9 research outputs found

    High-density marker profiling confirms ancestral genomes of Avena species and identifies D-genome chromosomes of hexaploid oat

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    We investigated genomic relationships among 27 species of the genus Avena using high-density genetic markers revealed by genotyping-by-sequencing (GBS). Two methods of GBS analysis were used: one based on tag-level haplotypes that were previously mapped in cultivated hexaploid oat (A. sativa), and one intended to sample and enumerate tag-level haplotypes originating from all species under investigation. Qualitatively, both methods gave similar predictions regarding the clustering of species and shared ancestral genomes. Furthermore, results were consistent with previous phylogenies of the genus obtained with conventional approaches, supporting the robustness of whole genome GBS analysis. Evidence is presented to justify the final and definitive classification of the tetraploids A. insularis, A. maroccana (=A. magna), and A. murphyi as containing D-plus-C genomes, and not A-plus-C genomes, as is most often specified in past literature. Through electronic painting of the 21 chromosome representations in the hexaploid oat consensus map, we show how the relative frequency of matches between mapped hexaploid-derived haplotypes and AC (DC)-genome tetraploids vs. A- and C-genome diploids can accurately reveal the genome origin of all hexaploid chromosomes, including the approximate positions of inter-genome translocations. Evidence is provided that supports the continued classification of a diverged B genome in AB tetraploids, and it is confirmed that no extant A-genome diploids, including A. canariensis, are similar enough to the D genome of tetraploid and hexaploid oat to warrant consideration as a D-genome diploid.publishersversionPeer reviewe

    Quantum critical dynamics in a 5000-qubit programmable spin glass

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    Experiments on disordered alloys suggest that spin glasses can be brought into low-energy states faster by annealing quantum fluctuations than by conventional thermal annealing. Due to the importance of spin glasses as a paradigmatic computational testbed, reproducing this phenomenon in a programmable system has remained a central challenge in quantum optimization. Here we achieve this goal by realizing quantum critical spin-glass dynamics on thousands of qubits with a superconducting quantum annealer. We first demonstrate quantitative agreement between quantum annealing and time-evolution of the Schr\"odinger equation in small spin glasses. We then measure dynamics in 3D spin glasses on thousands of qubits, where simulation of many-body quantum dynamics is intractable. We extract critical exponents that clearly distinguish quantum annealing from the slower stochastic dynamics of analogous Monte Carlo algorithms, providing both theoretical and experimental support for a scaling advantage in reducing energy as a function of annealing time

    Quantum error mitigation in quantum annealing

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    Quantum Error Mitigation (QEM) presents a promising near-term approach to reduce error when estimating expectation values in quantum computing. Here, we introduce QEM techniques tailored for quantum annealing, using Zero-Noise Extrapolation (ZNE). We implement ZNE through zero-temperature extrapolation as well as energy-time rescaling. We conduct experimental investigations into the quantum critical dynamics of a transverse-field Ising spin chain, demonstrating the successful mitigation of thermal noise through both of these techniques. Moreover, we show that energy-time rescaling effectively mitigates control errors in the coherent regime where the effect of thermal noise is minimal. Our ZNE results agree with exact calculations of the coherent evolution over a range of annealing times that exceeds the coherent annealing range by almost an order of magnitude.Comment: 10 pages, 5 figure

    Model SNP development for complex genomes based on hexaploid oat using high-throughput 454 sequencing technology

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    <p>Abstract</p> <p>Background</p> <p>Genetic markers are pivotal to modern genomics research; however, discovery and genotyping of molecular markers in oat has been hindered by the size and complexity of the genome, and by a scarcity of sequence data. The purpose of this study was to generate oat expressed sequence tag (EST) information, develop a bioinformatics pipeline for SNP discovery, and establish a method for rapid, cost-effective, and straightforward genotyping of SNP markers in complex polyploid genomes such as oat.</p> <p>Results</p> <p>Based on cDNA libraries of four cultivated oat genotypes, approximately 127,000 contigs were assembled from approximately one million Roche 454 sequence reads. Contigs were filtered through a novel bioinformatics pipeline to eliminate ambiguous polymorphism caused by subgenome homology, and 96 <it>in silico </it>SNPs were selected from 9,448 candidate loci for validation using high-resolution melting (HRM) analysis. Of these, 52 (54%) were polymorphic between parents of the Ogle1040 × TAM O-301 (OT) mapping population, with 48 segregating as single Mendelian loci, and 44 being placed on the existing OT linkage map. Ogle and TAM amplicons from 12 primers were sequenced for SNP validation, revealing complex polymorphism in seven amplicons but general sequence conservation within SNP loci. Whole-amplicon interrogation with HRM revealed insertions, deletions, and heterozygotes in secondary oat germplasm pools, generating multiple alleles at some primer targets. To validate marker utility, 36 SNP assays were used to evaluate the genetic diversity of 34 diverse oat genotypes. Dendrogram clusters corresponded generally to known genome composition and genetic ancestry.</p> <p>Conclusions</p> <p>The high-throughput SNP discovery pipeline presented here is a rapid and effective method for identification of polymorphic SNP alleles in the oat genome. The current-generation HRM system is a simple and highly-informative platform for SNP genotyping. These techniques provide a model for SNP discovery and genotyping in other species with complex and poorly-characterized genomes.</p
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