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July 7, 2019  |  

Clustering of circular consensus sequences: accurate error correction and assembly of single molecule real-time reads from multiplexed amplicon libraries.

Targeted resequencing with high-throughput sequencing (HTS) platforms can be used to efficiently interrogate the genomes of large numbers of individuals. A critical issue for research and applications using HTS data, especially from long-read platforms, is error in base calling arising from technological limits and bioinformatic algorithms. We found that the community standard long amplicon analysis (LAA) module from Pacific Biosciences is prone to substantial bioinformatic errors that raise concerns about findings based on this pipeline, prompting the need for a new method.A single molecule real-time (SMRT) sequencing-error correction and assembly pipeline, C3S-LAA, was developed for libraries of pooled amplicons. By uniquely leveraging the structure of SMRT sequence data (comprised of multiple low quality subreads from which higher quality circular consensus sequences are formed) to cluster raw reads, C3S-LAA produced accurate consensus sequences and assemblies of overlapping amplicons from single sample and multiplexed libraries. In contrast, despite read depths in excess of 100X per amplicon, the standard long amplicon analysis module from Pacific Biosciences generated unexpected numbers of amplicon sequences with substantial inaccuracies in the consensus sequences. A bootstrap analysis showed that the C3S-LAA pipeline per se was effective at removing bioinformatic sources of error, but in rare cases a read depth of nearly 400X was not sufficient to overcome minor but systematic errors inherent to amplification or sequencing.C3S-LAA uses a divide and conquer processing algorithm for SMRT amplicon-sequence data that generates accurate consensus sequences and local sequence assemblies. Solving the confounding bioinformatic source of error in LAA allowed for the identification of limited instances of errors due to DNA amplification or sequencing of homopolymeric nucleotide tracts. For research and development in genomics, C3S-LAA allows meaningful conclusions and biological inferences to be made from accurately polished sequence output.

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July 7, 2019  |  

Implementation of pharmacogenomics in everyday clinical settings.

Currently, germline pharmacogenomics (PGx) is successfully implemented within certain specialties in clinical care. With the integration of PGx in pharmacotherapy multiple stakeholders are involved, which are identified in this chapter. Clinically relevant pharmacogenes with their related PGx test are discussed, along with diagnostic test criteria to guide clinicians and policy makers in PGx test selection. The chapter further reviews the similarities and the differences between the guidelines of the Dutch Pharmacogenetics Working Group and the Clinical Pharmacogenetics Implementation Consortium which both support healthcare professionals in understanding PGx test results and help guiding pharmacotherapy by providing evidence-based dosing recommendations. Finally, clinical studies which provide scientific evidence and information on cost-effectiveness supporting clinical implementation of PGx in clinical care are discussed along with the remaining barriers for adoption of PGx testing by healthcare professionals.© 2018 Elsevier Inc. All rights reserved.

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July 7, 2019  |  

A universal SNP and small-indel variant caller using deep neural networks.

Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls. The approach, called DeepVariant, outperforms existing state-of-the-art tools. The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the wealth of human ground-truth data. We further show that DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, including deep whole genomes from 10X Genomics and Ion Ampliseq exomes, highlighting the benefits of using more automated and generalizable techniques for variant calling.

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July 7, 2019  |  

Spalter: A meta machine learning approach to distinguish true DNA variants from sequencing artefacts

Being able to distinguish between true DNA variants and technical sequencing artefacts is a fundamental task in whole genome, exome or targeted gene analysis. Variant calling tools provide diagnostic parameters, such as strand bias or an aggregated overall quality for each called variant, to help users make an informed choice about which variants to accept or discard. Having several such quality indicators poses a problem for the users of variant callers because they need to set or adjust thresholds for each such indicator. Alternatively, machine learning methods can be used to train a classifier based on these indicators. This approach needs large sets of labeled training data, which is not easily available. The new approach presented here relies on the idea that a true DNA variant exists independently of technical features of the read in which it appears (e.g. base quality, strand, position in the read). Therefore the nucleotide separability classification problem – predicting the nucleotide state of each read in a given pileup based on technical features only – should be near impossible to solve for true variants. Nucleotide separability, i.e. achievable classification accuracy, can either be used to distinguish between true variants and technical artefacts directly, using a thresholding approach, or it can be used as a meta-feature to train a separability-based classifier. This article explores both possibilities with promising results, showing accuracies around 90%.

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July 7, 2019  |  

STRetch: detecting and discovering pathogenic short tandem repeat expansions.

Short tandem repeat (STR) expansions have been identified as the causal DNA mutation in dozens of Mendelian diseases. Most existing tools for detecting STR variation with short reads do so within the read length and so are unable to detect the majority of pathogenic expansions. Here we present STRetch, a new genome-wide method to scan for STR expansions at all loci across the human genome. We demonstrate the use of STRetch for detecting STR expansions using short-read whole-genome sequencing data at known pathogenic loci as well as novel STR loci. STRetch is open source software, available from github.com/Oshlack/STRetch .

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July 7, 2019  |  

Traditional Norwegian kveik are a genetically distinct group of domesticated Saccharomyces cerevisiae brewing yeasts.

The widespread production of fermented food and beverages has resulted in the domestication of Saccharomyces cerevisiae yeasts specifically adapted to beer production. While there is evidence beer yeast domestication was accelerated by industrialization of beer, there also exists a farmhouse brewing culture in western Norway which has passed down yeasts referred to as kveik for generations. This practice has resulted in ale yeasts which are typically highly flocculant, phenolic off flavor negative (POF-), and exhibit a high rate of fermentation, similar to previously characterized lineages of domesticated yeast. Additionally, kveik yeasts are reportedly high-temperature tolerant, likely due to the traditional practice of pitching yeast into warm (>28°C) wort. Here, we characterize kveik yeasts from 9 different Norwegian sources via PCR fingerprinting, whole genome sequencing of selected strains, phenotypic screens, and lab-scale fermentations. Phylogenetic analysis suggests that kveik yeasts form a distinct group among beer yeasts. Additionally, we identify a novel POF- loss-of-function mutation, as well as SNPs and CNVs potentially relevant to the thermotolerance, high ethanol tolerance, and high fermentation rate phenotypes of kveik strains. We also identify domestication markers related to flocculation in kveik. Taken together, the results suggest that Norwegian kveik yeasts are a genetically distinct group of domesticated beer yeasts with properties highly relevant to the brewing sector.

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July 7, 2019  |  

Recombination hotspots in an extended human pseudoautosomal domain predicted from double-strand break maps and characterized by sperm-based crossover analysis.

The human X and Y chromosomes are heteromorphic but share a region of homology at the tips of their short arms, pseudoautosomal region 1 (PAR1), that supports obligate crossover in male meiosis. Although the boundary between pseudoautosomal and sex-specific DNA has traditionally been regarded as conserved among primates, it was recently discovered that the boundary position varies among human males, due to a translocation of ~110 kb from the X to the Y chromosome that creates an extended PAR1 (ePAR). This event has occurred at least twice in human evolution. So far, only limited evidence has been presented to suggest this extension is recombinationally active. Here, we sought direct proof by examining thousands of gametes from each of two ePAR-carrying men, for two subregions chosen on the basis of previously published male X-chromosomal meiotic double-strand break (DSB) maps. Crossover activity comparable to that seen at autosomal hotspots was observed between the X and the ePAR borne on the Y chromosome both at a distal and a proximal site within the 110-kb extension. Other hallmarks of classic recombination hotspots included evidence of transmission distortion and GC-biased gene conversion. We observed good correspondence between the male DSB clusters and historical recombination activity of this region in the X chromosomes of females, as ascertained from linkage disequilibrium analysis; this suggests that this region is similarly primed for crossover in both male and female germlines, although sex-specific differences may also exist. Extensive resequencing and inference of ePAR haplotypes, placed in the framework of the Y phylogeny as ascertained by both Y microsatellites and single nucleotide polymorphisms, allowed us to estimate a minimum rate of crossover over the entire ePAR region of 6-fold greater than genome average, comparable with pedigree estimates of PAR1 activity generally. We conclude ePAR very likely contributes to the critical crossover function of PAR1.

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July 7, 2019  |  

Picky comprehensively detects high-resolution structural variants in nanopore long reads.

Acquired genomic structural variants (SVs) are major hallmarks of cancer genomes, but they are challenging to reconstruct from short-read sequencing data. Here we exploited the long reads of the nanopore platform using our customized pipeline, Picky ( https://github.com/TheJacksonLaboratory/Picky ), to reveal SVs of diverse architecture in a breast cancer model. We identified the full spectrum of SVs with superior specificity and sensitivity relative to short-read analyses, and uncovered repetitive DNA as the major source of variation. Examination of genome-wide breakpoints at nucleotide resolution uncovered micro-insertions as the common structural features associated with SVs. Breakpoint density across the genome is associated with the propensity for interchromosomal connectivity and was found to be enriched in promoters and transcribed regions of the genome. Furthermore, we observed an over-representation of reciprocal translocations from chromosomal double-crossovers through phased SVs. We demonstrate that Picky analysis is an effective tool for comprehensive detection of SVs in cancer genomes from long-read data.

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July 7, 2019  |  

Signatures of selection and environmental adaptation across the goat genome post-domestication.

Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems and/or purposes and (ii) adaptation to different environments. As a result, approximately 600 goat breeds have developed worldwide; they differ considerably from one another in terms of phenotypic characteristics and are adapted to a wide range of climatic conditions. In this work, we analyzed the AdaptMap goat dataset, which is composed of data from more than 3000 animals collected worldwide and genotyped with the CaprineSNP50 BeadChip. These animals were partitioned into groups based on geographical area, production uses, available records on solid coat color and environmental variables including the sampling geographical coordinates, to investigate the role of natural and/or artificial selection in shaping the genome of goat breeds.Several signatures of selection on different chromosomal regions were detected across the different breeds, sub-geographical clusters, phenotypic and climatic groups. These regions contain genes that are involved in important biological processes, such as milk-, meat- or fiber-related production, coat color, glucose pathway, oxidative stress response, size, and circadian clock differences. Our results confirm previous findings in other species on adaptation to extreme environments and human purposes and provide new genes that could explain some of the differences between goat breeds according to their geographical distribution and adaptation to different environments.These analyses of signatures of selection provide a comprehensive first picture of the global domestication process and adaptation of goat breeds and highlight possible genes that may have contributed to the differentiation of this species worldwide.

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July 7, 2019  |  

Synaptogyrin-2 influences replication of Porcine circovirus 2.

Porcine circovirus 2 (PCV2) is a circular single-stranded DNA virus responsible for a group of diseases collectively known as PCV2 Associated Diseases (PCVAD). Variation in the incidence and severity of PCVAD exists between pigs suggesting a host genetic component involved in pathogenesis. A large-scale genome-wide association study of experimentally infected pigs (n = 974), provided evidence of a host genetic role in PCV2 viremia, immune response and growth during challenge. Host genotype explained 64% of the phenotypic variation for overall viral load, with two major Quantitative Trait Loci (QTL) identified on chromosome 7 (SSC7) near the swine leukocyte antigen complex class II locus and on the proximal end of chromosome 12 (SSC12). The SNP having the strongest association, ALGA0110477 (SSC12), explained 9.3% of the genetic and 6.2% of the phenotypic variance for viral load. Dissection of the SSC12 QTL based on gene annotation, genomic and RNA-sequencing, suggested that a missense mutation in the SYNGR2 (SYNGR2 p.Arg63Cys) gene is potentially responsible for the variation in viremia. This polymorphism, located within a protein domain conserved across mammals, results in an amino acid variant SYNGR2 p.63Cys only observed in swine. PCV2 titer in PK15 cells decreased when the expression of SYNGR2 was silenced by specific-siRNA, indicating a role of SYNGR2 in viral replication. Additionally, a PK15 edited clone generated by CRISPR-Cas9, carrying a partial deletion of the second exon that harbors a key domain and the SYNGR2 p.Arg63Cys, was associated with a lower viral titer compared to wildtype PK15 cells (>24 hpi) and supernatant (>48hpi)(P < 0.05). Identification of a non-conservative substitution in this key domain of SYNGR2 suggests that the SYNGR2 p.Arg63Cys variant may underlie the observed genetic effect on viral load.

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July 7, 2019  |  

iMGEins: detecting novel mobile genetic elements inserted in individual genomes.

Recent advances in sequencing technology have allowed us to investigate personal genomes to find structural variations, which have been studied extensively to identify their association with the physiology of diseases such as cancer. In particular, mobile genetic elements (MGEs) are one of the major constituents of the human genomes, and cause genome instability by insertion, mutation, and rearrangement.We have developed a new program, iMGEins, to identify such novel MGEs by using sequencing reads of individual genomes, and to explore the breakpoints with the supporting reads and MGEs detected. iMGEins is the first MGE detection program that integrates three algorithmic components: discordant read-pair mapping, split-read mapping, and insertion sequence assembly. Our evaluation results showed its outstanding performance in detecting novel MGEs from simulated genomes, as well as real personal genomes. In detail, the average recall and precision rates of iMGEins are 96.67 and 100%, respectively, which are the highest among the programs compared. In the testing with real human genomes of the NA12878 sample, iMGEins shows the highest accuracy in detecting MGEs within 20?bp proximity of the breakpoints annotated.In order to study the dynamics of MGEs in individual genomes, iMGEins was developed to accurately detect breakpoints and report inserted MGEs. Compared with other programs, iMGEins has valuable features of identifying novel MGEs and assembling the MGEs inserted.

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July 7, 2019  |  

Allele-level KIR genotyping of more than a million samples: Workflow, algorithm, and observations.

The killer-cell immunoglobulin-like receptor (KIR) genes regulate natural killer cell activity, influencing predisposition to immune mediated disease, and affecting hematopoietic stem cell transplantation (HSCT) outcome. Owing to the complexity of the KIR locus, with extensive gene copy number variation (CNV) and allelic diversity, high-resolution characterization of KIR has so far been applied only to relatively small cohorts. Here, we present a comprehensive high-throughput KIR genotyping approach based on next generation sequencing. Through PCR amplification of specific exons, our approach delivers both copy numbers of the individual genes and allelic information for every KIR gene. Ten-fold replicate analysis of a set of 190 samples revealed a precision of 99.9%. Genotyping of an independent set of 360 samples resulted in an accuracy of more than 99% taking into account consistent copy number prediction. We applied the workflow to genotype 1.8 million stem cell donor registry samples. We report on the observed KIR allele diversity and relative abundance of alleles based on a subset of more than 300,000 samples. Furthermore, we identified more than 2,000 previously unreported KIR variants repeatedly in independent samples, underscoring the large diversity of the KIR region that awaits discovery. This cost-efficient high-resolution KIR genotyping approach is now applied to samples of volunteers registering as potential donors for HSCT. This will facilitate the utilization of KIR as additional selection criterion to improve unrelated donor stem cell transplantation outcome. In addition, the approach may serve studies requiring high-resolution KIR genotyping, like population genetics and disease association studies.

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July 7, 2019  |  

De novo genome assembly of the olive fruit fly (Bactrocera oleae) developed through a combination of linked-reads and long-read technologies

Long-read sequencing has greatly contributed to the generation of high quality assemblies, albeit at a high cost. It is also not always clear how to combine sequencing platforms. We sequenced the genome of the olive fruit fly (Bactrocera oleae), the most important pest in the olive fruits agribusiness industry, using Illumina short-reads, mate-pairs, 10x Genomics linked-reads, Pacific Biosciences (PacBio), and Oxford Nanopore Technologies (ONT). The 10x linked-reads assembly gave the most contiguous assembly with an N50 of 2.16 Mb. Scaffolding the linked-reads assembly using long-reads from ONT gave a more contiguous assembly with scaffold N50 of 4.59 Mb. We also present the most extensive transcriptome datasets of the olive fly derived from different tissues and stages of development. Finally, we used the Chromosome Quotient method to identify Y-chromosome scaffolds and show that the long-reads based assembly generates very highly contiguous Y-chromosome assembly.

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July 7, 2019  |  

The Draft Genome of the MD-2 Pineapple

The main challenge in assembling plant genome is its ploidy level, repeats content, and polymorphism. The second-generation sequencing delivered the throughput and the accuracy that is crucial to whole-genome sequencing but insufficient and remained challenging for some plant species. It is known that genomes produced by next-gen- eration sequencing produced small contigs that would inflate the number of annotated genes (Varshney et al. 2011) and missed on the transposable elements that are abun- dant in plant genome due to their repetitive nature (Michael and Jackson 2013).

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