Panoptes: web-based exploration of large scale genome variation data.

Motivation: The size and complexity of modern large-scale genome variation studies demand novel approaches for exploring and sharing the data. In order to unlock the potential of these data for a broad audience of scientists with various areas of expertise, a unified exploration framework is required that is accessible, coherent and user-friendly.

Results: Panoptes is an open-source software framework for collaborative visual exploration of large-scale genome variation data and associated metadata in a web browser.

Indels, structural variation, and recombination drive genomic diversity in Plasmodium falciparum.

The malaria parasite Plasmodium falciparum has a great capacity for evolutionary adaptation to evade host immunity and develop drug resistance. Current understanding of parasite evolution is impeded by the fact that a large fraction of the genome is either highly repetitive or highly variable and thus difficult to analyze using short-read sequencing technologies. Here, we describe a resource of deep sequencing data on parents and progeny from genetic crosses, which has enabled us to perform the first genome-wide, integrated analysis of SNP, indel and complex polymorphisms, using Mendelian error rates as an indicator of genotypic accuracy.

High Resolution Typing by Whole Genome Mapping Enables Discrimination of LA-MRSA (CC398) Strains and Identification of Transmission Events.

After its emergence in 2003, a livestock-associated (LA-)MRSA clade (CC398) has caused an impressive increase in the number of isolates submitted for the Dutch national MRSA surveillance and now comprises 40% of all isolates. The currently used molecular typing techniques have limited discriminatory power for this MRSA clade, which hampers studies on the origin and transmission routes. Recently, a new molecular analysis technique named whole genome mapping was introduced.

A resampling strategy for reliable network construction.

In the classical approach to tree reconstruction schemes, such as pair group methods, maximum parsimony or minimum spanning trees, two major problems are not addressed at a fundamental level. First, for numerous kinds of experimental data, these methods produce equivalent solutions, but provide no way of handling those degeneracies. Second, the real-life data fed to these methods is treated as exact data, and possible measurement errors cannot be taken into account.

Bacterial species identification from MALDI-TOF mass spectra through data analysis and machine learning.

At present, there is much variability between MALDI-TOF MS methodology for the characterization of bacteria through differences in e.g., sample preparation methods, matrix solutions, organic solvents, acquisition methods and data analysis methods.