Genomic Databases for Crop Improvement

Progress in single-access information systems for wheat and rice crop improvement

Improving productivity of the staple crops wheat and rice is essential to feed the growing global population, particularly in the context of a changing climate. However, current rates of yield gain are insufficient to support the predicted population growth. New approaches are required to accelerate the breeding process, and many of these are driven by the application of large-scale crop data. To leverage the substantial volumes and types of data that can be applied for precision breeding, the wheat and rice research communities are working towards the development of integrated systems to access and standardize the dispersed, heterogeneous available data. Here, we outline the initiatives of the International Wheat Information System (WheatIS) and the International Rice Informatics Consortium (IRIC) to establish Web-based single-access systems and data mining tools to make the available resources more accessible, drive discovery and accelerate the production of new crop varieties. We discuss the progress of WheatIS and IRIC towards unifying specialized wheat and rice databases and building custom software platforms to manage and interrogate these data. Single-access crop information systems will strengthen scientific collaboration, optimize the use of public research funds and help achieve the required yield gains in the two most important global food crops.

Transcriptome analysis of the effect of GA3 in sugarcane culm

Our earlier studies have indicated that GA₃, being a growth hormone, increases internodal length, in turn increasing sink strength and improving sucrose accumulation in sugarcane. In this study, transcriptomic level analysis was carried out on internodal samples of a high sugar accumulating variety (CoLk 94184) of sugarcane, to determine the effect of exogenous application of GA₃ vis a vis functional analysis of differentially expressing transcripts. Overall, a total of 201,184 transcripts were identified, with median contig length of 450 bp and N50 length of 1029 bp. Analyzing the data from control and GA₃-treated canes, at 0.01 significance, a total of 1516 differentially expressing transcripts were identified in bottom internodes and 1589 in top internodes. A KEGG (enrichment) analysis grouped the transcripts into 153 plant-related functional categories. From among these, the transcripts which were functionally relevant to sugar metabolism and photosynthesis were sieved out. Starch and sucrose metabolizing genes showed maximum fold change of 5.0 and 3.0 among top and bottom internodal samples. A homology match using Blastx analysis tool yielded 65 transcripts/differentially expressed genes (DEGs) which were found to share homology with C₄ plants like Saccharum, Sorghum and Zea mays. Differentially expressing transcripts from both top and bottom internodes were validated by qRT-PCR, indicating their importance in such study. Results also enriched sugarcane transcriptome resources useful for omics study in genus Saccharum and family Poaceae.

Construction of pseudomolecule sequences of Brassica rapa ssp. pekinensis inbred line CT001 and analysis of spontaneous mutations derived via sexual propagation

Chinese cabbage (Brassica rapa ssp. pekinensis) is a major crop that is widely cultivated, especially in Korea, Japan, and China. With the advent of next generation sequencing technology, the cost and time required for sequencing have decreased and the development of genome research accelerated. Genome sequencing of Chinese cabbage was completed in 2011 using the variety Chiifu-401-42, and since then the genome has been continuously updated. In the present study, we conducted whole-genome sequencing of Chinese cabbage inbred line CT001, a line widely used in traditional or molecular breeding, to improve the accuracy of genetic polymorphism analysis. The constructed CT001 pseudomolecule represented 85.4% (219.8 Mb) of the Chiifu reference genome, and a total of 38,567 gene models were annotated using RNA-Seq analysis. In addition, the spontaneous mutation rate of CT001 was estimated by resequencing DNA obtained from individual plants after sexual propagation for six generations to estimate the naturally occurring variations. The CT001 pseudomolecule constructed in this study will provide valuable resources for genomic studies on Chinese cabbage.

Cyberinfrastructure to Improve Forest Health and Productivity: The Role of Tree Databases in Connecting Genomes, Phenomes, and the Environment

Despite tremendous advancements in high throughput sequencing, the vast majority of tree genomes, and in particular, forest trees, remain elusive. Although primary databases store genetic resources for just over 2,000 forest tree species, these are largely focused on sequence storage, basic genome assemblies, and functional assignment through existing pipelines. The tree databases reviewed here serve as secondary repositories for community data. They vary in their focal species, the data they curate, and the analytics provided, but they are united in moving toward a goal of centralizing both data access and analysis. They provide frameworks to view and update annotations for complex genomes, interrogate systems level expression profiles, curate data for comparative genomics, and perform real-time analysis with genotype and phenotype data. The organism databases of today are no longer simply catalogs or containers of genetic information. These repositories represent integrated cyberinfrastructure that support cross-site queries and analysis in web-based environments. These resources are striving to integrate across diverse experimental designs, sequence types, and related measures through ontologies, community standards, and web services. Efficient, simple, and robust platforms that enhance the data generated by the research community, contribute to improving forest health and productivity.

MAGIC populations in crops: current status and future prospects

MAGIC populations present novel challenges and opportunities in crops due to their complex pedigree structure. They offer great potential both for dissecting genomic structure and for improving breeding populations. The past decade has seen the rise of multiparental populations as a study design offering great advantages for genetic studies in plants. The genetic diversity of multiple parents, recombined over several generations, generates a genetic resource population with large phenotypic diversity suitable for high-resolution trait mapping. While there are many variations on the general design, this review focuses on populations where the parents have all been inter-mated, typically termed Multi-parent Advanced Generation Intercrosses (MAGIC). Such populations have already been created in model animals and plants, and are emerging in many crop species. However, there has been little consideration of the full range of factors which create novel challenges for design and analysis in these populations. We will present brief descriptions of large MAGIC crop studies currently in progress to motivate discussion of population construction, efficient experimental design, and genetic analysis in these populations. In addition, we will highlight some recent achievements and discuss the opportunities and advantages to exploit the unique structure of these resources post-QTL analysis for gene discovery.

Bioinformatics: identification of markers from next-generation sequence data

With the advent of sequencing technology, next-generation sequencing (NGS) technology has dramatically revolutionized plant genomics. NGS technology combined with new software tools enables the discovery, validation, and assessment of genetic markers on a large scale. Among different markers systems, simple sequence repeats (SSRs) and Single nucleotide polymorphisms (SNPs) are the markers of choice for genetics and plant breeding. SSR markers have been a choice for large-scale characterization of germplasm collections, construction of genetic maps, and QTL identification. Similarly, SNPs are the most abundant genetic variations with higher frequencies throughout the genome of plant species. This chapter discusses various tools available for genome assembly and widely focuses on SSR and SNP marker discovery.

Molecular marker databases

The detection and analysis of genetic variation plays an important role in plant breeding and this role is increasing with the continued development of genome sequencing technologies. Molecular genetic markers are important tools to characterize genetic variation and assist with genomic breeding. Processing and storing the growing abundance of molecular marker data being produced requires the development of specific bioinformatics tools and advanced databases. Molecular marker databases range from species specific through to organism wide and often host a variety of additional related genetic, genomic, or phenotypic information. In this chapter, we will present some of the features of plant molecular genetic marker databases, highlight the various types of marker resources, and predict the potential future direction of crop marker databases.

A chromosomal genomics approach to assess and validate the desi and kabuli draft chickpea genome assemblies

With the expansion of next-generation sequencing technology and advanced bioinformatics, there has been a rapid growth of genome sequencing projects. However, while this technology enables the rapid and cost-effective assembly of draft genomes, the quality of these assemblies usually falls short of gold standard genome assemblies produced using the more traditional BAC by BAC and Sanger sequencing approaches. Assembly validation is often performed by the physical anchoring of genetically mapped markers, but this is prone to errors and the resolution is usually low, especially towards centromeric regions where recombination is limited. New approaches are required to validate reference genome assemblies. The ability to isolate individual chromosomes combined with next-generation sequencing permits the validation of genome assemblies at the chromosome level. We demonstrate this approach by the assessment of the recently published chickpea kabuli and desi genomes. While previous genetic analysis suggests that these genomes should be very similar, a comparison of their chromosome sizes and published assemblies highlights significant differences. Our chromosomal genomics analysis highlights short defined regions that appear to have been misassembled in the kabuli genome and identifies large-scale misassembly in the draft desi genome. The integration of chromosomal genomics tools within genome sequencing projects has the potential to significantly improve the construction and validation of genome assemblies. The approach could be applied both for new genome assemblies as well as published assemblies, and complements currently applied genome assembly strategies.

New technologies for ultrahigh-throughput genotyping in plant taxonomy

Molecular genetic markers represent one of the most powerful tools for the analysis of variation between plant genomes. Molecular marker technology has developed rapidly over the last decade, with the introduction of new DNA sequencing methods and the development of high-throughput genotyping methods. Single nucleotide polymorphisms (SNPs) now dominate applications in modern plant genetic analysis. The reducing cost of DNA sequencing and increasing availability of large sequence data sets permit the mining of this data for large numbers of SNPs. These may then be used in applications such as genetic linkage analysis and trait mapping, diversity analysis, association studies, and marker-assisted selection. Here we describe automated methods for the discovery of SNP molecular markers and new technologies for high-throughput, low-cost molecular marker genotyping. Examples include SNP discovery using autoSNPdb and wheatgenome.info as well as SNP genotyping using Illumina's GoldenGate™ and Infinium™ methods.

Next generation characterisation of cereal genomes for marker discovery

Cereal crops form the bulk of the world’s food sources, and thus their importance cannot be understated. Crop breeding programs increasingly rely on high-resolution molecular genetic markers to accelerate the breeding process. The development of these markers is hampered by the complexity of some of the major cereal crop genomes, as well as the time and cost required. In this review, we address current and future methods available for the characterisation of cereal genomes, with an emphasis on faster and more cost effective approaches for genome sequencing and the development of markers for trait association and marker assisted selection (MAS) in crop breeding programs.

Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement

More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.

Accessing complex crop genomes with next-generation sequencing

Many important crop species have genomes originating from ancestral or recent polyploidisation events. Multiple homoeologous gene copies, chromosomal rearrangements and amplification of repetitive DNA within large and complex crop genomes can considerably complicate genome analysis and gene discovery by conventional, forward genetics approaches. On the other hand, ongoing technological advances in molecular genetics and genomics today offer unprecedented opportunities to analyse and access even more recalcitrant genomes. In this review, we describe next-generation sequencing and data analysis techniques that vastly improve our ability to dissect and mine genomes for causal genes underlying key traits and allelic variation of interest to breeders. We focus primarily on wheat and oilseed rape, two leading examples of major polyploid crop genomes whose size or complexity present different, significant challenges. In both cases, the latest DNA sequencing technologies, applied using quite different approaches, have enabled considerable progress towards unravelling the respective genomes. Our ability to discover the extent and distribution of genetic diversity in crop gene pools, and its relationship to yield and quality-related traits, is swiftly gathering momentum as DNA sequencing and the bioinformatic tools to deal with growing quantities of genomic data continue to develop. In the coming decade, genomic and transcriptomic sequencing, discovery and high-throughput screening of single nucleotide polymorphisms, presence-absence variations and other structural chromosomal variants in diverse germplasm collections will give detailed insight into the origins, domestication and available trait-relevant variation of polyploid crops, in the process facilitating novel approaches and possibilities for genomics-assisted breeding.

Bread matters: a national initiative to profile the genetic diversity of Australian wheat

The large and complex genome of wheat makes genetic and genomic analysis in this important species both expensive and resource intensive. The application of next-generation sequencing technologies is particularly resource intensive, with at least 17 Gbp of sequence data required to obtain minimal (1×) coverage of the genome. A similar volume of data would represent almost 40× coverage of the rice genome. Progress can be made through the establishment of consortia to produce shared genomic resources. Australian wheat genome researchers, working with Bioplatforms Australia, have collaborated in a national initiative to establish a genetic diversity dataset representing Australian wheat germplasm based on whole genome next-generation sequencing data. Here, we describe the establishment and validation of this resource which can provide a model for broader international initiatives for the analysis of large and complex genomes.