Bayesian inference of the evolution of HBV/E.

Despite its wide spread and high prevalence in sub-Saharan Africa, hepatitis B virus genotype E (HBV/E) has a surprisingly low genetic diversity, indicating an only recent emergence of this genotype in the general African population. Here, we performed extensive phylogeographic analyses, including Bayesian MCMC modeling. Our results indicate a mutation rate of 1.9 × 10(-4) substitutions per site and year (s/s/y) and confirm a recent emergence of HBV/E, most likely within the last 130 years, and only after the transatlantic slave-trade had come to an end. Our analyses suggest that HBV/E originated from the area of Nigeria, before rapidly spreading throughout sub-Saharan Africa. Interestingly, viral strains found in Haiti seem to be the result of multiple introductions only in the second half of the 20th century, corroborating an absence of a significant number of HBV/E strains in West Africa several centuries ago. Our results confirm that the hyperendemicity of HBV(E) in today's Africa is a recent phenomenon and likely the result of dramatic changes in the routes of viral transmission in a relatively recent past

Origin and sampling date of analyzed HBV genotype S full-length and S-gene sequences.

<p>Origin and sampling date of analyzed HBV genotype S full-length and S-gene sequences.</p

Bayesian skyline plot showing the epidemic history of the HBV/E S-gene dataset.

<p>The plot indicates the median estimate of the effective population size, with the 95% highest posterior density indicated in blue. The applied timeframe ranges between the most recent sampling date and the calculated 130 years of evolution from the most recent common ancestor (MRCA), as calculated in the HBV/E full-length analysis.</p

Phylogeographic spread of HBV/E.

<p>The snapshots represent the geographic and temporal spread of HBV/E for which at least the S-gene and the spatial and temporal sampling information were available. A mutation rate of 7×10⁻⁵ s/s/y with the GTR+G+I model with geographic information was used. Spread analysis by the SPREAD software was visualized using Map Resources.</p

Median Joining Network of HBV/E S-gene sequences.

<p>Pie charts represent sequence variants at the nodes, with colors indicating the country of sampling of individual sequences, the sizes reflecting the frequencies of the corresponding variants.</p

Phylogenetic analyses of all available HBV/E S-gene and full-length sequences.

<p>Analyses of S-gene (a) and full-length sequences (b) were performed using the GTR+G+I model with geographic information. Branching and roots of strains from individual countries are indicated by colors. Clusters with strains sampled in the same country and during the same year are collapsed.</p

Additional file 2: of Genome scaffolding and annotation for the pathogen vector Ixodes ricinus by ultra-long single molecule sequencing

Figure S1. Sequence distribution as percent identity between I. ricinus and I. scapularis.I. ricinus scaffolds were blasted against I. scapularis scaffolds. Only sequences passing the threshold of a maximum e-value of 1.0 e-5 and minimum 80% identity are included. (PDF 229 kb

GigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets

Background: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data with hundreds of millions of single-cell data points with >40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to downsample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. Results: We present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. Conclusions: GigaSOM.jl facilitates the use of commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from a massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies

Additional file 13: of Integration of Ixodes ricinus genome sequencing with transcriptome and proteome annotation of the naĂŻve midgut

FDR analysis of mass spectrometry search results. Mass spectrometry search results against concatenated target-decoy databases including protein scores and cumulative FDR. (XLSX 138 kb

Additional file 12: of Integration of Ixodes ricinus genome sequencing with transcriptome and proteome annotation of the naïve midgut

Combined direct acyclic graphs for the different GO categories “molecular function” (A), “biological process” (B) and “cellular component” (C). (PDF 90 kb