Sequencing and de novo assembly of 150 genomes from Denmark as a population reference.

Hundreds of thousands of human genomes are now being sequenced to characterize genetic variation and use this information to augment association mapping studies of complex disorders and other phenotypic traits. Genetic variation is identified mainly by mapping short reads to the reference genome or by performing local assembly. However, these approaches are biased against discovery of structural variants and variation in the more complex parts of the genome. Hence, large-scale de novo assembly is needed. Here we show that it is possible to construct excellent de novo assemblies from high-coverage sequencing with mate-pair libraries extending up to 20 kilobases. We report de novo assemblies of 150 individuals (50 trios) from the GenomeDenmark project. The quality of these assemblies is similar to those obtained using the more expensive long-read technology. We use the assemblies to identify a rich set of structural variants including many novel insertions and demonstrate how this variant catalogue enables further deciphering of known association mapping signals. We leverage the assemblies to provide 100 completely resolved major histocompatibility complex haplotypes and to resolve major parts of the Y chromosome. Our study provides a regional reference genome that we expect will improve the power of future association mapping studies and hence pave the way for precision medicine initiatives, which now are being launched in many countries including Denmark.

Accurate genotyping across variant classes and lengths using variant graphs.

Genotype estimates from short-read sequencing data are typically based on the alignment of reads to a linear reference, but reads originating from more complex variants (for example, structural variants) often align poorly, resulting in biased genotype estimates. This bias can be mitigated by first collecting a set of candidate variants across discovery methods, individuals and databases, and then realigning the reads to the variants and reference simultaneously. However, this realignment problem has proved computationally difficult. Here, we present a new method (BayesTyper) that uses exact alignment of read k-mers to a graph representation of the reference and variants to efficiently perform unbiased, probabilistic genotyping across the variation spectrum. We demonstrate that BayesTyper generally provides superior variant sensitivity and genotyping accuracy relative to existing methods when used to integrate variants across discovery approaches and individuals. Finally, we demonstrate that including a 'variation-prior' database containing already known variants significantly improves sensitivity.

Coalescent Inference Using Serially Sampled, High-Throughput Sequencing Data from Intrahost HIV Infection.

Human immunodeficiency virus (HIV) is a rapidly evolving pathogen that causes chronic infections, so genetic diversity within a single infection can be very high. High-throughput "deep" sequencing can now measure this diversity in unprecedented detail, particularly since it can be performed at different time points during an infection, and this offers a potentially powerful way to infer the evolutionary dynamics of the intrahost viral population. However, population genomic inference from HIV sequence data is challenging because of high rates of mutation and recombination, rapid demographic changes, and ongoing selective pressures. In this article we develop a new method for inference using HIV deep sequencing data, using an approach based on importance sampling of ancestral recombination graphs under a multilocus coalescent model. The approach further extends recent progress in the approximation of so-called conditional sampling distributions, a quantity of key interest when approximating coalescent likelihoods. The chief novelties of our method are that it is able to infer rates of recombination and mutation, as well as the effective population size, while handling sampling over different time points and missing data without extra computational difficulty. We apply our method to a data set of HIV-1, in which several hundred sequences were obtained from an infected individual at seven time points over 2 years. We find mutation rate and effective population size estimates to be comparable to those produced by the software BEAST. Additionally, our method is able to produce local recombination rate estimates. The software underlying our method, Coalescenator, is freely available.

RNA Sequencing of Trigeminal Ganglia in Rattus Norvegicus after Glyceryl Trinitrate Infusion with Relevance to Migraine.

INTRODUCTION: Infusion of glyceryl trinitrate (GTN), a donor of nitric oxide, induces immediate headache in humans that in migraineurs is followed by a delayed migraine attack. In order to achieve increased knowledge of mechanisms activated during GTN-infusion this present study aims to investigate transcriptional responses to GTN-infusion in the rat trigeminal ganglia. METHODS: Rats were infused with GTN or vehicle and trigeminal ganglia were isolated either 30 or 90 minutes post infusion. RNA sequencing was used to investigate transcriptomic changes in response to the treatment. Furthermore, we developed a novel method for Gene Set Analysis Of Variance (GSANOVA) to identify gene sets associated with transcriptional changes across time. RESULTS: 15 genes displayed significant changes in transcription levels in response to GTN-infusion. Ten of these genes showed either sustained up- or down-regulation in the 90-minute period after infusion. The GSANOVA analysis demonstrate enrichment of pathways pointing towards an increase in immune response, signal transduction, and neuroplasticity in response to GTN-infusion. Future functional in-depth studies of these mechanisms are expected to increase our understanding of migraine pathogenesis.

Next-generation biology: Sequencing and data analysis approaches for non-model organisms.

As sequencing technologies become more affordable, it is now realistic to propose studying the evolutionary history of virtually any organism on a genomic scale. However, when dealing with non-model organisms it is not always easy to choose the best approach given a specific biological question, a limited budget, and challenging sample material. Furthermore, although recent advances in technology offer unprecedented opportunities for research in non-model organisms, they also demand unprecedented awareness from the researcher regarding the assumptions and limitations of each method. In this review we present an overview of the current sequencing technologies and the methods used in typical high-throughput data analysis pipelines. Subsequently, we contextualize high-throughput DNA sequencing technologies within their applications in non-model organism biology. We include tips regarding managing unconventional sample material, comparative and population genetic approaches that do not require fully assembled genomes, and advice on how to deal with low depth sequencing data.

Bayesian transcriptome assembly.

RNA sequencing allows for simultaneous transcript discovery and quantification, but reconstructing complete transcripts from such data remains difficult. Here, we introduce Bayesembler, a novel probabilistic method for transcriptome assembly built on a Bayesian model of the RNA sequencing process. Under this model, samples from the posterior distribution over transcripts and their abundance values are obtained using Gibbs sampling. By using the frequency at which transcripts are observed during sampling to select the final assembly, we demonstrate marked improvements in sensitivity and precision over state-of-the-art assemblers on both simulated and real data. Bayesembler is available at https://github.com/bioinformatics-centre/bayesembler.