Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade.

/pdf/big-data-astronomical-or-genomical-1motzi0nx2.pdf

Big data: Astronomical or genomical?

A Natural Allele of a Transcription Factor in Rice Confers Broad-Spectrum Blast Resistance.

We have witnessed an explosion in our understanding of the evolution and structure of plant genomes in recent years. Here, we highlight three important emergent realizations: (1) that the evolutionary history of all plant genomes contains multiple, cyclical episodes of whole-genome doubling that were followed by myriad fractionation processes; (2) that the vast majority of the variation in genome size reflects the dynamics of proliferation and loss of lineage-specific transposable elements; and (3) that various classes of small RNAs help shape genomic architecture and function. We illustrate ways in which understanding these organism-level and molecular genetic processes can be used for crop plant improvement.

/pdf/evolution-of-plant-genome-architecture-arph2a199b.pdf

Evolution of plant genome architecture

Recent advances in next-generation sequencing (NGS) and single nucleotide polymorphism (SNP) genotyping promise to greatly accelerate crop improvement if properly deployed. High-throughput SNP genotyping offers a number of advantages over previous marker systems, including an abundance of markers, rapid processing of large populations, a variety of genotyping systems to meet different needs, and straightforward allele calling and database storage due to the bi-allelic nature of SNP markers. NGS technologies have enabled rapid whole genome sequencing, providing extensive SNP discovery pools to select informative markers for different sets of germplasm. Highly multiplexed fixed array platforms have enabled powerful approaches such as genome-wide association studies. On the other hand, routine deployment of trait-specific SNP markers requires flexible, low-cost systems for genotyping smaller numbers of SNPs across large breeding populations, using platforms such as Fluidigm's Dynamic Arrays™, Douglas Scientific's Array Tape™, and LGC's automated systems for running KASP™ markers. At the same time, genotyping by sequencing (GBS) is rapidly becoming popular for low-cost high-density genome-wide scans through multiplexed sequencing. This review will discuss the range of options available to modern breeders for integrating SNP markers into their programs, whether by outsourcing to service providers or setting up in-house genotyping facilities, and will provide an example of SNP deployment for rice research and breeding as demonstrated by the Genotyping Services Lab at the International Rice Research Institute.

https://e-sc.org/upload/pbb/pdf/pbb-02-195.pdf

High-Throughput SNP Genotyping to Accelerate Crop Improvement

The purpose of this review is to present the most common and emerging DNA-based methods used to generate data for biodiversity and biomonitoring studies. As environmental assessment and monitoring programmes may require biodiversity information at multiple levels, we pay particular attention to the DNA metabarcoding method and discuss a number of bioinformatic tools and considerations for producing DNA-based indicators using operational taxonomic units (OTUs), taxa at a variety of ranks and community composition. By developing the capacity to harness the advantages provided by the newest technologies, investigators can "scale up" by increasing the number of samples and replicates processed, the frequency of sampling over time and space, and even the depth of sampling such as by sequencing more reads per sample or more markers per sample. The ability to scale up is made possible by the reduced hands-on time and cost per sample provided by the newest kits, platforms and software tools. Results gleaned from broad-scale monitoring will provide opportunities to address key scientific questions linked to biodiversity and its dynamics across time and space as well as being more relevant for policymakers, enabling science-based decision-making, and provide a greater socio-economic impact. As genomic approaches are continually evolving, we provide this guide to methods used in biodiversity genomics.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mec.14478

Scaling up: A guide to high-throughput genomic approaches for biodiversity analysis.

Rice is the world’s most important staple grown by millions of small-holder farmers. Sustaining rice production relies on the intelligent use of rice diversity. The 3,000 Rice Genomes Project is a giga-dataset of publically available genome sequences (averaging 14× depth of coverage) derived from 3,000 accessions of rice with global representation of genetic and functional diversity. The seed of these accessions is available from the International Rice Genebank Collection. Together, they are an unprecedented resource for advancing rice science and breeding technology. Our immediate challenge now is to comprehensively and systematically mine this dataset to link genotypic variation to functional variation with the ultimate goal of creating new and sustainable rice varieties that can support a future world population that will approach 9.6 billion by 2050.

https://link.springer.com/content/pdf/10.1186%2F2047-217X-3-8.pdf

The 3,000 rice genomes project: new opportunities and challenges for future rice research

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The 3,000 rice genomes project: new opportunities and challenges for future rice research