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Auto Tag: Variant detection

July 7, 2019

Updated reference genome sequence and annotation of Mycobacterium bovis AF2122/97.

We report here an update to the reference genome sequence of the bovine tuberculosis bacillus Mycobacterium bovis AF2122/97, generated using an integrative multiomics approach. The update includes 42 new coding sequences (CDSs), 14 modified annotations, 26 single-nucleotide polymorphism (SNP) corrections, and disclosure that the RD900 locus, previously described as absent from the genome, is in fact present. Copyright © 2017 Malone et al.

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July 7, 2019

Hybrid assembly with long and short reads improves discovery of gene family expansions.

Long-read and short-read sequencing technologies offer competing advantages for eukaryotic genome sequencing projects. Combinations of both may be appropriate for surveys of within-species genomic variation.We developed a hybrid assembly pipeline called “Alpaca” that can operate on 20X long-read coverage plus about 50X short-insert and 50X long-insert short-read coverage. To preclude collapse of tandem repeats, Alpaca relies on base-call-corrected long reads for contig formation.Compared to two other assembly protocols, Alpaca demonstrated the most reference agreement and repeat capture on the rice genome. On three accessions of the model legume Medicago truncatula, Alpaca generated the most agreement to a conspecific reference and predicted tandemly repeated genes absent from the other assemblies.Our results suggest Alpaca is a useful tool for investigating structural and copy number variation within de novo assemblies of sampled populations.

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July 7, 2019

CLOVE: classification of genomic fusions into structural variation events.

A precise understanding of structural variants (SVs) in DNA is important in the study of cancer and population diversity. Many methods have been designed to identify SVs from DNA sequencing data. However, the problem remains challenging because existing approaches suffer from low sensitivity, precision, and positional accuracy. Furthermore, many existing tools only identify breakpoints, and so not collect related breakpoints and classify them as a particular type of SV. Due to the rapidly increasing usage of high throughput sequencing technologies in this area, there is an urgent need for algorithms that can accurately classify complex genomic rearrangements (involving more than one breakpoint or fusion).We present CLOVE, an algorithm for integrating the results of multiple breakpoint or SV callers and classifying the results as a particular SV. CLOVE is based on a graph data structure that is created from the breakpoint information. The algorithm looks for patterns in the graph that are characteristic of more complex rearrangement types. CLOVE is able to integrate the results of multiple callers, producing a consensus call.We demonstrate using simulated and real data that re-classified SV calls produced by CLOVE improve on the raw call set of existing SV algorithms, particularly in terms of accuracy. CLOVE is freely available from http://www.github.com/PapenfussLab .

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July 7, 2019

Genome graphs

There is increasing recognition that a single, monoploid reference genome is a poor universal reference structure for human genetics, because it represents only a tiny fraction of human variation. Adding this missing variation results in a structure that can be described as a mathematical graph: a genome graph. We demonstrate that, in comparison to the existing reference genome (GRCh38), genome graphs can substantially improve the fractions of reads that map uniquely and perfectly. Furthermore, we show that this fundamental simplification of read mapping transforms the variant calling problem from one in which many non-reference variants must be discovered de-novo to one in which the vast majority of variants are simply re-identified within the graph. Using standard benchmarks as well as a novel reference-free evaluation, we show that a simplistic variant calling procedure on a genome graph can already call variants at least as well as, and in many cases better than, a state-of-the-art method on the linear human reference genome. We anticipate that graph-based references will supplant linear references in humans and in other applications where cohorts of sequenced individuals are available.

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July 7, 2019

The MHC locus and genetic susceptibility to autoimmune and infectious diseases.

In the past 50 years, variants in the major histocompatibility complex (MHC) locus, also known as the human leukocyte antigen (HLA), have been reported as major risk factors for complex diseases. Recent advances, including large genetic screens, imputation, and analyses of non-additive and epistatic effects, have contributed to a better understanding of the shared and specific roles of MHC variants in different diseases. We review these advances and discuss the relationships between MHC variants involved in autoimmune and infectious diseases. Further work in this area will help to distinguish between alternative hypotheses for the role of pathogens in autoimmune disease development.

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July 7, 2019

A large gene family in fission yeast encodes spore killers that subvert Mendel’s law.

Spore killers in fungi are selfish genetic elements that distort Mendelian segregation in their favor. It remains unclear how many species harbor them and how diverse their mechanisms are. Here, we discover two spore killers from a natural isolate of the fission yeast Schizosaccharomyces pombe. Both killers belong to the previously uncharacterized wtf gene family with 25 members in the reference genome. These two killers act in strain-background-independent and genome-location-independent manners to perturb the maturation of spores not inheriting them. Spores carrying one killer are protected from its killing effect but not that of the other killer. The killing and protecting activities can be uncoupled by mutation. The numbers and sequences of wtf genes vary considerably between S. pombe isolates, indicating rapid divergence. We propose that wtf genes contribute to the extensive intraspecific reproductive isolation in S. pombe, and represent ideal models for understanding how segregation-distorting elements act and evolve.

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July 7, 2019

Comparative genomics of Burkholderia multivorans, a ubiquitous pathogen with a highly conserved genomic structure.

The natural environment serves as a reservoir of opportunistic pathogens. A well-established method for studying the epidemiology of such opportunists is multilocus sequence typing, which in many cases has defined strains predisposed to causing infection. Burkholderia multivorans is an important pathogen in people with cystic fibrosis (CF) and its epidemiology suggests that strains are acquired from non-human sources such as the natural environment. This raises the central question of whether the isolation source (CF or environment) or the multilocus sequence type (ST) of B. multivorans better predicts their genomic content and functionality. We identified four pairs of B. multivorans isolates, representing distinct STs and consisting of one CF and one environmental isolate each. All genomes were sequenced using the PacBio SMRT sequencing technology, which resulted in eight high-quality B. multivorans genome assemblies. The present study demonstrated that the genomic structure of the examined B. multivorans STs is highly conserved and that the B. multivorans genomic lineages are defined by their ST. Orthologous protein families were not uniformly distributed among chromosomes, with core orthologs being enriched on the primary chromosome and ST-specific orthologs being enriched on the second and third chromosome. The ST-specific orthologs were enriched in genes involved in defense mechanisms and secondary metabolism, corroborating the strain-specificity of these virulence characteristics. Finally, the same B. multivorans genomic lineages occur in both CF and environmental samples and on different continents, demonstrating their ubiquity and evolutionary persistence.

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July 7, 2019

Chromosome-level genome assembly and transcriptome of the green alga Chromochloris zofingiensis illuminates astaxanthin production.

Microalgae have potential to help meet energy and food demands without exacerbating environmental problems. There is interest in the unicellular green alga Chromochloris zofingiensis, because it produces lipids for biofuels and a highly valuable carotenoid nutraceutical, astaxanthin. To advance understanding of its biology and facilitate commercial development, we present a C. zofingiensis chromosome-level nuclear genome, organelle genomes, and transcriptome from diverse growth conditions. The assembly, derived from a combination of short- and long-read sequencing in conjunction with optical mapping, revealed a compact genome of ~58 Mbp distributed over 19 chromosomes containing 15,274 predicted protein-coding genes. The genome has uniform gene density over chromosomes, low repetitive sequence content (~6%), and a high fraction of protein-coding sequence (~39%) with relatively long coding exons and few coding introns. Functional annotation of gene models identified orthologous families for the majority (~73%) of genes. Synteny analysis uncovered localized but scrambled blocks of genes in putative orthologous relationships with other green algae. Two genes encoding beta-ketolase (BKT), the key enzyme synthesizing astaxanthin, were found in the genome, and both were up-regulated by high light. Isolation and molecular analysis of astaxanthin-deficient mutants showed that BKT1 is required for the production of astaxanthin. Moreover, the transcriptome under high light exposure revealed candidate genes that could be involved in critical yet missing steps of astaxanthin biosynthesis, including ABC transporters, cytochrome P450 enzymes, and an acyltransferase. The high-quality genome and transcriptome provide insight into the green algal lineage and carotenoid production.

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July 7, 2019

Critical points for an accurate human genome analysis.

Next-generation sequencing is radically changing how DNA diagnostic laboratories operate. What started as a single-gene profession is now developing into gene panel sequencing and whole-exome and whole-genome sequencing (WES/WGS) analyses. With further advances in sequencing technology and concomitant price reductions, WGS will soon become the standard and be routinely offered. Here, we focus on the critical steps involved in performing WGS, with a particular emphasis on points where WGS differs from WES, the important variables that should be taken into account, and the quality control measures that can be taken to monitor the process. The points discussed here, combined with recent publications on guidelines for reporting variants, will facilitate the routine implementation of WGS into a diagnostic setting.© 2017 Wiley Periodicals, Inc.

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July 7, 2019

Automated structural variant verification in human genomesw using single-molecule electronic DNA mapping.

The importance of structural variation in human disease and the difficulty of detecting structural variants larger than 50 base pairs has led to the development of several long-read sequencing technologies and optical mapping platforms. Frequently, multiple technologies and ad hoc methods are required to obtain a consensus regarding the location, size and nature of a structural variant, with no approach able to reliably bridge the gap of variant sizes between the domain of short-read approaches and the largest rearrangements observed with optical mapping. To address this unmet need, we have developed a new software package, SV-VerifyTM, which utilizes data collected with the Nabsys High Definition Mapping (HD-MappingTM) system, to perform hypothesis-based verification of putative deletions. We demonstrate that whole genome maps, constructed from electronic detection of tagged DNA, hundreds of kilobases in length, can be used effectively to facilitate calling of structural variants ranging in size from 300 base pairs to hundreds of kilobase pairs. SV-Verify implements hypothesis-based verification of putative structural variants using a set of support vector machines and is capable of concurrently testing several thousand independent hypotheses. We describe support vector machine training, utilizing a well-characterized human genome, and application of the resulting classifiers to another human genome, demonstrating high sensitivity and specificity for deletions >= 300 base pairs.

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July 7, 2019

A supervised statistical learning approach for accurate Legionella pneumophila source attribution during outbreaks.

Public health agencies are increasingly relying on genomics during Legionnaires’ disease investigations. However, the causative bacterium (Legionella pneumophila) has an unusual population structure, with extreme temporal and spatial genome sequence conservation. Furthermore, Legionnaires’ disease outbreaks can be caused by multiple L. pneumophila genotypes in a single source. These factors can confound cluster identification using standard phylogenomic methods. Here, we show that a statistical learning approach based on L. pneumophila core genome single nucleotide polymorphism (SNP) comparisons eliminates ambiguity for defining outbreak clusters and accurately predicts exposure sources for clinical cases. We illustrate the performance of our method by genome comparisons of 234 L. pneumophila isolates obtained from patients and cooling towers in Melbourne, Australia, between 1994 and 2014. This collection included one of the largest reported Legionnaires’ disease outbreaks, which involved 125 cases at an aquarium. Using only sequence data from L. pneumophila cooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires’ disease outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires’ disease within a UK hospital and observed a model predictive ability of 86%. We have developed a promising means to breach L. pneumophila genetic diversity extremes and provide objective source attribution data for outbreak investigations.IMPORTANCE Microbial outbreak investigations are moving to a paradigm where whole-genome sequencing and phylogenetic trees are used to support epidemiological investigations. It is critical that outbreak source predictions are accurate, particularly for pathogens, like Legionella pneumophila, which can spread widely and rapidly via cooling system aerosols, causing Legionnaires’ disease. Here, by studying hundreds of Legionella pneumophila genomes collected over 21 years around a major Australian city, we uncovered limitations with the phylogenetic approach that could lead to a misidentification of outbreak sources. We implement instead a statistical learning technique that eliminates the ambiguity of inferring disease transmission from phylogenies. Our approach takes geolocation information and core genome variation from environmental L. pneumophila isolates to build statistical models that predict with high confidence the environmental source of clinical L. pneumophila during disease outbreaks. We show the versatility of the technique by applying it to unrelated Legionnaires’ disease outbreaks in Australia and the UK. Copyright © 2017 American Society for Microbiology.

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July 7, 2019

Euglena gracilis genome and transcriptome: organelles, nuclear genome assembly strategies and initial features.

Euglena gracilis is a major component of the aquatic ecosystem and together with closely related species, is ubiquitous worldwide. Euglenoids are an important group of protists, possessing a secondarily acquired plastid and are relatives to the Kinetoplastidae, which themselves have global impact as disease agents. To understand the biology of E. gracilis, as well as to provide further insight into the evolution and origins of the Kinetoplastidae, we embarked on sequencing the nuclear genome; the plastid and mitochondrial genomes are already in the public domain. Earlier studies suggested an extensive nuclear DNA content, with likely a high degree of repetitive sequence, together with significant extrachromosomal elements. To produce a list of coding sequences we have combined transcriptome data from both published and new sources, as well as embarked on de novo sequencing using a combination of 454, Illumina paired end libraries and long PacBio reads. Preliminary analysis suggests a surprisingly large genome approaching 2 Gbp, with a highly fragmented architecture and extensive repeat composition. Over 80% of the RNAseq reads from E. gracilis maps to the assembled genome sequence, which is comparable with the well assembled genomes of T. brucei and T. cruzi. In order to achieve this level of assembly we employed multiple informatics pipelines, which are discussed here. Finally, as a preliminary view of the genome architecture, we discuss the tubulin and calmodulin genes, which highlight potential novel splicing mechanisms.

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July 7, 2019

A small secreted protein in Zymoseptoria tritici is responsible for avirulence on wheat cultivars carrying the Stb6 resistance gene.

Zymoseptoria tritici is the causal agent of Septoria tritici blotch, a major pathogen of wheat globally and the most damaging pathogen of wheat in Europe. A gene-for-gene (GFG) interaction between Z. tritici and wheat cultivars carrying the Stb6 resistance gene has been postulated for many years, but the genes have not been identified. We identified AvrStb6 by combining quantitative trait locus mapping in a cross between two Swiss strains with a genome-wide association study using a natural population of c. 100 strains from France. We functionally validated AvrStb6 using ectopic transformations. AvrStb6 encodes a small, cysteine-rich, secreted protein that produces an avirulence phenotype on wheat cultivars carrying the Stb6 resistance gene. We found 16 nonsynonymous single nucleotide polymorphisms among the tested strains, indicating that AvrStb6 is evolving very rapidly. AvrStb6 is located in a highly polymorphic subtelomeric region and is surrounded by transposable elements, which may facilitate its rapid evolution to overcome Stb6 resistance. AvrStb6 is the first avirulence gene to be functionally validated in Z. tritici, contributing to our understanding of avirulence in apoplastic pathogens and the mechanisms underlying GFG interactions between Z. tritici and wheat. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

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July 7, 2019

Resolving multicopy duplications de novo using polyploid phasing

While the rise of single-molecule sequencing systems has enabled an unprecedented rise in the ability to assemble complex regions of the genome, long segmental duplications in the genome still remain a challenging frontier in assembly. Segmental duplications are at the same time both gene rich and prone to large structural rearrangements, making the resolution of their sequences important in medical and evolutionary studies. Duplicated sequences that are collapsed in mammalian de novo assemblies are rarely identical; after a sequence is duplicated, it begins to acquire paralog-specific variants. In this paper, we study the problem of resolving the variations in multicopy, long segmental duplications by developing and utilizing algorithms for polyploid phasing. We develop two algorithms: the first one is targeted at maximizing the likelihood of observing the reads given the underlying haplotypes using discrete matrix completion. The second algorithm is based on correlation clustering and exploits an assumption, which is often satisfied in these duplications, that each paralog has a sizable number of paralog-specific variants. We develop a detailed simulation methodology and demonstrate the superior performance of the proposed algorithms on an array of simulated datasets. We measure the likelihood score as well as reconstruction accuracy, i.e., what fraction of the reads are clustered correctly. In both the performance metrics, we find that our algorithms dominate existing algorithms on more than 93% of the datasets. While the discrete matrix completion performs better on likelihood score, the correlation-clustering algorithm performs better on reconstruction accuracy due to the stronger regularization inherent in the algorithm. We also show that our correlation-clustering algorithm can reconstruct on average 7.0 haplotypes in 10-copy duplication datasets whereas existing algorithms reconstruct less than one copy on average.

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