Chewie Nomenclature Server (chewie-NS): a deployable nomenclature server for easy sharing of core and whole genome MLST schemas

Chewie Nomenclature Server (chewie-NS, https://chewbbaca.online/) allows users to share genome-based gene-by-gene typing schemas and to maintain a common nomenclature, simplifying the comparison of results. The combination between local analyses and a public repository of allelic data strikes a balance between potential confidentiality issues and the need to compare results. The possibility of deploying private instances of chewie-NS facilitates the creation of nomenclature servers with a restricted user base to allow compliance with the strictest data policies. Chewie-NS allows users to easily share their own schemas and to explore publicly available schemas, including informative statistics on schemas and loci presented in interactive charts and tables. Users can retrieve all the information necessary to run a schema locally or all the alleles identified at a particular locus. The integration with the chewBBACA suite enables users to directly upload new schemas to chewie-NS, download existing schemas and synchronize local and remote schemas from chewBBACA command line version, allowing an easier integration into high-throughput analysis pipelines. The same REST API linking chewie-NS and the chewBBACA suite supports the interaction of other interfaces or pipelines with the databases available at chewie-NS, facilitating the reusability of the stored data.

Two multi-fragment recombination events resulted in the β-lactam-resistant serotype 11A-ST6521 related to Spain9V-ST156 pneumococcal clone spreading in south-western Europe, 2008 to 2016

BackgroundThe successful pneumococcal clone Spain9V-ST156 (PMEN3) is usually associated with vaccine serotypes 9V and 14.AimOur objective was to analyse the increase of a serotype 11A variant of PMEN3 as cause of invasive pneumococcal disease (IPD) in Spain and its spread in south-western Europe.MethodsWe conducted a prospective multicentre study of adult IPD in Spain (2008-16). Furthermore, a subset of 61 penicillin-resistant serotype 11A isolates from France, Italy, Portugal and Spain were subjected to whole genome sequencing (WGS) and compared with 238 genomes from the European Nucleotide Archive (ENA).ResultsAlthough the incidence of serotype 11A in IPD was stable, a clonal shift was detected from CC62 (penicillin-susceptible) to CC156 (penicillin-resistant). By WGS, three major 11A-CC156 lineages were identified, linked to ST156 (n = 5 isolates; France, Italy and Portugal), ST166 (n = 4 isolates; France and Portugal) and ST838/6521 (n = 52 isolates; France, Portugal and Spain). Acquisition of the 11A capsule allowed to escape vaccine effect. AP200 (11A-ST62) was the donor for ST156 and ST838/6521 but not for ST166. In-depth analysis of ST838/6521 lineage showed two multi-fragment recombination events including four and seven fragments from an 11A-ST62 and an NT-ST344 representative, respectively.ConclusionThe increase in penicillin-resistant serotype 11A IPD in Spain was linked to the spread of a vaccine escape PMEN3 recombinant clone. Several recombination events were observed in PMEN3 acquiring an 11A capsule. The most successful 11A-PMEN3 lineage spreading in south-western Europe appeared after two multi-fragment recombination events with representatives of two major pneumococcal clones (11A-ST62 and NT-ST344).

Distance-based phylogenetic inference from typing data: a unifying view

Typing methods are widely used in the surveillance of infectious diseases, outbreaks investigation and studies of the natural history of an infection. Moreover, their use is becoming standard, in particular with the introduction of high-throughput sequencing. On the other hand, the data being generated are massive and many algorithms have been proposed for a phylogenetic analysis of typing data, addressing both correctness and scalability issues. Most of the distance-based algorithms for inferring phylogenetic trees follow the closest pair joining scheme. This is one of the approaches used in hierarchical clustering. Moreover, although phylogenetic inference algorithms may seem rather different, the main difference among them resides on how one defines cluster proximity and on which optimization criterion is used. Both cluster proximity and optimization criteria rely often on a model of evolution. In this work, we review, and we provide a unified view of these algorithms. This is an important step not only to better understand such algorithms but also to identify possible computational bottlenecks and improvements, important to deal with large data sets.

Plasmid ATLAS: plasmid visual analytics and identification in high-throughput sequencing data

Plasmid ATLAS (pATLAS, http://www.patlas.site) provides an easy-to-use web accessible database with visual analytics tools to explore the relationships of plasmids available in NCBI's RefSeq database. pATLAS has two main goals: (i) to provide an easy way to search for plasmids deposited in NCBI RefSeq and their associated metadata; (ii) to visualize the relationships of plasmids in a graph, allowing the exploration of plasmid evolution. pATLAS allows searching by plasmid name, bacterial host taxa, antibiotic resistance and virulence genes, plasmid families, and by sequence length and similarity. pATLAS is also able to represent in the plasmid network, plasmid sets identified by external pipelines using mapping, mash screen or assembly from high-throughput sequencing data. By representing the identified hits within the network of relationships between plasmids, allowing the possibility of removing redundant results, and by taking advantage of the browsing capabilities of pATLAS, users can more easily interpret the pipelines' results. All these analyses can be saved to a JSON file for sharing and future re-evaluation. Furthermore, by offering a REST-API, the pATLAS database and network display are easily accessible by other interfaces or pipelines.

DEN-IM: dengue virus genotyping from amplicon and shotgun metagenomic sequencing

Dengue virus (DENV) represents a public health threat and economic burden in affected countries. The availability of genomic data is key to understanding viral evolution and dynamics, supporting improved control strategies. Currently, the use of high-throughput sequencing (HTS) technologies, which can be applied both directly to patient samples (shotgun metagenomics) and to PCR-amplified viral sequences (amplicon sequencing), is potentially the most informative approach to monitor viral dissemination and genetic diversity by providing, in a single methodological step, identification and characterization of the whole viral genome at the nucleotide level. Despite many advantages, these technologies require bioinformatics expertise and appropriate infrastructure for the analysis and interpretation of the resulting data. In addition, the many software solutions available can hamper the reproducibility and comparison of results. Here we present DEN-IM, a one-stop, user-friendly, containerized and reproducible workflow for the analysis of DENV short-read sequencing data from both amplicon and shotgun metagenomics approaches. It is able to infer the DENV coding sequence (CDS), identify the serotype and genotype, and generate a phylogenetic tree. It can easily be run on any UNIX-like system, from local machines to high-performance computing clusters, performing a comprehensive analysis without the requirement for extensive bioinformatics expertise. Using DEN-IM, we successfully analysed two types of DENV datasets. The first comprised 25 shotgun metagenomic sequencing samples from patients with variable serotypes and genotypes, including an in vitro spiked sample containing the four known serotypes. The second consisted of 106 paired-end and 76 single-end amplicon sequences of DENV 3 genotype III and DENV 1 genotype I, respectively, where DEN-IM allowed detection of the intra-genotype diversity. The DEN-IM workflow, parameters and execution configuration files, and documentation are freely available at https://github.com/B-UMMI/DEN-IM).

BacPipe: A Rapid, User-Friendly Whole-Genome Sequencing Pipeline for Clinical Diagnostic Bacteriology

Despite rapid advances in whole genome sequencing (WGS) technologies, their integration into routine microbiological diagnostics has been hampered by the lack of standardized downstream bioinformatics analysis. We developed a comprehensive and computationally low-resource bioinformatics pipeline (BacPipe) enabling direct analyses of bacterial whole-genome sequences (raw reads or contigs) obtained from second- or third-generation sequencing technologies. A graphical user interface was developed to visualize real-time progression of the analysis. The scalability and speed of BacPipe in handling large datasets was demonstrated using 4,139 Illumina paired-end sequence files of publicly available bacterial genomes (2.9–5.4 Mb) from the European Nucleotide Archive. BacPipe is integrated in EBI-SELECTA, a project-specific portal (H2020-COMPARE), and is available as an independent docker image that can be used across Windows- and Unix-based systems. BacPipe offers a fully automated “one-stop” bacterial WGS analysis pipeline to overcome the major hurdle of WGS data analysis in hospitals and public-health and for infection control monitoring.

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BacPipe is an automated whole genome sequencing pipeline

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Interactive user-friendly GUI

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BacPipe can process raw reads, contigs, or scaffolds

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Time-to-analysis for a 5 Mb genome is ∼30–40 min

Distinct Phenotypic and Genomic Signatures Underlie Contrasting Pathogenic Potential of Staphylococcus epidermidis Clonal Lineages

Background: Staphylococcus epidermidis is a common skin commensal that has emerged as a pathogen in hospitals, mainly related to medical devices-associated infections. Noteworthy, infection rates by S. epidermidis have the tendency to rise steeply in next decades together with medical devices use and immunocompromized population growth. Staphylococcus epidermidis population structure includes two major clonal lineages (A/C and B) that present contrasting pathogenic potentials. To address this distinction and explore the basis of increased pathogenicity of A/C lineage, we performed a detailed comparative analysis using phylogenetic and integrated pangenome-wide-association study (panGWAS) approaches and compared the lineages's phenotypes in in vitro conditions mimicking carriage and infection. Results: Each S. epidermidis lineage had distinct phenotypic signatures in skin and infection conditions and differed in genomic content. Combination of phenotypic and genotypic data revealed that both lineages were well adapted to skin environmental cues. However, they appear to occupy different skin niches, perform distinct biological functions in the skin and use different mechanisms to complete the same function: lineage B strains showed evidence of specialization to survival in microaerobic and lipid rich environment, characteristic of hair follicle and sebaceous glands; lineage A/C strains showed evidence for adaption to diverse osmotic and pH conditions, potentially allowing them to occupy a broader and more superficial skin niche. In infection conditions, A/C strains had an advantage, having the potential to bind blood-associated host matrix proteins, form biofilms at blood pH, resist antibiotics and macrophage acidity and to produce proteases. These features were observed to be rare in the lineage B strains. PanGWAS analysis produced a catalog of putative S. epidermidis virulence factors and identified an epidemiological molecular marker for the more pathogenic lineage. Conclusion: The prevalence of A/C lineage in infection is probably related to a higher metabolic and genomic versatility that allows rapid adaptation during transition from a commensal to a pathogenic lifestyle. The putative virulence and phenotypic factors associated to A/C lineage constitute a reliable framework for future studies on S. epidermidis pathogenesis and the finding of an epidemiological marker for the more pathogenic lineage is an asset for the management of S. epidermidis infections.

Fast phylogenetic inference from typing data

Background

Microbial typing methods are commonly used to study the relatedness of bacterial strains. Sequence-based typing methods are a gold standard for epidemiological surveillance due to the inherent portability of sequence and allelic profile data, fast analysis times and their capacity to create common nomenclatures for strains or clones. This led to development of several novel methods and several databases being made available for many microbial species. With the mainstream use of High Throughput Sequencing, the amount of data being accumulated in these databases is huge, storing thousands of different profiles. On the other hand, computing genetic evolutionary distances among a set of typing profiles or taxa dominates the running time of many phylogenetic inference methods. It is important also to note that most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles.

Results

We propose here an average-case linear-time algorithm to compute pairwise Hamming distances among a set of taxa under a given Hamming distance threshold. This article includes both a theoretical analysis and extensive experimental results concerning the proposed algorithm. We further show how this algorithm can be successfully integrated into a well known phylogenetic inference method, and how it can be used to speedup querying local phylogenetic patterns over large typing databases.

A primer on microbial bioinformatics for nonbioinformaticians

BACKGROUND

Presently, the bottleneck in the deployment of high-throughput sequencing technology is the ability to analyse the increasing amount of data produced in a fit-for-purpose manner. The field of microbial bioinformatics is thriving and quickly adapting to technological changes, which creates difficulties for nonbioinformaticians in following the complexity and increasingly obscure jargon of this field.

AIMS

This review is directed towards nonbioinformaticians who wish to gain understanding of the overall microbial bioinformatic processes, from raw data obtained from sequencers to final outputs.

SOURCES

The software and analytical strategies reviewed are based on the personal experience of the authors.

CONTENT

The bioinformatic processes of transforming raw reads to actionable information in a clinical and epidemiologic context is explained. We review the advantages and limitations of two major strategies currently applied: read mapping, which is the comparison with a predefined reference genome, and de novo assembly, which is the unguided assembly of the raw data. Finally, we discuss the main analytical methodologies and the most frequently used freely available software and its application in the context of bacterial infectious disease management.

IMPLICATIONS

High-throughput sequencing technologies are overhauling outbreak investigation and epidemiologic surveillance while creating new challenges due to the amount and complexity of data generated. The continuously evolving field of microbial bioinformatics is required for stakeholders to fully harness the power of these new technologies.

Food Safety in the Age of Next Generation Sequencing, Bioinformatics, and Open Data Access

Public health labs and food regulatory agencies globally are embracing whole genome sequencing (WGS) as a revolutionary new method that is positioned to replace numerous existing diagnostic and microbial typing technologies with a single new target: the microbial draft genome. The ability to cheaply generate large amounts of microbial genome sequence data, combined with emerging policies of food regulatory and public health institutions making their microbial sequences increasingly available and public, has served to open up the field to the general scientific community. This open data access policy shift has resulted in a proliferation of data being deposited into sequence repositories and of novel bioinformatics software designed to analyze these vast datasets. There also has been a more recent drive for improved data sharing to achieve more effective global surveillance, public health and food safety. Such developments have heightened the need for enhanced analytical systems in order to process and interpret this new type of data in a timely fashion. In this review we outline the emergence of genomics, bioinformatics and open data in the context of food safety. We also survey major efforts to translate genomics and bioinformatics technologies out of the research lab and into routine use in modern food safety labs. We conclude by discussing the challenges and opportunities that remain, including those expected to play a major role in the future of food safety science.

PHYLOViZ 2.0: providing scalable data integration and visualization for multiple phylogenetic inference methods

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.ptSupplementary information: Supplementary data are available at Bioinformatics online.

Not seeing the forest for the trees: size of the minimum spanning trees (MSTs) forest and branch significance in MST-based phylogenetic analysis

Trees, including minimum spanning trees (MSTs), are commonly used in phylogenetic studies. But, for the research community, it may be unclear that the presented tree is just a hypothesis, chosen from among many possible alternatives. In this scenario, it is important to quantify our confidence in both the trees and the branches/edges included in such trees. In this paper, we address this problem for MSTs by introducing a new edge betweenness metric for undirected and weighted graphs. This spanning edge betweenness metric is defined as the fraction of equivalent MSTs where a given edge is present. The metric provides a per edge statistic that is similar to that of the bootstrap approach frequently used in phylogenetics to support the grouping of taxa. We provide methods for the exact computation of this metric based on the well known Kirchhoff’s matrix tree theorem. Moreover, we implement and make available a module for the PHYLOViZ software and evaluate the proposed metric concerning both effectiveness and computational performance. Analysis of trees generated using multilocus sequence typing data (MLST) and the goeBURST algorithm revealed that the space of possible MSTs in real data sets is extremely large. Selection of the edge to be represented using bootstrap could lead to unreliable results since alternative edges are present in the same fraction of equivalent MSTs. The choice of the MST to be presented, results from criteria implemented in the algorithm that must be based in biologically plausible models.

Bacillus invictae sp. nov., isolated from a health product

A Gram-positive, rod-shaped, endospore-forming Bacillus isolate, Bi.FFUP1 T, recovered in Portugal from a health product was subjected to a polyphasic study and compared with the type strains of Bacillus pumilus , Bacillus safensis , Bacillus altitudinis and Bacillus xiamenensis , the phenotypically and genotypically most closely related species. Acid production from cellobiose, d-glucose and d-mannose and absence of acid production from d-arabinose, erythritol, inositol, maltose, mannitol, raffinose, rhamnose, sorbitol, starch and l-tryptophan discriminated this new isolate from the type strains of the most closely related species. Additionally, a significant different protein and carbohydrate signature was evidenced by spectroscopic techniques, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and Fourier transform IR spectroscopy with attenuated total reflectance. Using a chemometric approach, the score plot generated by principal component analysis clearly delineated the isolate as a separate cluster. The quinone system for strain Bi.FFUP1 T comprised predominantly menaquinone MK-7 and major polar lipids were diphosphatidylglycerol, an unidentified phospholipid and an unidentified glycolipid. Strain Bi.FFUP1 T showed ≥99 % 16S rRNA gene sequence similarity to B. safensis FO-036bT, B. pumilus (7061T and SAFR-032), B. altitudinis 41KF2bT and B. xiamenensis HYC-10T. Differences in strain Bi.FFUP1 T gyrB and rpoB sequences in comparison with the most closely related species and DNA–DNA hybridization experiments with Bi.FFUP1 T and B. pumilus ATCC 7061T, B. safensis FO-036bT, B. altitudinis 41KF2bT and B. xiamenensis HYC-10T gave relatedness values of 39.6 % (reciprocal 38.0 %), 49.9 % (reciprocal 42.9 %), 61.9 % (reciprocal 52.2 %) and 61.7 % (reciprocal 49.2 %), respectively, supported the delineation of strain Bi.FFUP1 T as a representative of a novel species of the genus Bacillus , for which the name Bacillus invictae sp. nov. is proposed, with strain Bi.FFUP1 T ( = DSM 26896T = CCUG 64113T) as the type strain.

TypOn: the microbial typing ontology

Abstract

Bacterial identification and characterization at subspecies level is commonly known as Microbial Typing. Currently, these methodologies are fundamental tools in Clinical Microbiology and bacterial population genetics studies to track outbreaks and to study the dissemination and evolution of virulence or pathogenicity factors and antimicrobial resistance. Due to advances in DNA sequencing technology, these methods have evolved to become focused on sequence-based methodologies. The need to have a common understanding of the concepts described and the ability to share results within the community at a global level are increasingly important requisites for the continued development of portable and accurate sequence-based typing methods, especially with the recent introduction of Next Generation Sequencing (NGS) technologies. In this paper, we present an ontology designed for the sequence-based microbial typing field, capable of describing any of the sequence-based typing methodologies currently in use and being developed, including novel NGS based methods. This is a fundamental step to accurately describe, analyze, curate, and manage information for microbial typing based on sequence based typing methods.

Phylogenetic and clonality analysis of Bacillus pumilus isolates uncovered a highly heterogeneous population of different closely related species and clones

Bacillus pumilus is a Gram-positive bacterium with a wide range of attributed applications, namely as a plant growth promoting rhizobacteria (PGPR), animal, and human probiotic. However, a rare putative role in human diseases has been reported, namely in food poisoning or as anthrax-like cutaneous infectious agent. This species is difficult to distinguish from its closely related species on the basis of phenotypic or biochemical characteristics and 16S rRNA gene sequences. In this study, the phylogenetic analysis of gyrB and rpoB gene sequences of a collection of isolates previously identified as B. pumilus, assigned most of them (93%, 38 of 41 isolates) to B. safensis or to the new recently described B. invictae. Moreover, we extended the previously reported recognized habitats of these species and unveiled a human health or biotechnological relevance (e.g. as implicated in food poisoning or PGPR) for them. Additionally, we demonstrated that both B. safensis and B. invictae species encompass a clonally diverse population, which can justify their great adaptation ability to different niches, with evidence of clonal-host specificity.

Bioinformatics in bacterial molecular epidemiology and public health: databases, tools and the next-generation sequencing revolution

Advances in typing methodologies have been the driving force in the field of molecular epidemiology of pathogens. The development of molecular methodologies, and more recently of DNA sequencing methods to complement and improve phenotypic identification methods, was accompanied by the generation of large amounts of data and the need to develop ways of storing and analysing them. Simultaneously, advances in computing allowed the development of specialised algorithms for image analysis, data sharing and integration, and for mining the ever larger amounts of accumulated data. In this review, we will discuss how bioinformatics accompanied the changes in bacterial molecular epidemiology. We will discuss the benefits for public health of specialised online typing databases and algorithms allowing for real-time data analysis and visualisation. The impact of the new and disruptive next-generation sequencing methodologies will be evaluated, and we will look ahead into these novel challenges.

Prognostic Prediction through Biclustering-Based Classification of Clinical Gene Expression Time Series

The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perspective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expression patterns in biomedical applications, and CCC-Biclustering arose as an efficient algorithm relying on the temporal nature of data to identify all maximal temporal patterns in gene expression time series. In this work, CCC-Biclustering was integrated in new biclustering-based classifiers for prognostic prediction. As case study we analyzed multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-β, to which nearly half of the patients reveal a negative response. In this scenario, using an effective predictive model of a patient’s response would avoid useless and possibly harmful therapies for the non-responder group. The results revealed interesting potentialities to be further explored in classification problems involving other (clinical) time series.

Global optimal eBURST analysis of multilocus typing data using a graphic matroid approach

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.

Ranked Adjusted Rand: integrating distance and partition information in a measure of clustering agreement

Background

Biological information is commonly used to cluster or classify entities of interest such as genes, conditions, species or samples. However, different sources of data can be used to classify the same set of entities and methods allowing the comparison of the performance of two data sources or the determination of how well a given classification agrees with another are frequently needed, especially in the absence of a universally accepted "gold standard" classification.

Results

Here, we describe a novel measure – the Ranked Adjusted Rand (RAR) index. RAR differs from existing methods by evaluating the extent of agreement between any two groupings, taking into account the intercluster distances. This characteristic is relevant to evaluate cases of pairs of entities grouped in the same cluster by one method and separated by another. The latter method may assign them to close neighbour clusters or, on the contrary, to clusters that are far apart from each other. RAR is applicable even when intercluster distance information is absent for both or one of the groupings. In the first case, RAR is equal to its predecessor, Adjusted Rand (HA) index. Artificially designed clusterings were used to demonstrate situations in which only RAR was able to detect differences in the grouping patterns. A study with larger simulated clusterings ensured that in realistic conditions, RAR is effectively integrating distance and partition information. The new method was applied to biological examples to compare 1) two microbial typing methods, 2) two gene regulatory network distances and 3) microarray gene expression data with pathway information. In the first application, one of the methods does not provide intercluster distances while the other originated a hierarchical clustering. RAR proved to be more sensitive than HA in the choice of a threshold for defining clusters in the hierarchical method that maximizes agreement between the results of both methods.

Conclusion

RAR has its major advantage in combining cluster distance and partition information, while the previously available methods used only the latter. RAR should be used in the research problems were HA was previously used, because in the absence of inter cluster distance effects it is an equally effective measure, and in the presence of distance effects it is a more complete one.

Illustration of a common framework for relating multiple typing methods by application to macrolide-resistant Streptococcus pyogenes

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.

Natural history of drug-resistant clones of Streptococcus pneumoniae colonizing healthy children in Portugal

A total of 3,539 Streptococcus pneumoniae (Pn) were recovered from 4,969 nasopharyngeal samples of children attending 13 day-care centers (DCCs) located in Lisbon, Portugal, during a surveillance study from January, 2001, through March, 2003, integrated in the European intervention project (EURIS, European Resistance Intervention Study). All Pn isolates were tested for anti-biotyping and drug-resistant pneumococci (DRPn) were further tested by serotyping and pulsed-field gel electrophoresis (PFGE). Overall carriage of Pn was very high (71.2%) and 39.9% of the isolates were resistant to antimicrobials (22.5% with decreased susceptibility to penicillin and 17.4% susceptible to penicillin and resistant to other antimicrobials). Serotypes 6B, 14, 23 F, 19F, and 19 A were prevalent among the 1,287 DRPn and 5.8% of the isolates were non-typeable. Eighty PFGE patterns were identified among 1,285 DRPn, and 93.1% of the DRPn belonged to 26 major clonal types that comprised: Pneumococcal Molecular Epidemiology Network (PMEN) clones (76.3%), Portuguese (PT)-DCC clones, previously detected in 1996-1999 (14.3%), and EURIS PT-DCC new clones, identified for the first time in the EURIS study, during 2001-2003 (9.4%). Comparing with previous Portuguese surveillance studies carried out since 1996, we observed that carriage increased from 47% to 71%, but no major changes were detected on the prevalence of pneumococcal serotypes. Moreover, although PMEN clones were predominant in all DCCs, in the present study the majority of them were gradually decreasing in time whereas several PT-DCC and new clones seemed to be increasing.

Assessment of band-based similarity coefficients for automatic type and subtype classification of microbial isolates analyzed by pulsed-field gel electrophoresis

Pulsed-field gel electrophoresis (PFGE) has been the typing method of choice for strain identification in epidemiological studies of several bacterial species of medical importance. The usual procedure for the comparison of strains and assignment of strain type and subtype relies on visual assessment of band difference number, followed by an incremental assignment to the group hosting the most similar type previously seen. Band-based similarity coefficients, such as the Dice or the Jaccard coefficient, are then used for dendrogram construction, which provides a quantitative assessment of strain similarity. PFGE type assignment is based on the definition of a threshold linkage value, below which strains are assigned to the same group. This is typically performed empirically by inspecting the hierarchical cluster analysis dendrogram containing the strains of interest. This approach has the problem that the threshold value selected is dependent on the linkage method used for dendrogram construction. Furthermore, the use of a linkage method skews the original similarity values between strains. In this paper we assess the goodness of classification of several band-based similarity coefficients by comparing it with the band difference number for PFGE type and subtype classification using receiver operating characteristic curves. The procedure described was applied to a collection of PFGE results for 1,798 isolates of Streptococcus pneumoniae, which documented 96 types and 396 subtypes. The band-based similarity coefficients were found to perform equally well for type classification, but with different proportions of false-positive and false-negative classifications in their minimal false discovery rate when they were used for subtype classification.

Properties of novel international drug-resistant pneumococcal clones identified in day-care centers of Lisbon, Portugal

In this study, 61 drug-resistant Streptococcus pneumoniae strains were characterized by multilocus sequence typing (MLST). These strains were representatives of 26 major clones (defined using pulsed-field gel electrophoresis) accounting for 93% of the 1,285 drug-resistant Streptococcus pneumoniae isolates recovered from the nasopharynges of healthy children attending day-care centers in Lisbon during 2001 to 2003. Using MLST, 13 of the 26 clones were found to be identical or closely related to 11 Pneumococcal Molecular Epidemiology Network (PMEN) clones, 4 clones were found to be unique as there were no identical or highly related allelic profiles deposited in the MLST database, and the remaining 9 clones had sequence types that matched or differed at a single or double locus from allelic profiles available in the MLST database. These nine clones were of serotypes 33F, 10A, 19A, 19F, 6A, 20, 24F, and 3, one was nontypeable, and, by MLST, they were found to be identical or highly related to isolates from disease origin that were dispersed internationally. Since the majority of these clones had serotypes that are not included in the 7-valent conjugate pneumococcal vaccine, monitoring of these clones is important for surveying their possible spread in the future. We propose the inclusion of these novel international clones in the PMEN.

Characterization of the genetic lineages responsible for pneumococcal invasive disease in Portugal

The availability of a conjugate vaccine has the potential to reduce the disease burden of pneumococci and to alter the serotype frequency in the disease-causing population through immunoselection. These changes will probably be reflected in the distributions of individual genetic lineages within the population. We present a characterization of a collection of recent (1999 to 2002) invasive isolates from Portugal (n = 465) by macrorestriction profiling with pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing. During this time, serotypes 14, 1, 3, 4, 8, 9V, 23F, 7F, 19A, and 12B were the 10 most prevalent overall by decreasing rank order. By combining the PFGE data with the sequence types (STs) of 104 isolates, we were able to identify the genetic lineages of the majority of the isolates. We found 66 STs, including 20 novel STs, corresponding to 47 different lineages by e-BURST analysis. We found in our collection a number of previously identified internationally disseminated lineages, especially among macrolide-resistant and penicillin-resistant isolates, and these accounted for most of the isolates. Most of the major lineages (17 of 25) were identified in all years of the study, suggesting that the pneumococcal population associated with invasive disease was stable. This study provides a characterization of the pneumococcal population associated with invasive disease that will be useful for detecting potential selective effects of the novel conjugate vaccine.

Effect of the seven-valent conjugate pneumococcal vaccine on carriage and drug resistance of Streptococcus pneumoniae in healthy children attending day-care centers in Lisbon

AIMS

Prospective study to evaluate the impact of the 7-valent pneumococcal conjugate vaccine (Prevenar) on the nasopharyngeal (NP) carriage of drug-resistant Streptococcus pneumoniae (DRPn), by healthy children attending day-care centers (ages 6 months-6 years).

METHODS

Vaccinees (238 children) who received vaccine and controls (457 children) were followed for carriage of total S. pneumoniae and DRPn and for the serotypes and genetic backgrounds of DRPn during 6 consecutive sampling periods between May 2001 and February 2003.

RESULTS

We detected no significant differences between vaccinees and the control group in the total carriage rate of Pn (average, 68%) or in the frequency of carriage of DRPn (average, 38%), including the frequency of penicillin-nonsusceptible strains (average, 24%). In contrast, there was a decline in the carriage of DRPn with vaccine serotypes which was compensated by the appearance and gradual increase in the frequency of DRPn expressing unusual serotypes (6A, 10A, 15A and 15C, 19A, 23A, 33F) which were not present in the vaccine as well as an increase in nontypable strains. The majority of the DRPn with unusual serotypes showed different pulsed field gel electrophoresis patterns indicating replacement of the original resistant flora by other clonal types of drug-resistant bacteria. Antibiotic consumption and the frequency of respiratory tract infections were similar among the vaccinees and controls.

CONCLUSIONS

Pneumococcal vaccination did not change the frequency of carriage of drug-resistant strains being the initially dominant vaccine serotypes replaced by others expressing nonvaccine serotypes. Reduction in the carriage of DRPn may require a combination of the conjugate vaccine and a decrease in antibiotic pressure.

Analysis of genomic sequences by Chaos Game Representation

MOTIVATION

Chaos Game Representation (CGR) is an iterative mapping technique that processes sequences of units, such as nucleotides in a DNA sequence or amino acids in a protein, in order to find the coordinates for their position in a continuous space. This distribution of positions has two properties: it is unique, and the source sequence can be recovered from the coordinates such that distance between positions measures similarity between the corresponding sequences. The possibility of using the latter property to identify succession schemes have been entirely overlooked in previous studies which raises the possibility that CGR may be upgraded from a mere representation technique to a sequence modeling tool.

RESULTS

The distribution of positions in the CGR plane were shown to be a generalization of Markov chain probability tables that accommodates non-integer orders. Therefore, Markov models are particular cases of CGR models rather than the reverse, as currently accepted. In addition, the CGR generalization has both practical (computational efficiency) and fundamental (scale independence) advantages. These results are illustrated by using Escherichia coli K-12 as a test data-set, in particular, the genes thrA, thrB and thrC of the threonine operon.