GEO DataSets

(Submitter supplied) Through domestication, humans have substantially altered the morphology of Zea mays ssp. parviglumis (teosinte) into the currently recognizable maize. A wealth of archeological and population genetic data has established maize as a model system for studying domestication , genome evolution and the genetics and evolution of complex traits. We used expression profiling of 18,242 genes for 38 diverse maize genotypes and 18 teosinte genotypes to examine how domestication has re-shaped the transcriptome of maize seedlings. more...

Organism:

Zea mays; Zea mays subsp. parviglumis

Type:

Expression profiling by array

Platform:

GPL13736

94 Samples

Download data: PAIR

(Submitter supplied) DNA methylation is a ubiquitous chromatin feature — in maize, more than 25% of cytosines in the genome are methylated. Recently, major progress has been made in describing the molecular mechanisms driving methylation, yet variation and evolution of the methylation landscape during maize domestication remain largely unknown. Here we leveraged whole-genome sequencing (WGS) and whole-genome bisulfite sequencing (WGBS) on populations of modern maize, landrace, and teosinte (Zea mays ssp. more...

Organism:

Zea mays

Type:

Other; Methylation profiling by high throughput sequencing

Platforms:

GPL15463 GPL17628

74 Samples

Download data: BED, XLS

(Submitter supplied) Modification of cis regulatory elements to produce differences in gene expression level, localization, and timing is an important mechanism by which organisms evolve divergent adaptations. To examine gene regulatory change during the domestication of maize from its wild progenitor, teosinte, we assessed allele-specific expression in a collection of maize and teosinte inbreds and their F1 hybrids using three tissues from different developmental stages. more...

Organism:

Zea mays

Type:

Expression profiling by high throughput sequencing

Platform:

GPL15463

448 Samples

Download data: TXT

(Submitter supplied) Following the domestication of maize over the past 10,000 years, breeders have exploited the extensive genetic diversity of this species to mold its phenotype to meet human needs. The extent of structural variation, including copy number variation (CNV) and presence/absence variation (PAV), which are thought to contribute to the extraordinary phenotypic diversity and plasticity of this important crop, have not been elucidated. more...

Organism:

Zea mays

Type:

Genome variation profiling by genome tiling array

Platform:

GPL8790

9 Samples

Download data: PAIR, TXT

(Submitter supplied) Cis-regulatory elements (CREs) encode the genomic blueprints for coordinating the spatiotemporal regulation of gene transcription programs necessary for highly specialized cellular functions. To identify cis-regulatory elements underlying cell-type specification and developmental transitions, we implemented single-cell sequencing of Assay for Transposase Accessible Chromatin (scATAC-seq) in an atlas of Zea mays tissues and organs. more...

Organism:

Zea mays; Arabidopsis thaliana

Type:

Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing

Platforms:

GPL25410 GPL26208

14 Samples

Download data: TXT

(Submitter supplied) Different plant species within the grasses were parallel targets of domestication, giving rise to crops with distinct evolutionary histories and traits. Key traits that distinguish these species are mediated by specialized cell types. Here, we compare the transcriptomes of root cells in three grass species—Zea mays (maize), Sorghum bicolor (sorghum), and Setaria viridis (Setaria). We first show that single-cell and single-nucleus RNA-seq provide complementary readouts of cell identity in both dicots and monocots, warranting a combined analysis. more...

Organism:

Setaria viridis; Zea mays; Sorghum bicolor; Arabidopsis thaliana

Type:

Expression profiling by high throughput sequencing

4 related Platforms

22 Samples

Download data: CSV, RDATA

(Submitter supplied) Inferring phenotypic outcome from genomics features is the promise and challenge for basic and applied research. Functionally testing whether the feature(s) with predictive power can shed light on the underlying mechanism remains largely unexplored. We address these gaps by applying a machine learning approach to predict phenotypes based on transcriptome data. We validate the predictive models in silico using out-of-sample varieties and in planta by reverse genetics. more...

Organism:

Arabidopsis thaliana; Zea mays

Type:

Expression profiling by high throughput sequencing

Platforms:

GPL17639 GPL24163

199 Samples

Download data: TXT