A systems genomics and genetics approach to identify the genetic regulatory network for lignin content in Brassica napus seeds.

Seed quality traits of oilseed rape, Brassica napus (B. napus), exhibit quantitative inheritance determined by its genetic makeup and the environment via the mediation of a complex genetic architecture of hundreds to thousands of genes. Thus, instead of single gene analysis, network-based systems genomics and genetics approaches that combine genotype, phenotype, and molecular phenotypes offer a promising alternative to uncover this complex genetic architecture. In the current study, systems genetics approaches were used to explore the genetic regulation of lignin traits in B. napus seeds. Four QTL (qLignin_A09_1, qLignin_A09_2, qLignin_A09_3, and qLignin_C08) distributed on two chromosomes were identified for lignin content. The qLignin_A09_2 and qLignin_C08 loci were homologous QTL from the A and C subgenomes, respectively. Genome-wide gene regulatory network analysis identified eighty-three subnetworks (or modules); and three modules with 910 genes in total, were associated with lignin content, which was confirmed by network QTL analysis. eQTL (expression quantitative trait loci) analysis revealed four cis-eQTL genes including lignin and flavonoid pathway genes, cinnamoyl-CoA-reductase (CCR1), and TRANSPARENT TESTA genes TT4, TT6, TT8, as causal genes. The findings validated the power of systems genetics to identify causal regulatory networks and genes underlying complex traits. Moreover, this information may enable the research community to explore new breeding strategies, such as network selection or gene engineering, to rewire networks to develop climate resilience crops with better seed quality.

Mapping QTL for vernalization requirement identified adaptive divergence of the candidate gene Flowering Locus C in polyploid Camelina sativa.

Vernalization requirement is an integral component of flowering in winter-type plants. The availability of winter ecotypes among Camelina species facilitated the mapping of quantitative trait loci (QTL) for vernalization requirement in Camelina sativa. An inter and intraspecific crossing scheme between related Camelina species, where one spring and two different sources of winter-type habit were used, resulted in the development of two segregating populations. Linkage maps generated with sequence-based markers identified three QTLs associated with vernalization requirement in C. sativa; two from the interspecific (chromosomes 13 and 20) and one from the intraspecific cross (chromosome 8). Notably, the three loci were mapped to different homologous regions of the hexaploid C. sativa genome. All three QTLs were found in proximity to Flowering Locus C (FLC), variants of which have been reported to affect the vernalization requirement in plants. Temporal transcriptome analysis for winter-type Camelina alyssum demonstrated reduction in expression of FLC on chromosomes 13 and 20 during cold treatment, which would trigger flowering, since FLC would be expected to suppress floral initiation. FLC on chromosome 8 also showed reduced expression in the C. sativa ssp. pilosa winter parent upon cold treatment, but was expressed at very high levels across all time points in the spring-type C. sativa. The chromosome 8 copy carried a deletion in the spring-type line, which could impact its functionality. Contrary to previous reports, all three FLC loci can contribute to controlling the vernalization response in C. sativa and provide opportunities for manipulating this requirement in the crop.

Genomic asymmetry of the Brassica napus seed: epigenetic contributions of DNA methylation and small RNAs to subgenome bias.

Polyploidy is a persistent phenomenon in angiosperm genome evolution that is hypothesized to have contributed to the diversity of extant flowering plants. Brassica napus, one of the world's most important angiosperm oilseed species, originated from the interspecific hybridization of Brassica rapa (An ) and Brassica oleracea (Cn ). While the trends of genome dominance in transcriptomics are beginning to emerge, less is known about the epigenetic and small RNA landscapes in polyploids during reproductive development. The seed is the pivotal developmental transition into the new sporophytic generation, and experiences substantial epigenetic modifications over time. Here, we investigated the prevalence of bias in the contexts of DNA methylation and small interfering (si)RNA profiles in both subgenomes (An and Cn ), as well as the ancestral fractionated genomes across B. napus seed development. We report ubiquitous Cn subgenome bias of siRNA expression and cytosine methylation, with DNA methylation being particularly abundant on gene promoters in the Cn subgenome. Further, we provide evidence that siRNA transcriptional patterns were conserved within the ancestral triplicated subgenomes of B. napus, but not across the An and Cn subgenomes. We discuss how methylation patterns in the B. napus seed relate to genes, promoter regions, siRNA loci and transposable elements through the lens of genome fractionation and polyploidization. Taken together we provide evidence for epigenetic regulation selectively silencing the Cn subgenome during seed development, and explore the impact of genome fractionation on the epigenetic components of the B. napus seed.

Sequencing of Camelina neglecta, a diploid progenitor of the hexaploid oilseed Camelina sativa.

Camelina neglecta is a diploid species from the genus Camelina, which includes the versatile oilseed Camelina sativa. These species are closely related to Arabidopsis thaliana and the economically important Brassica crop species, making this genus a useful platform to dissect traits of agronomic importance while providing a tool to study the evolution of polyploids. A highly contiguous chromosome-level genome sequence of C. neglecta with an N50 size of 29.1 Mb was generated utilizing Pacific Biosciences (PacBio, Menlo Park, CA) long-read sequencing followed by chromosome conformation phasing. Comparison of the genome with that of C. sativa shows remarkable coincidence with subgenome 1 of the hexaploid, with only one major chromosomal rearrangement separating the two. Synonymous substitution rate analysis of the predicted 34 061 genes suggested subgenome 1 of C. sativa directly descended from C. neglecta around 1.2 mya. Higher functional divergence of genes in the hexaploid as evidenced by the greater number of unique orthogroups, and differential composition of resistant gene analogs, might suggest an immediate adaptation strategy after genome merger. The absence of genome bias in gene fractionation among the subgenomes of C. sativa in comparison with C. neglecta, and the complete lack of fractionation of meiosis-specific genes attests to the neopolyploid status of C. sativa. The assembled genome will provide a tool to further study genome evolution processes in the Camelina genus and potentially allow for the identification and exploitation of novel variation for Camelina crop improvement.

Genetic variation and structural diversity in major seed proteins among and within Camelina species.

MAIN CONCLUSION: Genetic variation in seed protein composition, seed protein gene expression and predictions of seed protein physiochemical properties were documented in C. sativa and other Camelina species. Seed protein diversity was examined in six Camelina species (C. hispida, C. laxa, C. microcarpa, C. neglecta, C. rumelica and C. sativa). Differences were observed in seed protein electrophoretic profiles, total seed protein content and amino acid composition between the species. Genes encoding major seed proteins (cruciferins, napins, oleosins and vicilins) were catalogued for C. sativa and RNA-Seq analysis established the expression patterns of these and other genes in developing seed from anthesis through to maturation. Examination of 187 C. sativa accessions revealed limited variation in seed protein electrophoretic profiles, though sufficient to group the majority into classes based on high MW protein profiles corresponding to the cruciferin region. C. sativa possessed four distinct types of cruciferins, named CsCRA, CsCRB, CsCRC and CsCRD, which corresponded to orthologues in Arabidopsis thaliana with members of each type encoded by homeologous genes on the three C. sativa sub-genomes. Total protein content and amino acid composition varied only slightly; however, RNA-Seq analysis revealed that CsCRA and CsCRB genes contributed > 95% of the cruciferin transcripts in most lines, whereas CsCRC genes were the most highly expressed cruciferin genes in others, including the type cultivar DH55. This was confirmed by proteomics analyses. Cruciferin is the most abundant seed protein and contributes the most to functionality. Modelling of the C. sativa cruciferins indicated that each type possesses different physiochemical attributes that were predicted to impart unique functional properties. As such, opportunities exist to create C. sativa cultivars with seed protein profiles tailored to specific technical applications.

Unweaving the population structure and genetic diversity of Canadian shrub willow.

Perennial shrub willow are increasingly being promoted in short-rotation coppice systems as biomass feedstocks, for phytoremediation applications, and for the diverse ecosystem services that can accrue. This renewed interest has led to widespread willow cultivation, particularly of non-native varieties. However, Canadian willow species have not been widely adopted and their inherent diversity has not yet been thoroughly investigated. In this study, 324 genotypes of Salix famelica and Salix eriocephala collected from 33 sites of origin were analyzed using 26,016 single nucleotide polymorphisms to reveal patterns of population structure and genetic diversity. Analyses by Bayesian methods and principal component analysis detected five main clusters that appeared to be largely shaped by geoclimatic variables including mean annual precipitation and the number of frost-free days. The overall observed (HO) and expected (HE) heterozygosity were 0.126 and 0.179, respectively. An analysis of molecular variance revealed that the highest genetic variation occurred within genotypes (69%), while 8% of the variation existed among clusters and 23% between genotypes within clusters. These findings provide new insights into the extent of genetic variation that exists within native shrub willow species which could be leveraged in pan-Canadian willow breeding programs.

Spatiotemporal Transcriptomic Atlas of Developing Embryos and Vegetative Tissues in Flax.

Flax (Linum usitatissimum L.) is an important multipurpose crop widely grown for oil and fiber. Despite recent advances in genomics, detailed gene activities during the important reproductive phase of its development are not well defined. In this study, we employed high-throughput RNA-sequencing methods to generate in-depth transcriptome profiles of flax tissues with emphasis on the reproductive phases of five key stages of embryogenesis (globular embryo, heart embryo, torpedo embryo, cotyledon embryo, and mature embryo), mature seed, and vegetative tissues viz. ovary, anther, and root. These datasets were used to establish the co-expression networks covering 36 gene modules based on the expression patterns for each gene through weighted gene co-expression network analysis (WGCNA). Functional interrogation with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) of dominantly expressed genetic modules in tissues revealed pathways involved in the development of different tissues. Moreover, the essential genes in embryo development and synthesis of storage reserves were identified based on their dynamic expression patterns. Together, this comprehensive dataset for developing embryos, mature seeds and vegetative tissues provides new insights into molecular mechanisms of seed development with potential for flax crop improvement.

Visualization Tools for Genomic Conservation.

SynVisio and Accusyn ( genomevis.usask.ca ) are freely available web-based tools for visualizing genomic conservation that provide easy-to-access visualizations for researchers to interact with their datasets and change parameters in real time to carry out synteny exploration and analysis through multiple coordinated visual representations. The tools use standard file formats and outputs from existing synteny detection systems such as MCScanX or DAGChainer, and provide several features that are valuable for large-scale genomic analysis: a range of visualization scales from full genomes down to single collinearity blocks; single-level and multiple-level plots that enable the analysis of more than two genomic regions; annotation tracks that can be loaded using standard BedGraph files; several techniques for reducing visual clutter in visualizations; the ability to download high-quality images of the visualizations; and a snapshot panel for storing configurations of the interface for later revisitation.

Gene expression profiling reveals transcription factor networks and subgenome bias during Brassica napus seed development.

We profiled the global gene expression landscape across the reproductive lifecycle of Brassica napus. Comparative analysis of this nascent amphidiploid revealed the contribution of each subgenome to plant reproduction. Whole-genome transcription factor networks identified BZIP11 as a transcriptional regulator of early B. napus seed development. Knockdown of BZIP11 using RNA interference resulted in a similar reduction in gene activity of predicted gene targets, and a reproductive-lethal phenotype. Global mRNA profiling revealed lower accumulation of Cn subgenome transcripts relative to the An subgenome. Subgenome-specific transcription factor networks identified distinct transcription factor families enriched in each of the An and Cn subgenomes early in seed development. Analysis of laser-microdissected seed subregions further reveal subgenome expression dynamics in the embryo, endosperm and seed coat of early stage seeds. Transcription factors predicted to be regulators encoded by the An subgenome are expressed primarily in the seed coat, whereas regulators encoded by the Cn subgenome were expressed primarily in the embryo. Data suggest subgenome bias are characteristic features of the B. napus seed throughout development, and that such bias might not be universal across the embryo, endosperm and seed coat of the developing seed. Transcriptional networks spanning both the An and Cn genomes of the whole B. napus seed can identify valuable targets for seed development research and that -omics level approaches to studying gene regulation in B. napus can benefit from both broad and high-resolution analyses.

Deep neural networks for genomic prediction do not estimate marker effects.

Genomic prediction is a promising technology for advancing both plant and animal breeding, with many different prediction models evaluated in the literature. It has been suggested that the ability of powerful nonlinear models, such as deep neural networks, to capture complex epistatic effects between markers offers advantages for genomic prediction. However, these methods tend not to outperform classical linear methods, leaving it an open question why this capacity to model nonlinear effects does not seem to result in better predictive capability. In this work, we propose the theory that, because of a previously described principle called shortcut learning, deep neural networks tend to base their predictions on overall genetic relatedness rather than on the effects of particular markers such as epistatic effects. Using several datasets of crop plants [lentil (Lens culinaris Medik.), wheat (Triticum aestivum L.), and Brassica carinata A. Braun], we demonstrate the network's indifference to the values of the markers by showing that the same network, provided with only the locations of matches between markers for two individuals, is able to perform prediction to the same level of accuracy.

Phenotyping Flowering in Canola (Brassica napus L.) and Estimating Seed Yield Using an Unmanned Aerial Vehicle-Based Imagery.

Phenotyping crop performance is critical for line selection and variety development in plant breeding. Canola (Brassica napus L.) flowers, the bright yellow flowers, indeterminately increase over a protracted period. Flower production of canola plays an important role in yield determination. Yellowness of canola petals may be a critical reflectance signal and a good predictor of pod number and, therefore, seed yield. However, quantifying flowering based on traditional visual scales is subjective, time-consuming, and labor-consuming. Recent developments in phenotyping technologies using Unmanned Aerial Vehicles (UAVs) make it possible to effectively capture crop information and to predict crop yield via imagery. Our objectives were to investigate the application of vegetation indices in estimating canola flower numbers and to develop a descriptive model of canola seed yield. Fifty-six diverse Brassica genotypes, including 53 B. napus lines, two Brassica carinata lines, and a Brassica juncea variety, were grown near Saskatoon, SK, Canada from 2016 to 2018 and near Melfort and Scott, SK, Canada in 2017. Aerial imagery with geometric and radiometric corrections was collected through the flowering stage using a UAV mounted with a multispectral camera. We found that the normalized difference yellowness index (NDYI) was a useful vegetation index for representing canola yellowness, which is related to canola flowering intensity during the full flowering stage. However, the flowering pixel number estimated by the thresholding method improved the ability of NDYI to detect yellow flowers with coefficient of determination (R 2) ranging from 0.54 to 0.95. Moreover, compared with using a single image date, the NDYI-based flowering pixel numbers integrated over time covers more growth information and can be a good predictor of pod number and thus, canola yield with R 2 up to 0.42. These results indicate that NDYI-based flowering pixel numbers can perform well in estimating flowering intensity. Integrated flowering intensity extracted from imagery over time can be a potential phenotype associated with canola seed yield.

Genome structural evolution in Brassica crops.

The cultivated Brassica species include numerous vegetable and oil crops of global importance. Three genomes (designated A, B and C) share mesohexapolyploid ancestry and occur both singly and in each pairwise combination to define the Brassica species. With organizational errors (such as misplaced genome segments) corrected, we showed that the fundamental structure of each of the genomes is the same, irrespective of the species in which it occurs. This enabled us to clarify genome evolutionary pathways, including updating the Ancestral Crucifer Karyotype (ACK) block organization and providing support for the Brassica mesohexaploidy having occurred via a two-step process. We then constructed genus-wide pan-genomes, drawing from genes present in any species in which the respective genome occurs, which enabled us to provide a global gene nomenclature system for the cultivated Brassica species and develop a methodology to cost-effectively elucidate the genomic impacts of alien introgressions. Our advances not only underpin knowledge-based approaches to the more efficient breeding of Brassica crops but also provide an exemplar for the study of other polyploids.

Long-read sequencing reveals widespread intragenic structural variants in a recent allopolyploid crop plant.

Genome structural variation (SV) contributes strongly to trait variation in eukaryotic species and may have an even higher functional significance than single-nucleotide polymorphism (SNP). In recent years, there have been a number of studies associating large chromosomal scale SV ranging from hundreds of kilobases all the way up to a few megabases to key agronomic traits in plant genomes. However, there have been little or no efforts towards cataloguing small- (30-10 000 bp) to mid-scale (10 000-30 000 bp) SV and their impact on evolution and adaptation-related traits in plants. This might be attributed to complex and highly duplicated nature of plant genomes, which makes them difficult to assess using high-throughput genome screening methods. Here, we describe how long-read sequencing technologies can overcome this problem, revealing a surprisingly high level of widespread, small- to mid-scale SV in a major allopolyploid crop species, Brassica napus. We found that up to 10% of all genes were affected by small- to mid-scale SV events. Nearly half of these SV events ranged between 100 bp and 1000 bp, which makes them challenging to detect using short-read Illumina sequencing. Examples demonstrating the contribution of such SV towards eco-geographical adaptation and disease resistance in oilseed rape suggest that revisiting complex plant genomes using medium-coverage long-read sequencing might reveal unexpected levels of functional gene variation, with major implications for trait regulation and crop improvement.

Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population.

Phenotyping is considered a significant bottleneck impeding fast and efficient crop improvement. Similar to many crops, Brassica napus, an internationally important oilseed crop, suffers from low genetic diversity, and will require exploitation of diverse genetic resources to develop locally adapted, high yielding and stress resistant cultivars. A pilot study was completed to assess the feasibility of using indoor high-throughput phenotyping (HTP), semi-automated image processing, and machine learning to capture the phenotypic diversity of agronomically important traits in a diverse B. napus breeding population, SKBnNAM, introduced here for the first time. The experiment comprised 50 spring-type B. napus lines, grown and phenotyped in six replicates under two treatment conditions (control and drought) over 38 days in a LemnaTec Scanalyzer 3D facility. Growth traits including plant height, width, projected leaf area, and estimated biovolume were extracted and derived through processing of RGB and NIR images. Anthesis was automatically and accurately scored (97% accuracy) and the number of flowers per plant and day was approximated alongside relevant canopy traits (width, angle). Further, supervised machine learning was used to predict the total number of raceme branches from flower attributes with 91% accuracy (linear regression and Huber regression algorithms) and to identify mild drought stress, a complex trait which typically has to be empirically scored (0.85 area under the receiver operating characteristic curve, random forest classifier algorithm). The study demonstrates the potential of HTP, image processing and computer vision for effective characterization of agronomic trait diversity in B. napus, although limitations of the platform did create significant variation that limited the utility of the data. However, the results underscore the value of machine learning for phenotyping studies, particularly for complex traits such as drought stress resistance.

Characterization and Mapping of retr04, retr05 and retr06 Broad-Spectrum Resistances to Turnip Mosaic Virus in Brassica juncea, and the Development of Robust Methods for Utilizing Recalcitrant Genotyping Data.

Turnip mosaic virus (TuMV) induces disease in susceptible hosts, notably impacting cultivation of important crop species of the Brassica genus. Few effective plant viral disease management strategies exist with the majority of current approaches aiming to mitigate the virus indirectly through control of aphid vector species. Multiple sources of genetic resistance to TuMV have been identified previously, although the majority are strain-specific and have not been exploited commercially. Here, two Brassica juncea lines (TWBJ14 and TWBJ20) with resistance against important TuMV isolates (UK 1, vVIR24, CDN 1, and GBR 6) representing the most prevalent pathotypes of TuMV (1, 3, 4, and 4, respectively) and known to overcome other sources of resistance, have been identified and characterized. Genetic inheritance of both resistances was determined to be based on a recessive two-gene model. Using both single nucleotide polymorphism (SNP) array and genotyping by sequencing (GBS) methods, quantitative trait loci (QTL) analyses were performed using first backcross (BC1) genetic mapping populations segregating for TuMV resistance. Pairs of statistically significant TuMV resistance-associated QTLs with additive interactive effects were identified on chromosomes A03 and A06 for both TWBJ14 and TWBJ20 material. Complementation testing between these B. juncea lines indicated that one resistance-linked locus was shared. Following established resistance gene nomenclature for recessive TuMV resistance genes, these new resistance-associated loci have been termed retr04 (chromosome A06, TWBJ14, and TWBJ20), retr05 (A03, TWBJ14), and retr06 (A03, TWBJ20). Genotyping by sequencing data investigated in parallel to robust SNP array data was highly suboptimal, with informative data not established for key BC1 parental samples. This necessitated careful consideration and the development of new methods for processing compromised data. Using reductive screening of potential markers according to allelic variation and the recombination observed across BC1 samples genotyped, compromised GBS data was rendered functional with near-equivalent QTL outputs to the SNP array data. The reductive screening strategy employed here offers an alternative to methods relying upon imputation or artificial correction of genotypic data and may prove effective for similar biparental QTL mapping studies.

A major quantitative trait locus on chromosome A9, BnaPh1, controls homoeologous recombination in Brassica napus.

Ensuring faithful homologous recombination in allopolyploids is essential to maintain optimal fertility of the species. Variation in the ability to control aberrant pairing between homoeologous chromosomes in Brassica napus has been identified. The current study exploited the extremes of such variation to identify genetic factors that differentiate newly resynthesised B. napus, which is inherently unstable, and established B. napus, which has adapted to largely control homoeologous recombination. A segregating B. napus mapping population was analysed utilising both cytogenetic observations and high-throughput genotyping to quantify the levels of homoeologous recombination. Three quantitative trait loci (QTL) were identified that contributed to the control of homoeologous recombination in the important oilseed crop B. napus. One major QTL on BnaA9 contributed between 32 and 58% of the observed variation. This study is the first to assess homoeologous recombination and map associated QTLs resulting from deviations in normal pairing in allotetraploid B. napus. The identified QTL regions suggest candidate meiotic genes that could be manipulated in order to control this important trait and further allow the development of molecular markers to utilise this trait to exploit homoeologous recombination in a crop.

Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies.

Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C4 grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for a nested association mapping population of canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.

Characterization of B-Genome Specific High Copy hAT MITE Families in Brassica nigra Genome.

Miniature inverted-repeat transposable elements (MITEs) are non-autonomous class II transposons which have been shown to influence genome evolution. Brassica nigra L. (B-genome) is one of three Brassica diploids cultivated primarily as an oil crop, which harbors novel alleles important for breeding. Two new high copy hAT MITE families (BniHAT-1 and BniHAT-2) from the B-genome were characterized and their prevalence assessed in the genomes of the related diploids, rapa L. (A) and Brassica oleracea L. (C). Both novel MITE families were present at high copy numbers in the B-genome with 434 and 331 copies of BniHAT-1 and BniHAT-2, respectively. Yet less than 20 elements were identified in the genome assemblies of the A, and C -genomes, supporting B-genome specific proliferation of these MITE families. Although apparently randomly distributed across the genome, 68 and 70% of the B-genome MITEs were present within 2 kb flanking regions of annotated genes suggesting they might influence gene expression and/or function. In addition, MITE derived microRNAs and transcription factor binding sites suggested a putative role in gene regulation. Age of insertion analysis revealed that the major proliferation of these elements occurred during 2-3 million years ago. Additionally, site-specific polymorphism analyses showed that 44% MITEs were undergoing active amplification into the B-genome. Overall, this study provides a comprehensive analysis of two high copy MITE families, which were specifically amplified in the B-genome, suggesting a potential role in shaping the Brassica B-genome.

A high-contiguity Brassica nigra genome localizes active centromeres and defines the ancestral Brassica genome.

It is only recently, with the advent of long-read sequencing technologies, that we are beginning to uncover previously uncharted regions of complex and inherently recursive plant genomes. To comprehensively study and exploit the genome of the neglected oilseed Brassica nigra, we generated two high-quality nanopore de novo genome assemblies. The N50 contig lengths for the two assemblies were 17.1 Mb (12 contigs), one of the best among 324 sequenced plant genomes, and 0.29 Mb (424 contigs), respectively, reflecting recent improvements in the technology. Comparison with a de novo short-read assembly corroborated genome integrity and quantified sequence-related error rates (0.2%). The contiguity and coverage allowed unprecedented access to low-complexity regions of the genome. Pericentromeric regions and coincidence of hypomethylation enabled localization of active centromeres and identified centromere-associated ALE family retro-elements that appear to have proliferated through relatively recent nested transposition events (<1 Ma). Genomic distances calculated based on synteny relationships were used to define a post-triplication Brassica-specific ancestral genome, and to calculate the extensive rearrangements that define the evolutionary distance separating B. nigra from its diploid relatives.