An online database for einkorn wheat to aid in gene discovery and functional genomics studies

Diploid A-genome wheat (einkorn wheat) presents a nutrition-rich option as an ancient grain crop and a resource for the improvement of bread wheat against abiotic and biotic stresses. Realizing the importance of this wheat species, reference-level assemblies of two einkorn wheat accessions were generated (wild and domesticated). This work reports an einkorn genome database that provides an interface to the cereals research community to perform comparative genomics, applied genetics and breeding research. It features queries for annotated genes, the use of a recent genome browser release, and the ability to search for sequence alignments using a modern BLAST interface. Other features include a comparison of reference einkorn assemblies with other wheat cultivars through genomic synteny visualization and an alignment visualization tool for BLAST results. Altogether, this resource will help wheat research and breeding. Database URL  https://wheat.pw.usda.gov/GG3/pangenome

Einkorn genomics sheds light on history of the oldest domesticated wheat

Einkorn (Triticum monococcum) was the first domesticated wheat species, and was central to the birth of agriculture and the Neolithic Revolution in the Fertile Crescent around 10,000 years ago$^{1,2}$. Here we generate and analyse 5.2-Gb genome assemblies for wild and domesticated einkorn, including completely assembled centromeres. Einkorn centromeres are highly dynamic, showing evidence of ancient and recent centromere shifts caused by structural rearrangements. Whole-genome sequencing analysis of a diversity panel uncovered the population structure and evolutionary history of einkorn, revealing complex patterns of hybridizations and introgressions after the dispersal of domesticated einkorn from the Fertile Crescent. We also show that around 1% of the modern bread wheat (Triticum aestivum) A subgenome originates from einkorn. These resources and findings highlight the history of einkorn evolution and provide a basis to accelerate the genomics-assisted improvement of einkorn and bread wheat

Singularity image and example dataset for Genome Variant Calling Workflow

<p>The Genome Variant Calling Workflow (GVCW) is classified into four phases: (1) Genome mapping, (2) Variants discovery, (3) Call set refinement and combining variants, and (4) Variants matrixes. We provided the singularity image for various bioinformatics tools used in GVCW. Further, the automation scripts and the example dataset are included in the provided docker image as a single compressed file (rice_pipeline_demo.tar.gz). The instructions for running the docker image for 4 different phases of workflows is given here: <a title="Singularity image and example dataset for Genome Variant Calling Workflow " href="https://github.com/IBEXCluster/Rice-Variant-Calling/wiki/Singularity">WiKi page for Singularity</a>. </p&gt

IBEXCluster/Rice-Variant-Calling: HPC-GVCW

<p><span>The objective of this proposed workflow is to </span><span>automate and accelerate the variant calling </span><span>at </span><span>high-performance computing (HPC)</span><span> platform </span><span>for </span><span>rice and other</span> <span>crops, e.g., maize, soybean and sorghum</span><span>. </span><span>The workflow is designed for HPC platform, where the variants of 3,000 rice samples could be processed with in a day and the workflow is also</span><span> suitable for </span><span>other </span><span>different system architectures </span><span>that includes</span><span> cluster </span><span>(</span><span>or cloud platform</span><span>)</span><span>, and high-end workstations</span><span>. </span><span>We classified the workflow into 4 Phases: (1) Genome mapping, (</span><span>2</span><span>) Variants discovery, (</span><span>3</span><span>) Call set refinement and combining </span><span>variants</span><span>, and (</span><span>4</span><span>) Variants </span><span>matrixes</span><span>. We </span><span>use</span><span> Genome Analysis Toolkit (GATK) best practices for large-scale variants discovery and </span><span>created a “</span><span>Genome Index splitter</span><span>” (GIS) </span><span>algorithm</span> <span>for data parallelization. We built every stage of this workflow is independent & flexible entity, where it can be seamlessly executed across different system architectures. The automation on every stage of the workflow is scalable across multiple nodes via data parallelization algorithm which will take care of data distribution. </span><span>The</span><span> novel data parallelization algorithm</span><span>,</span><span> which takes care of chromosome splitting into multiple chunks</span><span>,</span> <span>and</span><span> it can be executed independently across the nodes to reduce the execution time. The flexibility of the workflow offers collaboration in data sharing, data processing (e.g., various stages based on their computational limitations), improve the resource utilization (e.g., simultaneously use different system architectures), minimize overall execution time and many more. </span></p&gt

Fonio millet genome unlocks African orphan crop diversity for agriculture in a changing climate

Sustainable food production in the context of climate change necessitates diversification of agriculture and a more efficient utilization of plant genetic resources. Fonio millet (Digitaria exilis) is an orphan African cereal crop with a great potential for dryland agriculture. Here, we establish high-quality genomic resources to facilitate fonio improvement through molecular breeding. These include a chromosome-scale reference assembly and deep re-sequencing of 183 cultivated and wild Digitaria accessions, enabling insights into genetic diversity, population structure, and domestication. Fonio diversity is shaped by climatic, geographic, and ethnolinguistic factors. Two genes associated with seed size and shattering showed signatures of selection. Most known domestication genes from other cereal models however have not experienced strong selection in fonio, providing direct targets to rapidly improve this crop for agriculture in hot and dry environments

From QTL to variety- Harnessing the benefits of QTLs for drought, flood and salt tolerance in mega rice varieties of India through a multi-institutional network.

Rice is a staple cereal of India cultivated in about 43.5 Mha area but with relatively low average productivity. Abiotic factors like drought, flood and salinity affect rice production adversely in more than 50% of this area. Breeding rice varieties with inbuilt tolerance to these stresses offers an economically viable and sustainable option to improve rice productivity. Availability of high quality reference genome sequence of rice, knowledge of exact position of genes/QTLs governing tolerance to abiotic stresses andavailability of DNA markers linked to these traits has opened up opportunities for breeders to transfer the favorable alleles into widely grown rice varieties through marker-assisted back cross breeding (MABB). Alarge multi-institutional project, “From QTL to variety: marker-assisted breeding of abiotic stress tolerant rice varieties with major QTLs for drought, submergence and salt tolerance” was initiated in 2010 with funding support from Department of Biotechnology, Government of India, in collaboration with Interna-tional Rice Research Institute, Philippines. The main focus of this project is to improve rice productivity inthe fragile ecosystems of eastern, northeastern and southern part of the country, which bear the brunt ofone or the other abiotic stresses frequently. Seven consistent QTLs for grain yield under drought, namely,qDTY1.1, qDTY2.1, qDTY2.2, qDTY3.1, qDTY3.2, qDTY9.1and qDTY12.1are being transferred into submergence IR64-Sub1. To address the problem of complete submergence due to flash floods in the major river basins,the Sub1 gene is being transferred into ten highly popular locally adapted rice varieties namely, ADT 39,ADT 46, Bahadur, HUR 105, MTU 1075, Pooja, Pratikshya, Rajendra Mahsuri, Ranjit, and Sarjoo 52. Further,to address the problem of soil salinity, Saltol, a major QTL for salt tolerance is being transferred into sevenpopular locally adapted rice varieties, namely, ADT 45, CR 1009, Gayatri, MTU 1010, PR 114, Pusa 44 andSarjoo 52. Genotypic background selection is being done after BC2F2stage using an in-house designed50K SNP chip on a set of twenty lines for each combination, identified with phenotypic similarity in the field to the recipient parent. Near-isogenic lines with more than 90% similarity to the recipient parentare now in advanced generation field trials. These climate smart varieties are expected to improve rice productivity in the adverse ecologies and contribute to the farmer’s livelihood