Increasing the Power to Detect Causal Associations by Combining Genotypic and Expression Data in Segregating Populations

To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models

Ein neues, unkompliziertes Verfahren zur Bestimmung der Zusammensetzung binärer Flüssigkeitsgemische

Ein neues Verfahren zur Bestimmung der Zusammensetzung binärer Flüssigkeitsgemische mit Hilfe solvatochromer Farbstoffe wird beschrieben. Die Analyse erfolgt durch einfache UV/VIS-Absorptionsmessung und ist unter Verwendung einer Zwei-Parameter-Gleichung ein exakter Schnelltest

Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboar

A survey of visualization tools for biological network analysis

The analysis and interpretation of relationships between biological molecules, networks and concepts is becoming a major bottleneck in systems biology. Very often the pure amount of data and their heterogeneity provides a challenge for the visualization of the data. There are a wide variety of graph representations available, which most often map the data on 2D graphs to visualize biological interactions. These methods are applicable to a wide range of problems, nevertheless many of them reach a limit in terms of user friendliness when thousands of nodes and connections have to be analyzed and visualized. In this study we are reviewing visualization tools that are currently available for visualization of biological networks mainly invented in the latest past years. We comment on the functionality, the limitations and the specific strengths of these tools, and how these tools could be further developed in the direction of data integration and information sharing

Genomic microsatellites identify shared Jewish ancestry intermediate between Middle Eastern and European populations

<p>Abstract</p> <p>Background</p> <p>Genetic studies have often produced conflicting results on the question of whether distant Jewish populations in different geographic locations share greater genetic similarity to each other or instead, to nearby non-Jewish populations. We perform a genome-wide population-genetic study of Jewish populations, analyzing 678 autosomal microsatellite loci in 78 individuals from four Jewish groups together with similar data on 321 individuals from 12 non-Jewish Middle Eastern and European populations.</p> <p>Results</p> <p>We find that the Jewish populations show a high level of genetic similarity to each other, clustering together in several types of analysis of population structure. Further, Bayesian clustering, neighbor-joining trees, and multidimensional scaling place the Jewish populations as intermediate between the non-Jewish Middle Eastern and European populations.</p> <p>Conclusion</p> <p>These results support the view that the Jewish populations largely share a common Middle Eastern ancestry and that over their history they have undergone varying degrees of admixture with non-Jewish populations of European descent.</p

Multilocus Microsatellite Typing (MLMT) of Strains from Turkey and Cyprus Reveals a Novel Monophyletic L. donovani Sensu Lato Group

In eastern Mediterranean, leishmaniasis represents a major public health problem with considerable impact on morbidity and potential to spread. Cutaneous leishmaniasis (CL) caused by L. major or L. tropica accounts for most cases in this region although visceral leishmaniasis (VL) caused by L. infantum is also common. New foci of human CL caused by L. donovani complex strains were recently described in Cyprus and Turkey. Herein we analyzed Turkish strains from human CL foci in Çukurova region (north of Cyprus) and a human VL case in Kuşadasi. These were compared to Cypriot strains that were previously typed by Multilocus Enzyme Electrophoresis (MLEE) as L. donovani MON-37. Nevertheless, they were found genetically distinct from MON-37 strains of other regions and therefore their origin remained enigmatic. A population study was performed by Multilocus Microsatellite Typing (MLMT) and the profile of the Turkish strains was compared to previously analyzed L. donovani complex strains. Our results revealed close genetic relationship between Turkish and Cypriot strains, which form a genetically distinct L. infantum monophyletic group, suggesting that Cypriot strains may originate from Turkey. Our analysis indicates that the epidemiology of leishmaniasis in this region is more complicated than originally thought

The Evolutionary Dynamics of the Lion Panthera leo Revealed by Host and Viral Population Genomics

The lion Panthera leo is one of the world's most charismatic carnivores and is one of Africa's key predators. Here, we used a large dataset from 357 lions comprehending 1.13 megabases of sequence data and genotypes from 22 microsatellite loci to characterize its recent evolutionary history. Patterns of molecular genetic variation in multiple maternal (mtDNA), paternal (Y-chromosome), and biparental nuclear (nDNA) genetic markers were compared with patterns of sequence and subtype variation of the lion feline immunodeficiency virus (FIVPle), a lentivirus analogous to human immunodeficiency virus (HIV). In spite of the ability of lions to disperse long distances, patterns of lion genetic diversity suggest substantial population subdivision (mtDNA ΦST = 0.92; nDNA FST = 0.18), and reduced gene flow, which, along with large differences in sero-prevalence of six distinct FIVPle subtypes among lion populations, refute the hypothesis that African lions consist of a single panmictic population. Our results suggest that extant lion populations derive from several Pleistocene refugia in East and Southern Africa (∼324,000–169,000 years ago), which expanded during the Late Pleistocene (∼100,000 years ago) into Central and North Africa and into Asia. During the Pleistocene/Holocene transition (∼14,000–7,000 years), another expansion occurred from southern refugia northwards towards East Africa, causing population interbreeding. In particular, lion and FIVPle variation affirms that the large, well-studied lion population occupying the greater Serengeti Ecosystem is derived from three distinct populations that admixed recently

Effect of Anthropogenic Landscape Features on Population Genetic Differentiation of Przewalski's Gazelle: Main Role of Human Settlement

Anthropogenic landscapes influence evolutionary processes such as population genetic differentiation, however, not every type of landscape features exert the same effect on a species, hence it is necessary to estimate their relative effect for species management and conservation. Przewalski's gazelle (Procapra przewalskii), which inhabits a human-altered area on Qinghai-Tibet Plateau, is one of the most endangered antelope species in the world. Here, we report a landscape genetic study on Przewalski's gazelle. We used skin and fecal samples of 169 wild gazelles collected from nine populations and thirteen microsatellite markers to assess the genetic effect of anthropogenic landscape features on this species. For comparison, the genetic effect of geographical distance and topography were also evaluated. We found significant genetic differentiation, six genetic groups and restricted dispersal pattern in Przewalski's gazelle. Topography, human settlement and road appear to be responsible for observed genetic differentiation as they were significantly correlated with both genetic distance measures [FST/(1−FST) and F′ST/(1−F′ST)] in Mantel tests. IBD (isolation by distance) was also inferred as a significant factor in Mantel tests when genetic distance was measured as FST/(1−FST). However, using partial Mantel tests, AICc calculations, causal modeling and AMOVA analysis, we found that human settlement was the main factor shaping current genetic differentiation among those tested. Altogether, our results reveal the relative influence of geographical distance, topography and three anthropogenic landscape-type on population genetic differentiation of Przewalski's gazelle and provide useful information for conservation measures on this endangered species