Background: 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. Results: 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. Conclusions: 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.

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.

Chaos Game Representation (CGR) is an iterated function that bijectively maps discrete sequences into a continuous domain. As a result, discrete sequences can be object of statistical and topological analyses otherwise reserved to numerical systems. Characteristically, CGR coordinates of substrings sharing an L-long suffix will be located within 2 -L distance of each other. In the two decades since its original proposal, CGR has been generalized beyond its original focus on genomic sequences and has been successfully applied to a wide range of problems in bioinformatics. This report explores the possibility that it can be further extended to approach algorithms that rely on discrete, graph-based representations.

The value of graph-based big data can be unlocked by exploring the topology and metrics of the networks they represent, and the computational approaches to this exploration take on many forms. For the use-case of performing global computations over a graph, it is first ingested into a graph processing system from one of many digital representations. Extracting information from graphs involves processing all their elements globally, which can be done with single-machine systems (with varying approaches to hardware usage), distributed systems (either homogeneous or heterogeneous groups of machines) and systems dedicated to high-performance computing (HPC). For these systems focused on processing the bulk of graph elements, common use-cases consist in executing for example algorithms for vertex ranking or community detection, which produce insights on graph structure and relevance of their elements. Many distributed systems (such as , ) and libraries (e.g. , ) have been built to enable these tasks and improve performance. This is achieved with techniques ranging from classic load balancing (often geared to reduce communication overhead) to exploring trade-offs between delaying computation and relaxing accuracy. In this survey we firstly familiarize the reader with common graph datasets and applications in the world of today. We provide an overview of different aspects of the graph processing landscape and describe classes of systems based on a set of dimensions we describe. The dimensions we detail encompass paradigms to express graph processing, different types of systems to use, coordination and communication models in distributed graph processing, partitioning techniques and different definitions related to the potential for a graph to be updated. This survey is aimed at both the experienced software engineer or researcher as well as the graduate student looking for an understanding of the landscape of solutions (and their limitations) for graph processing.