Cátia Vaz

PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods

BackgroundWith 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.ResultsPHYLOViZ 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.ConclusionsPHYLOViZ 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.

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.

On the expressive power of primitives for compensation handling

Modern software systems have frequently to face unexpected events, reacting so to reach a consistent state. In the field of concurrent and mobile systems (e.g., for web services) the problem is usually tackled using long running transactions and compensations: activities programmed to recover partial executions of long running transactions. We compare the expressive power of different approaches to the specification of those compensations. We consider (i) static recovery, where the compensation is statically defined together with the transaction, (ii) parallel recovery, where the compensation is dynamically built as parallel composition of compensation elements and (iii) general dynamic recovery, where more refined ways of composing compensation elements are provided. We define an encoding of parallel recovery into static recovery enjoying nice compositionality properties, showing that the two approaches have the same expressive power. We also show that no such encoding of general dynamic recovery into static recovery is possible, i.e. general dynamic recovery is strictly more expressive.

Dynamic Recovering of Long Running Transactions

Most business applications rely on the notion of long running transaction as a fundamental building block. This paper presents a calculus for modelling long running transactions within the framework of the *** -calculus, with support for compensation as a recovery mechanism. The underlying model of this calculus is the asynchronous polyadic *** -calculus, with transaction scopes and dynamic installation of compensation processes. We add to the framework a type system which guarantees that transactions are unequivocally identified, ensuring that upon a failure the correct compensation process is invoked. Moreover, the operational semantics of the calculus ensures both installation and activation of the compensation of a transaction.

PHYLOViZ Online: web-based tool for visualization, phylogenetic inference, analysis and sharing of minimum spanning trees

High-throughput sequencing methods generated allele and single nucleotide polymorphism information for thousands of bacterial strains that are publicly available in online repositories and created the possibility of generating similar information for hundreds to thousands of strains more in a single study. Minimum spanning tree analysis of allelic data offers a scalable and reproducible methodological alternative to traditional phylogenetic inference approaches, useful in epidemiological investigations and population studies of bacterial pathogens. PHYLOViZ Online was developed to allow users to do these analyses without software installation and to enable easy accessing and sharing of data and analyses results from any Internet enabled computer. PHYLOViZ Online also offers a RESTful API for programmatic access to data and algorithms, allowing it to be seamlessly integrated into any third party web service or software. PHYLOViZ Online is freely available at https://online.phyloviz.net.

Towards compensation correctness in interactive systems

One fundamental idea of service-oriented computing is that applications should be developed by composing already available services. Due to the long running nature of service interactions, a main challenge in service composition is ensuring correctness of failure recovery. In this paper, we use a process calculus suitable for modelling long running transactions with a recovery mechanism based on compensations. Within this setting, we discuss and formally state correctness criteria for compensable processes compositions, assuming that each process is correct with respect to failure recovery. Under our theory, we formally interpret self-healing compositions, that can detect and recover from failures, as correct compositions of compensable processes.

TypOn: the microbial typing ontology

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.

An ontology and a REST API for sequence based microbial typing data

In the Microbial typing field, the need to have a common understanding of the concepts described and the ability to share results within the community is an increasingly important requisite for the continued development of portable and accurate sequence-based typing methods. These methods are used for bacterial strain identification and are fundamental tools in Clinical Microbiology and Bacterial Population Genetics studies. In this article we propose an ontology designed for the microbial typing field, focusing on the widely used Multi Locus Sequence Typing methodology, and a RESTful API for accessing information systems based on the proposed ontology. This constitutes an important first step to accurately describe, analyze, curate, and manage information for microbial typing methodologies based on sequence based typing methodologies, and allows for the future integration with data analysis Web services.

Towards Distance-Based Phylogenetic Inference in Average-Case Linear-Time

Computing genetic evolution distances among a set of taxa dominates the running time of many phylogenetic inference methods. Most of genetic evolution distance definitions rely, even if indirectly, on computing the pairwise Hamming distance among sequences or profiles. 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.

Fast phylogenetic inference from typing data

BackgroundMicrobial 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. ResultsWe 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.