Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers.

What does it take to convert a heap of sequencing data into a publishable result? First, common tools are employed to reduce primary data (sequencing reads) to a form suitable for further analyses (i.e., the list of variable sites). The subsequent exploratory stage is much more ad hoc and requires the development of custom scripts and pipelines, making it problematic for biomedical researchers. Here, we describe a hybrid platform combining common analysis pathways with the ability to explore data interactively. It aims to fully encompass and simplify the "raw data-to-publication" pathway and make it reproducible.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update.

High-throughput data production technologies, particularly 'next-generation' DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse. In this report we highlight recently added features enabling biomedical analyses on a large scale.

Web-based visual analysis for high-throughput genomics.

BACKGROUND: Visualization plays an essential role in genomics research by making it possible to observe correlations and trends in large datasets as well as communicate findings to others. Visual analysis, which combines visualization with analysis tools to enable seamless use of both approaches for scientific investigation, offers a powerful method for performing complex genomic analyses. However, there are numerous challenges that arise when creating rich, interactive Web-based visualizations/visual analysis applications for high-throughput genomics. These challenges include managing data flow from Web server to Web browser, integrating analysis tools and visualizations, and sharing visualizations with colleagues. RESULTS: We have created a platform simplifies the creation of Web-based visualization/visual analysis applications for high-throughput genomics. This platform provides components that make it simple to efficiently query very large datasets, draw common representations of genomic data, integrate with analysis tools, and share or publish fully interactive visualizations. Using this platform, we have created a Circos-style genome-wide viewer, a generic scatter plot for correlation analysis, an interactive phylogenetic tree, a scalable genome browser for next-generation sequencing data, and an application for systematically exploring tool parameter spaces to find good parameter values. All visualizations are interactive and fully customizable. The platform is integrated with the Galaxy (http://galaxyproject.org) genomics workbench, making it easy to integrate new visual applications into Galaxy. CONCLUSIONS: Visualization and visual analysis play an important role in high-throughput genomics experiments, and approaches are needed to make it easier to create applications for these activities. Our framework provides a foundation for creating Web-based visualizations and integrating them into Galaxy. Finally, the visualizations we have created using the framework are useful tools for high-throughput genomics experiments.

Rapid and robust phylotyping of spa t003, a dominant MRSA clone in Luxembourg and other European countries.

BACKGROUND: spa typing is a common genotyping tool for methicillin-resistant Staphylococcus aureus (MRSA) in Europe. Given the high prevalence of dominant clones, spa-typing is proving to be limited in its ability to distinguish outbreak isolates from background isolates. New molecular tools need to be employed to improve subtyping of dominant local MRSA strains (e.g., spa type t003). METHODS: Phylogenetically critical, or canonical, SNPs (can-SNPs) were identified as subtyping targets through sequence analysis of 40 MRSA whole genomes from Luxembourg. Real-time PCR assays were designed around target SNPs and validated using a repository of 240 previously sub-typed and epidemiologically characterized Luxembourg MRSA isolates, including 153 community and hospital isolates, 69 isolates from long term care (LTC) facilities, and 21 prospectively analyzed MRSA isolates. Selected isolates were also analyzed by whole genome SNP typing (WGST) for comparison to the SNP assays and other subtyping techniques. RESULTS: Fourteen real-time PCR assays were developed and validated, including two assays to determine presence of spa t003 or t008. The other twelve assays successfully provided a high degree of resolution within the t003 subtype. WGST analysis of the LTC facility isolates provided greater resolution than other subtyping tools, identifying clusters indicative of ongoing transmission within LTC facilities. CONCLUSIONS: canSNP-based PCR assays are useful for local level MRSA phylotyping, especially in the presence of one or more dominant clones. The assays designed here can be easily adapted for investigating t003 MRSA strains in other regions in Western Europe. WGST provides substantially better resolution than other typing methods.

The evolutionary history of amino acid variations mediating increased resistance of S. aureus identifies reversion mutations in metabolic regulators.

The evolution of resistance in Staphylococcus aureus occurs rapidly, and in response to all known antimicrobial treatments. Numerous studies of model species describe compensatory roles of mutations in mediating competitive fitness, and there is growing evidence that these mutation types also drive adaptation of S. aureus strains. However, few studies have tracked amino acid changes during the complete evolutionary trajectory of antibiotic adaptation or been able to predict their functional relevance. Here, we have assessed the efficacy of computational methods to predict biological resistance of a collection of clinically known Resistance Associated Mutations (RAMs). We have found that >90% of known RAMs are incorrectly predicted to be functionally neutral by at least one of the prediction methods used. By tracing the evolutionary histories of all of the false negative RAMs, we have discovered that a significant number are reversion mutations to ancestral alleles also carried in the MSSA476 methicillin-sensitive isolate. These genetic reversions are most prevalent in strains following daptomycin treatment and show a tendency to accumulate in biological pathway reactions that are distinct from those accumulating non-reversion mutations. Our studies therefore show that in addition to non-reversion mutations, reversion mutations arise in isolates exposed to new antibiotic treatments. It is possible that acquisition of reversion mutations in the genome may prevent substantial fitness costs during the progression of resistance. Our findings pose an interesting question to be addressed by further clinical studies regarding whether or not these reversion mutations lead to a renewed vulnerability of a vancomycin or daptomycin resistant strain to antibiotics administered at an earlier stage of infection.