion and diversification. Within each clonal complex, the rules identify which links between STs correspond to the most probable pattern of descent. However, the eBURST algorithm is not globally optimized, which can result in links, within the clonal complexes, that violate the rules proposed. Results: Here, we present a globally optimized implementation of the eBURST algorithm – goeBURST. The search for a global optimal solution led to the formalization of the problem as a graphic matroid, for which greedy algorithms that provide an optimal solution exist. Several public data sets of MLST data were tested and differences between the two implementations were found and are discussed for five bacterial species: Enterococcus faecium, Streptococcus pneumoniae, Burkholderia pseudomallei, Campylobacter jejuni and Neisseria spp.. A novel feature implemented in goeBURST is the representation of the level of tiebreak rule reached before deciding if a link should be drawn, which can used to visually evaluate the reliability of the represented hypothetical pattern of descent. Conclusion: goeBURST is a globally optimized implementation of the eBURST algorithm, that identifies alternative patterns of descent for several bacterial species. Furthermore, the algorithm can be applied to any multilocus typing data based on the number of differences between numeric profiles. A software implementation is available at http://goeBURST.phyloviz.net.
511 citations
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TL;DR: PHYLOViZ is platform independent Java software that allows the integrated analysis of sequence-based typing methods, including SNP data generated from whole genome sequence approaches, and associated epidemiological data.
Abstract: With the decrease of DNA sequencing costs, sequence-based typing methods are rapidly becoming the gold standard for epidemiological surveillance. These methods provide reproducible and comparable results needed for a global scale bacterial population analysis, while retaining their usefulness for local epidemiological surveys. Online databases that collect the generated allelic profiles and associated epidemiological data are available but this wealth of data remains underused and are frequently poorly annotated since no user-friendly tool exists to analyze and explore it. PHYLOViZ is platform independent Java software that allows the integrated analysis of sequence-based typing methods, including SNP data generated from whole genome sequence approaches, and associated epidemiological data. goeBURST and its Minimum Spanning Tree expansion are used for visualizing the possible evolutionary relationships between isolates. The results can be displayed as an annotated graph overlaying the query results of any other epidemiological data available. PHYLOViZ is a user-friendly software that allows the combined analysis of multiple data sources for microbial epidemiological and population studies. It is freely available at http://www.phyloviz.net
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452 citations
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TL;DR: A framework of measures for the quantitative assessment of correspondences between different typing methods is proposed as a first step to the global mapping of type equivalences and showed that if PFGE or MLST data are available one can confidently predict the emm type.
Abstract: The studies that correlate the results obtained by different typing methodologies rely solely on qualitative comparisons of the groups defined by each methodology. We propose a framework of measures for the quantitative assessment of correspondences between different typing methods as a first step to the global mapping of type equivalences. A collection of 325 macrolide-resistant Streptococcus pyogenes isolates associated with pharyngitis cases in Portugal was used to benchmark the proposed measures. All isolates were characterized by macrolide resistance phenotyping, T serotyping, emm sequence typing, and pulsed-field gel electrophoresis (PFGE), using SmaI or Cfr9I and SfiI. A subset of 41 isolates, representing each PFGE cluster, was also characterized by multilocus sequence typing (MLST). The application of Adjusted Rand and Wallace indices allowed the evaluation of the strength and the directionality of the correspondences between the various typing methods and showed that if PFGE or MLST data are available one can confidently predict the emm type (Wallace coefficients of 0.952 for both methods). In contrast, emm typing was a poor predictor of PFGE cluster or MLST sequence type (Wallace coefficients of 0.803 and 0.655, respectively). This was confirmed by the analysis of the larger data set available from http://spyogenes.mlst.net and underscores the necessity of performing PFGE or MLST to unambiguously define clones in S. pyogenes.
308 citations
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TL;DR: PHYLOViZ 2.0 is presented, an extension of PHYLoviZ tool, a platform independent Java tool that allows phylogenetic inference and data visualization for large datasets of sequence based typing methods, including Single Nucleotide Polymorphism (SNP) and whole genome/core genome Multilocus Sequence Typing (wg/cgMLST) analysis.
Abstract: Summary: High Throughput Sequencing provides a cost effective means of generating high resolution data for hundreds or even thousands of strains, and is rapidly superseding methodologies based on a few genomic loci. The wealth of genomic data deposited on public databases such as Sequence Read Archive/European Nucleotide Archive provides a powerful resource for evolutionary analysis and epidemiological surveillance. However, many of the analysis tools currently available do not scale well to these large datasets, nor provide the means to fully integrate ancillary data. Here we present PHYLOViZ 2.0, an extension of PHYLOViZ tool, a platform independent Java tool that allows phylogenetic inference and data visualization for large datasets of sequence based typing methods, including Single Nucleotide Polymorphism (SNP) and whole genome/core genome Multilocus Sequence Typing (wg/cgMLST) analysis. PHYLOViZ 2.0 incorporates new data analysis algorithms and new visualization modules, as well as the capability of saving projects for subsequent work or for dissemination of results. Availability and Implementation: http://www.phyloviz.net/ (licensed under GPLv3). Contact: cvaz@inesc-id.pt Supplementary information: Supplementary data are available at Bioinformatics online.
257 citations
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01 Mar 2018
TL;DR: The chewBBACA suite was designed to assist users in the creation and evaluation of novel whole-genome or core- genome gene-by-gene typing schemas and subsequent allele calling in bacterial strains of interest.
Abstract: Gene-by-gene approaches are becoming increasingly popular in bacterial genomic epidemiology and outbreak detection. However, there is a lack of open-source scalable software for schema definition and allele calling for these methodologies. The chewBBACA suite was designed to assist users in the creation and evaluation of novel whole-genome or core-genome gene-by-gene typing schemas and subsequent allele calling in bacterial strains of interest. chewBBACA performs the schema creation and allele calls on complete or draft genomes resulting from de novo assemblers. The chewBBACA software uses Python 3.4 or higher and can run on a laptop or in high performance clusters making it useful for both small laboratories and large reference centers. ChewBBACA is available at https://github.com/B-UMMI/chewBBACA.
201 citations
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01 Jun 2012
TL;DR: SPAdes as mentioned in this paper is a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler and on popular assemblers Velvet and SoapDeNovo (for multicell data).
Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.
10,124 citations
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TL;DR: In an elegant series of clinical observations and laboratory studies published in 1880 and 1882, Ogston described staphylococcal disease and its role in sepsis and abscess formation.
Abstract: Micrococcus, which, when limited in its extent and activity, causes acute suppurative inflammation (phlegmon), produces, when more extensive and intense in its action on the human system, the most virulent forms of septicaemia and pyaemia.1 In an elegant series of clinical observations and laboratory studies published in 1880 and 1882, Ogston described staphylococcal disease and its role in sepsis and abscess formation.1,2 More than 100 years later, Staphylococcus aureus remains a versatile and dangerous pathogen in humans. The frequencies of both community-acquired and hospital-acquired staphylococcal infections have increased steadily, with little change in overall mortality. Treatment of these infections . . .
5,550 citations
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TL;DR: The Bacterial Isolate Genome Sequence Database (BIGSDB) represents a freely available resource that will assist the broader community in the elucidation of the structure and function of bacteria by means of a population genomics approach.
Abstract: The opportunities for bacterial population genomics that are being realised by the application of parallel nucleotide sequencing require novel bioinformatics platforms These must be capable of the storage, retrieval, and analysis of linked phenotypic and genotypic information in an accessible, scalable and computationally efficient manner The Bacterial Isolate Genome Sequence Database (BIGSDB) is a scalable, open source, web-accessible database system that meets these needs, enabling phenotype and sequence data, which can range from a single sequence read to whole genome data, to be efficiently linked for a limitless number of bacterial specimens The system builds on the widely used mlstdbNet software, developed for the storage and distribution of multilocus sequence typing (MLST) data, and incorporates the capacity to define and identify any number of loci and genetic variants at those loci within the stored nucleotide sequences These loci can be further organised into 'schemes' for isolate characterisation or for evolutionary or functional analyses Isolates and loci can be indexed by multiple names and any number of alternative schemes can be accommodated, enabling cross-referencing of different studies and approaches LIMS functionality of the software enables linkage to and organisation of laboratory samples The data are easily linked to external databases and fine-grained authentication of access permits multiple users to participate in community annotation by setting up or contributing to different schemes within the database Some of the applications of BIGSDB are illustrated with the genera Neisseria and Streptococcus The BIGSDB source code and documentation are available at http://pubmlstorg/software/database/bigsdb/
Genomic data can be used to characterise bacterial isolates in many different ways but it can also be efficiently exploited for evolutionary or functional studies BIGSDB represents a freely available resource that will assist the broader community in the elucidation of the structure and function of bacteria by means of a population genomics approach
1,943 citations
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TL;DR: This work analyzes an international collection of 912 MRSA and methicillin-susceptible S. aureus isolates to establish the likely evolutionary origins of each major MRSA clone, the genotype of the original MRSAclone and its MSSA progenitor, and the extent of acquisition and horizontal movement of the methiillin resistance genes.
Abstract: Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of hospital-acquired infections that are becoming increasingly difficult to combat because of emerging resistance to all current antibiotic classes. The evolutionary origins of MRSA are poorly understood, no rational nomenclature exists, and there is no consensus on the number of major MRSA clones or the relatedness of clones described from different countries. We resolve all of these issues and provide a more thorough and precise analysis of the evolution of MRSA clones than has previously been possible. Using multilocus sequence typing and an algorithm, burst, we analyzed an international collection of 912 MRSA and methicillin-susceptible S. aureus (MSSA) isolates. We identified 11 major MRSA clones within five groups of related genotypes. The putative ancestral genotype of each group and the most parsimonious patterns of descent of isolates from each ancestor were inferred by using burst, which, together with analysis of the methicillin resistance genes, established the likely evolutionary origins of each major MRSA clone, the genotype of the original MRSA clone and its MSSA progenitor, and the extent of acquisition and horizontal movement of the methicillin resistance genes. Major MRSA clones have arisen repeatedly from successful epidemic MSSA strains, and isolates with decreased susceptibility to vancomycin, the antibiotic of last resort, are arising from some of these major MRSA clones, highlighting a depressing progression of increasing drug resistance within a small number of ecologically successful S. aureus genotypes.
1,653 citations
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TL;DR: The current review presents the available genomics and biological data on prophages from bacterial pathogens in an evolutionary framework to demonstrate that the chromosomes from bacteria and their viruses (bacteriophages) are coevolving.
Abstract: Comparative genomics demonstrated that the chromosomes from bacteria and their viruses (bacteriophages) are coevolving. This process is most evident for bacterial pathogens where the majority contain prophages or phage remnants integrated into the bacterial DNA. Many prophages from bacterial pathogens encode virulence factors. Two situations can be distinguished: Vibrio cholerae, Shiga toxin-producing Escherichia coli, Corynebacterium diphtheriae, and Clostridium botulinum depend on a specific prophage-encoded toxin for causing a specific disease, whereas Staphylococcus aureus, Streptococcus pyogenes, and Salmonella enterica serovar Typhimurium harbor a multitude of prophages and each phage-encoded virulence or fitness factor makes an incremental contribution to the fitness of the lysogen. These prophages behave like “swarms” of related prophages. Prophage diversification seems to be fueled by the frequent transfer of phage material by recombination with superinfecting phages, resident prophages, or occasional acquisition of other mobile DNA elements or bacterial chromosomal genes. Prophages also contribute to the diversification of the bacterial genome architecture. In many cases, they actually represent a large fraction of the strain-specific DNA sequences. In addition, they can serve as anchoring points for genome inversions. The current review presents the available genomics and biological data on prophages from bacterial pathogens in an evolutionary framework.
1,499 citations