Genetic differences between Arabidopsis thaliana accessions underlie the plant’s extensive phenotypic variation, and until now these have been interpreted largely in the context of the annotated reference accession Col-0. Here we report the sequencing, assembly and annotation of the genomes of 18 natural A. thaliana accessions, and their transcriptomes. When assessed on the basis of the reference annotation, one-third of protein-coding genes are predicted to be disrupted in at least one accession. However, re-annotation of each genome revealed that alternative gene models often restore coding potential. Gene expression in seedlings differed for nearly half of expressed genes and was frequently associated with cis variants within 5 kilobases, as were intron retention alternative splicing events. Sequence and expression variation is most pronounced in genes that respond to the biotic environment. Our data further promote evolutionary and functional studies in A. thaliana, especially the MAGIC genetic reference population descended from these accessions.

Rice, the primary source of dietary calories for half of humanity, is the first crop plant for which a high-quality reference genome sequence from a single variety was produced. We used resequencing microarrays to interrogate 100 Mb of the unique fraction of the reference genome for 20 diverse varieties and landraces that capture the impressive genotypic and phenotypic diversity of domesticated rice. Here, we report the distribution of 160,000 nonredundant SNPs. Introgression patterns of shared SNPs revealed the breeding history and relationships among the 20 varieties; some introgressed regions are associated with agronomic traits that mark major milestones in rice improvement. These comprehensive SNP data provide a foundation for deep exploration of rice diversity and gene-trait relationships and their use for future rice improvement.introgression ͉ Oryza sativa ͉ resequencing ͉ SNP discovery T he genomes of domesticated rice, Oryza sativa, contain a wealth of information that can explain the large morphological, physiological, and ecological variation observed in the many varieties cultivated for food. To meet population demands by 2025, rice production must increase by 24% (1). The innovative use of genetic diversity will play a key role in reaching this ambitious goal.The availability of complete genome sequences provides a starting point to understanding the tremendous diversity of the rice gene pool at a fine scale. Among the organisms with a high-quality genome sequence from at least one individual or strain, such as human, mouse, and Arabidopsis, genomewide surveys of SNP variation in small or moderately sized samples have captured significant portions of within-species variation. In human and mouse, for example, a sampling of 71 and 15 individuals captured 80% and 43% of the genotypic variation, respectively (2, 3). In the model plant, Arabidopsis, 20 diverse varieties captured Ͼ90% of the common genotypic variation in the species (4).We initiated the OryzaSNP project (www.OryzaSNP.org) to discover genetic variation within 20 rice varieties and landraces. These varieties, the OryzaSNPset collection (Table S1), are genetically diverse and actively used in international breeding programs because of their wide range of agronomic attributes (5). Most varieties belong to the 2 main groups, indica and japonica, including tropical and temperate japonica, whereas others represent the aus, deep water, and aromatic rice groups. Adapting a hybridization approach previously used for human, mouse, and Arabidopsis (3, 6, 7), we determined SNP variation in 100 Mb of the rice genome, representing Ϸ80% of the nonrepetitive portion of the 390-Mb Nipponbare reference genome (8). Here, we describe the discovery of 159,478 high-quality, nonredundant SNPs distributed across the entire genomes of the OryzaSNPset. Relative to the model dicotyledenous plant Arabidopsis (4), typical haplotype blocks in indica rice varieties are longer (Ϸ200 kb). Observed patterns of shared SNPs among groups indicate introgression caused by rece...

We provide a novel web service, called rQuant.web, allowing convenient access to tools for quantitative analysis of RNA sequencing data. The underlying quantitation technique rQuant is based on quadratic programming and estimates different biases induced by library preparation, sequencing and read mapping. It can tackle multiple transcripts per gene locus and is therefore particularly well suited to quantify alternative transcripts. rQuant.web is available as a tool in a Galaxy installation at http://galaxy.fml.mpg.de. Using rQuant.web is free of charge, it is open to all users, and there is no login requirement.