Biology subjects

Alexandrov, N.

Publications and source records attributed to Alexandrov, N..

3 recordsLinked to original sources

bioRxiv2019

Twelve Platinum-Standard Reference Genomes Sequences (PSRefSeq) that complete the full range of genetic diversity of Asian rice

As the human population grows from 7.8 billion to 10 billion over the next 30 years, breeders must do everything possible to create crops that are highly productive and nutritious, while simultaneously having less of an environmental footprint. Rice will play a critical role in meeting this demand and thus, knowledge of the full repertoire of genetic diversity that exists in germplasm banks across the globe is required. To meet this demand, we describe the generation, validation and preliminary analyses of transposable element and long-range structural variation content of 12 near-gap-free reference genome sequences (RefSeqs) from representatives of 12 of 15 subpopulations of cultivated rice. When combined with 4 existing RefSeqs, that represent the 3 remaining rice subpopulations and the largest admixed population, this collection of 16 Platinum Standard RefSeqs (PSRefSeq) can be used as a pan-genome template to map resequencing data to detect virtually all standing natural variation that exists in the pan-cultivated rice genome.

genomics↗

bioRxiv2019

The rice leaf microbiome has a conserved community structure controlled by complex host-microbe interactions

Understanding the factors that influence the outcome of crop interactions with microbes is key to managing crop diseases and improving yield. While the composition, structure and functional profile of crop microbial communities are shaped by complex interactions between the host, microbes and the environment, the relative contribution of each of these factors is mostly unknown. Here, we profiled the community composition of bacteria across leaves of 3,024 rice (Oryza sativa) accessions from field trials in China and the Philippines using metagenomics. Despite significant differences in diversity between environments, the structure and metabolic profiles of the microbiome appear to be conserved, suggesting that microbiomes converge onto core functions. Furthermore, co-occurrence analysis identified microbial hubs that regulate the network structure of the microbiome. We identified rice genomic regions controlling the abundance of these hubs, enriched for processes involved in stress responses and carbohydrate metabolism. We functionally validated the importance of these processes, finding that abundance of hub taxa was different in rice mutants with altered cellulose and salicylate accumulation, two major metabolites at the host-microbe interactions interface. By identifying key host genomic regions, host traits and hub microbes that govern microbiome composition, our study opens the door to designing future cropping systems.

microbiology↗

bioRxiv2018

Rice Galaxy: an open resource for plant science

Background\n\nRice molecular genetics, breeding, genetic diversity, and allied research (such as rice-pathogen interaction) have adopted sequencing technologies and high density genotyping platforms for genome variation analysis and gene discovery. Germplasm collections representing rice diversity, improved varieties and elite breeding materials are accessible through rice gene banks for use in research and breeding, with many having genome sequences and high density genotype data available. Combining phenotypic and genotypic information on these accessions enables genome-wide association analysis, which is driving quantitative trait loci (QTL) discovery and molecular marker development. Comparative sequence analyses across QTL regions facilitate the discovery of novel alleles. Analyses involving DNA sequences and large genotyping matrices for thousands of samples, however, pose a challenge to non-computer savvy rice researchers.\n\nFindings\n\nWe adopted the Galaxy framework to build the federated Rice Galaxy resource, with shared datasets, tools, and analysis workflows relevant to rice research. The shared datasets include high density genotypes from the 3,000 Rice Genomes project and sequences with corresponding annotations from nine published rice genomes. Rice Galaxy includes tools for designing single nucleotide polymorphism (SNP) assays, analyzing genome-wide association studies, population diversity, rice-bacterial pathogen diagnostics, and a suite of published genomic prediction methods. A prototype Rice Galaxy compliant to Open Access, Open Data, and Findable, Accessible, Interoperable, and Reproducible principles is also presented.\n\nConclusions\n\nRice Galaxy is a freely available resource that empowers the plant research community to perform state-of-the-art analyses and utilize publicly available big datasets for both fundamental and applied science.

bioinformatics↗