Biology subjects

Keeble-Gagnere, G.

Publications and source records attributed to Keeble-Gagnere, G..

8 recordsLinked to original sources

bioRxiv2024

Adaptive diversification through structural variation in barley

Pangenomes are collections of annotated genome sequences of multiple individuals of a species. The structural variants uncovered by these datasets are a major asset to genetic analysis in crop plants. Here, we report a pangenome of barley comprising long-read sequence assemblies of 76 wild and domesticated genomes and short-read sequence data of 1,315 genotypes. An expanded catalogue of sequence variation in the crop includes structurally complex loci that have become hot spots of gene copy number variation in evolutionarily recent times. To demonstrate the utility of the pangenome, we focus on four loci involved in disease resistance, plant architecture, nutrient release, and trichome development. Novel allelic variation at a powdery mildew resistance locus and population-specific copy number gains in a regulator of vegetative branching were found. Expansion of a family of starch-cleaving enzymes in elite malting barleys was linked to shifts in enzymatic activity in micro-malting trials. Deletion of an enhancer motif is likely to change the developmental trajectory of the hairy appendages on barley grains. Our findings indicate that rapid evolution at structurally complex loci may have helped crop plants adapt to new selective regimes in agricultural ecosystems.

plant biology↗

bioRxiv2022

The giant diploid faba genome unlocks variation in a global protein crop

Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emission and loss of biodiversity. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value. Faba bean (Vicia faba L.) has a high yield potential and is well-suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has grown to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, though with significant copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association (GWA) analysis to dissect the genetic basis of hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate improvement of sustainable protein production across Mediterranean, subtropical, and northern temperate agro-ecological zones.

plant biology↗

bioRxiv2021

Novel design of imputation-enabled SNP arrays for breeding and research applications supporting multi-species hybridisation

Array-based SNP genotyping platforms have low genotype error and missing data rates compared to genotyping-by-sequencing technologies. However, design decisions used to create array-based SNP genotyping assays for both research and breeding applications are critical to their success. We describe a novel approach applicable to any animal or plant species for the design of cost-effective imputation-enabled SNP genotyping arrays with broad utility and demonstrate its application through the development of the Infinium Wheat Barley 40K SNP array. We show the approach delivers high-quality and high-resolution data for wheat and barley, including when samples are jointly hybridised. The new array aims to maximally capture haplotypic diversity in globally diverse wheat and barley germplasm while minimising ascertainment bias. Comprising mostly biallelic markers designed to be species-specific and single-copy, it permits highly accurate imputation in diverse germplasm to improve statistical power for GWAS and genomic selection. The SNP content captures tetraploid wheat (A- and B-genome) and Ae. tauschii (D-genome) diversity and delineates synthetic and tetraploid wheat from other wheats, as well as tetraploid species and subgroups. The content includes SNP tagging key trait loci in wheat and barley and that directly connect to other genotyping platforms and legacy datasets. The utility of the array is enhanced through the web-based tool Pretzel (https://plantinformatics.io/) which enables the arrays content to be visualised and interrogated interactively in the context of numerous genetic and genomic resources to more seamlessly connect research and breeding. The array is available for use by the international wheat and barley community. Short summaryDesigning SNP genotyping arrays for closely related species with broad applicability in both research and breeding is challenging. Here we describe a novel generic approach to select SNP content for such arrays and demonstrate its utility in wheat and barley to: O_LIcapture haplotypic diversity while minimising ascertainment bias; C_LIO_LIaccurately impute to high SNP density in diverse germplasm; C_LIO_LIgenerate high-quality high-resolution genotypic data; and C_LIO_LIjointly hybridise samples to the same bead chip array. C_LI

genetics↗

bioRxiv2020

Compact and evenly distributed k-mer binning for genomic sequences

The processing of k-mers (subsequences of length k) is at the foundation of many sequence processing algorithms in bioinformatics, including k-mer counting for genome size estimation, genome assembly, and taxonomic classification for metagenomics. Minimizers - ordered m-mers where m < k - are often used to group k-mers into bins as a first step in such processing. However, minimizers are known to generate bins of very different sizes, which can pose challenges for distributed and parallel processing, as well as generally increase memory requirements. Furthermore, although various minimizer orderings have been proposed, their practical value for improving tool efficiency has not yet been fully explored. Here we present Discount, a distributed k-mer counting tool based on Apache Spark, which we use to investigate the behaviour of various minimizer orderings in practice when applied to metagenomics data. Using this tool, we then introduce the universal frequency ordering, a new combination of frequency counted minimizers and universal k-mer hitting sets, which yields both evenly distributed binning and small bin sizes. We show that this ordering allows Discount to perform distributed k-mer counting on a large dataset in as little as 1/8 of the memory of comparable approaches, making it the most efficient out-of-core distributed k-mer counting method available.

bioinformatics↗

bioRxiv2020

1RS.1BL molecular resolution provides novel contributions to wheat improvement

Wheat-rye 1RS.1BL translocation has a significant impact on wheat yield and hence food production globally. However, the genomic basis of its contributions to wheat improvement is undetermined. Here, we generated a high-quality assembly of 1RS.1BL translocation comprising 748,715,293 bp with 4,996 predicted protein-coding genes. We found the size of 1RS is larger than 1BS with the active centromere domains shifted to the 1RS side instead of the 1BL side in Aikang58 (AK58). The gene alignment showed excellent synteny with 1BS from wheat and genes from 1RS were expressed well in wheat especially for 1RS where expression was higher than that of 1BS for the grain-20DPA stage associated with greater grain weight and negative flour quality attributes. A formin-like-domain protein FH14 (TraesAK58CH1B01G010700) was important in regulating cell division. Two PPR genes were most likely the genes for the multi fertility restoration locus Rf multi. Our data not only provide the high-resolution structure and gene complement for the 1RS.1BL translocation, but also defined targets for enhancing grain yield, biotic and abiotic stress, and fertility restoration in wheat.

genomics↗

bioRxiv2019

Integrating past, present and future wheat research with Pretzel

MotivationMajor advances have been made in the assembly of complex genomes such as wheat. Remaining challenges include linking these new resources to legacy research, as well as lowering the bar to entry for members of the research community who may lack specific bioinformatics skills.\n\nResultsPretzel aims to solve these problems by providing an interactive, online environment for data visualisation and analysis which, when loaded with appropriately curated data, can enable researchers with no bioinformatics training to exploit the latest genomic resources. We demonstrate that Pretzel can be used to answer common questions asked by researchers as well as for advanced curation of pseudomolecule structure.\n\nAvailabilityPretzel is implemented in JavaScript and is freely available under a GPLv3 license online at: https://github.com/plantinformatics/pretzel.\n\nContactgabriel.keeble-gagnere@ecodev.vic.gov.au

bioinformatics↗

bioRxiv2018

Optical and physical mapping with local finishing enables megabase-scale resolution of agronomically important regions in the wheat genome

BackgroundNumerous scaffold-level sequences for wheat are now being released and, in this context, we report on a strategy for improving the overall assembly to a level comparable to that of the human genome.\n\nResultsUsing chromosome 7A of wheat as a model, sequence-finished megabase scale sections of this chromosome were established by combining a new independent assembly based on a BAC-based physical map, BAC pool paired end sequencing, chromosome arm specific mate-pair sequencing and Bionano optical mapping with the IWGSC RefSeq v1.0 sequence and its underlying raw data. The combined assembly results in 18 super-scaffolds across the chromosome. The value of finished genome regions is demonstrated for two approximately 2.5 Mb regions associated with yield and the grain quality phenotype of fructan carbohydrate grain levels. In addition, the 50 Mb centromere region analysis incorporates cytological data highlighting the importance of non-sequence data in the assembly of this complex genome region.\n\nConclusionsSufficient genome sequence information is shown to be now available for the wheat community to produce sequence-finished releases of each chromosome of the reference genome. The high-level completion identified that an array of seven fructosyl transferase genes underpins grain quality and yield attributes are affected by five f-box-only-protein-ubiquitin ligase domain and four root-specific lipid transfer domain genes. The completed sequence also includes the centromere.

genomics↗

bioRxiv2017

Best Practice Data Life Cycle Approaches for the Life Sciences

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a life cycle view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain.\n\nHere we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on omics datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

bioinformatics↗