SUMMARY The epigenome orchestrates genome accessibility, functionality and three-dimensional structure. Because epigenetic variation can impact transcription and thus phenotypes, it may contribute to adaptation. Here we report 1,107 high-quality single-base resolution methylomes and 1,203 transcriptomes from the 1001 Genomes collection of Arabidopsis thaliana. Although the genetic basis of methylation variation is highly complex, geographic origin is a major predictor of genome-wide DNA methylation levels and of altered gene expression caused by epialleles. Comparison to cistrome and epicistrome datasets identifies associations between transcription factor binding sites, methylation, nucleotide variation and co-expression modules. Physical maps for nine of the most diverse genomes reveals how transposons and other structural variants shape the epigenome, with dramatic effects on immunity genes. The 1001 Epigenomes Project provides a comprehensive resource for understanding how variation in DNA methylation contributes to molecular and non-molecular phenotypes in natural populations of the most studied model plant.

The T-DNA sequence-indexed mutant collections contain insertional mutants for most Arabidopsis thaliana genes and have played an important role in plant biology research for almost two decades. By providing a large source of mutant alleles for in vivo characterization of gene function, this resource has been leveraged thousands of times to study a wide range of problems in plant biology. Our primary goal in this chapter is to provide a general guide to strategies for the effective use of the data and materials in these collections. To do this, we provide a general introduction to the T-DNA insertional sequence-indexed mutant collections with a focus on how best to use the available data sources for good line selection. As isolation of a homozygous line is a common next step once a potential disruption line has been identified, the second half of the chapter provides a step-by-step guide for the design and implementation of a T-DNA genotyping pipeline. Finally, we describe interpretation of genotyping results and include a troubleshooting section for common types of segregation distortions that we have observed. In this chapter we introduce both basic concepts and specific applications to both new and more experienced users of the collections for the design and implementation of small- to large-scale genotyping pipelines.

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