As genomics studies become more complex and consider multiple sources of biological and technical variation, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation with a genome-wide summary, and identify genes that deviate from the genome-wide trend. variancePartition enables rapid interpretation of complex gene expression studies and is applicable to many genomics assays.

Most plant-feeding insects are ecological specialists restricted to one or a few closely related host-plant species (Forister et al. 2015). A long-standing hypothesis asserts that natural selection favors host specialization because trade-offs between performance on alternative host species limit the fitness of generalists, yet empirical evidence for such trade-offs is scarce (Futuyma and Moreno 1988; Forister et al. 2012). Here we show that trade-offs between adaptations to alternative hosts occur over both long- and short-term macroevolutionary timescales, but positive associations between host-use traits are also abundant. Host-use records of 1604 caterpillar (Lepidoptera) species revealed negative associations between adaptations to two diverse groups of host-plant taxa over 150 million years (Misof et al. 2014) of caterpillar evolutionary history, but a different division between use of angiosperm and pine hosts among closely related caterpillars. In contrast, host-use records of 955 true bug (Hemiptera) species suggested uniformly positive associations between adaptations to the same host taxa both over the 300-million-year (Misof et al. 2014) evolutionary history of true bugs and among closely related species. The lack of consistent patterns across insect orders and timescales suggests that host-use trade-offs are historically contingent rather than universal constraints, reflecting the diversity of mechanisms driving host-specialization in plant-feeding insects.

Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools. These preconfigured workflows provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks, and, within the context of the same environment, to visualize and further interrogate their results. This bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications.

DNA methylation, a common modification of genomic DNA, is known to influence the expression of transposable elements as well as some genes. Although commonly viewed as an epigenetic mark, evidence has shown that underlying genetic variation, such as transposable element polymorphisms, often associate with differential DNA methylation states. To investigate the role of DNA methylation variation, transposable element polymorphism, and genomic diversity, whole genome bisulfite sequencing was performed on genetically diverse lines of the model cereal Brachypodium distachyon. Although DNA methylation profiles are broadly similar, thousands of differentially methylated regions are observed between lines. An analysis of novel transposable element indel variation highlighted hundreds of new polymorphisms not seen in the reference sequence. DNA methylation and transposable element variation is correlated with the genome-wide amount of genetic variation present between samples. However, there was minimal evidence that novel transposon insertion or deletions are associated with nearby differential methylation. This study highlights the importance of genetic variation when assessing DNA methylation variation between samples and provides a valuable map of DNA methylation across diverse re-sequenced accessions of this model cereal species.

Douglas-fir (Pseudotsuga menziesii) is a conifer tree native to western North America. In central Europe, it shows superior growth performance and is considered a suitable substitute for tree species impaired in vitality due to climate change. Maintenance and improvement of growth performance in a changing environment is a main challenge for forest tree breeders. In this context, genetic variation as a factor underlying phenotypic variation, but also as the basis for future adaptation, is of particular interest. The aims of this study were to analyse (i) genetic diversity of selected Douglas-fir provenances, (ii) variation in height growth among provenances, and (iii) to assess the link between genetic and phenotypic variation height growth. Genotyping was done on microsatellite loci. Effects of provenance, genotype, and site on height growth were assessed by fitting mixed linear models. The most significant genetic differentiation was observed between provenances of the coastal variety, versus a provenance of the interior variety originating from British Columbia. Although genetic differentiation among provenances of the coastal variety was lower, genetic structures within this variety were identified. Moreover, genetic diversity showed a latitudinal gradient with the southernmost provenances being more diverse, probably reflecting the species’ evolutionary history. The modelling approach revealed that height growth differed significantly by provenance, site, and the interaction between site and provenance, demonstrating that height growth is under strong genetic control. Additionally, this analysis showed that genetic variation captured by the genotyped microsatellite loci was significantly related to variation in height growth, providing statistical evidence for a genetic component in the observed phenotypic variation.

Metagenomic profiling is challenging in part because of the highly uneven sampling of the tree of life by genome sequencing projects and the limitations imposed by performing phylogenetic inference at fixed taxonomic ranks. We present the algorithm MetaPalette which uses long k-mer sizes (k=30, 50) to fit a k-mer “palette” of a given sample to the k-mer palette of reference organisms. By modeling the k-mer palettes of unknown organisms, the method also gives an indication of the presence, abundance, and evolutionary relatedness of novel organisms present in the sample. The method returns a traditional, fixed-rank taxonomic profile which is shown on independently simulated data to be one of the most accurate to date. Tree figures are also returned that quantify the relatedness of novel organisms to reference sequences and the accuracy of such figures is demonstrated on simulated spike-ins and a metagenomic soil sample. The software implementing MetaPalette is available at: https://github.com/dkoslicki/MetaPalette Pre-trained databases are included for Archaea, Bacteria, Eukaryota, and viruses.

Mendelian traits are considered as the lower end of the complexity spectrum of heritable phenotypes. However, more than a century after the rediscovery of Mendel’s law, the global landscape of monogenic variants as well as their effects and inheritance patterns within natural populations is still not well understood. Using the yeast Saccharomyces cerevisiae, we performed a species-wide survey of Mendelian traits across a large population of isolates. We generated offspring from 41 unique parental pairs, and analyzed 1,105 cross/trait combinations. We found that 8.9% of the cases were Mendelian. Most were caused by common variants showing stable inheritances in a natural population. However, we also found that a rare monogenic variant related to drug resistance displayed a significant and variable expressivity across different genetic backgrounds, leading to modified inheritances ranging from intermediate to high complexities. Our results illustrate for the first time the continuum of the hidden complexity of a monogenic mutation, where genotype is hardly predictive of phenotype.

Motivation: Analysis of next-generation sequencing data often results in a list of genomic regions. These may include differentially methylated CpGs/regions, transcription factor binding sites, interacting chromatin regions, or GWAS-associated SNPs, among others. A common analysis step is to annotate such genomic regions to genomic annotations (promoters, exons, enhancers, etc.). Existing tools are limited by requiring an artificial one-to-one region-to-annotation mapping, by a lack of visualization options to clearly and easily summarize annotations, by the time it takes to annotate regions, or by some combination thereof. Results: We have developed the annotatr R package to easily and quickly summarize, and visualize the association of genomic regions with genomic annotations. The annotatr package reports all intersections of regions and annotations, giving a better understanding of the genomic context of the regions. A variety of visualization functions are implemented in annotatr to easily plot numerical or categorical data associated with the regions across the annotations, providing insight into how characteristics of the regions differ across the annotations. We also demonstrate that annotatr is up to 11x faster than the comparable R package, ChIPpeakAnno. Overall, annotatr facilitates easy and fast genomic annotation of genomic regions, enabling a richer biological interpretation of experiments. Availability: http://www.github.com/rcavalcante/annotatr/