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Aninteractive mapshowingPopulus trichocarpaGWAS sub-population structure identified by DBSCAN clustering, which were derived from a UMAP projection of the top 8 PCs of LD-pruned pangenome SNP data. Geographic origins are searchable by genotype or river system using the search bar.
Aninteractive mapshowing the geographic distribution ofPopulus trichocarpagenotypes carrying a deletion structural variant overlaid with Mount Rainier volcanic hazard zones. Hazard layers including lahar inundation zones and pyroclastic flow zones can be toggled on and off and geographic origins are searchable by genotype or river system using the search bar.
Aninteractive mapshowing the geographic distribution of long readPopulus trichocarpagenotypes (n=41) categorized by copy number variant (CNV) genotype class at a locus on chromosome 2. Points are colored by CNV category and can be toggled by using the layer control. Geographic origins are searchable by genotype or river system using the search bar.
Aninteractive UMAPplot derived from the top 8 PCs of LD-prunedPopulus trichocarpapangenome SNP data. Points are colored by DBSCAN clustering.
Aninteractive UMAP plotderived from the top 8 PCs of LD-prunedPopulus trichocarpapangenome SNP data. Points are colored by ADMIXTURE population structure (k=3) and geographic origins are searchable by genotype or river system using the search bar.
This data consists of total ion chromatogram (TIC) normalized Pyrolysis-Molecular Beam Mass Spectrometry (py-MBMS) spectra collected to predict lignin content and lignin monomeric ratios for “Variation in Biomass Yield and Cell Wall Composition in Switchgrass Natural Variants Under Two Nitrogen Regimes” (https://doi.org/10.1007/s12155-025-10838-8).
This dataset contains solid-state 13C NMR data and atomistic molecular dynamics simulation files supporting the study of nanoscale secondary cell wall architecture across 13 genetically diverse Populus trichocarpa genotypes grown under uniform greenhouse conditions in 13C-enriched CO2 atmospheres (~89% 13C enrichment).The dataset contains two collections of solid-state 13C NMR data. (1) 200 MHz data (Bruker Avance III HD, 4 mm HX probe, 10 kHz MAS): raw Bruker TopSpin experiment folders and DMFIT-exported ascii spectra for selective and non-selective 1D 13C-13C spin diffusion experiments (3000 ms mixing) used to quantify inter-polymer spatial proximities, and short-mixing (1 ms) reference spectra used for polymeric abundance quantification by spectral deconvolution. (2) 600 MHz data (Bruker Avance III, 1.6 mm PhoenixNMR HXY probe, 30 kHz MAS): raw Bruker TopSpin experiment folders containing 2D CORD, 2D CP-INADEQUATE, and 13C/1H relaxation (T1, T1rho) experiments for all 13 genotypes, with processed Excel workbooks per experiment type. Molecular dynamics simulation code, coordinate files, and analysis scripts (NAMD/CHARMM/Python) for six atomistic cell wall models are included. Summarized ssNMR data are compiled into a single excel file and subjected to statistical analysis. Multivariate analysis code (PCA, Pearson correlation) and summary data are provided as excel worksheets and Jupyter notebooks (Python 3).
These data are from the manuscript “Application of Genomic Prediction in a Populus trichocarpa Breeding Program”, by Brian J. Stanton, David Macaya-Sanz, Chanaka Roshan Abeyratne, David Kainer, Kathy Haiby, Austin Himes, Carlos Gantz, Gerald A. Tuskan, and Stephen P. DiFazio. The data are based on genome resequencing to approximately 10X depth on two collections of Populus trichocarpa trees from Oregon, Washington, California, and British Columbia. The first collection consists of 293 genets collected by Poplar Innovations LLC for a breeding program. The second collection consists of 961 trees collected for the purpose of genome-wide association studies. These genets were sequenced using short, paired-end Illumina sequence reads (Chhetri et al. 2019). Reads were aligned to the P. trichocarpa ′Stettler-14′ reference (Hofmeister et al. 2020), with minor modifications to correct mis-assemblies (Zhou et al. 2020), and variants were called as per methods described in (Abeyratne et al. 2023). Identified variants were filtered using GATK’s VariantFiltration tool (DePristo et al. 2011), with filter expression flag set to “AF < 0.01 || AF > 0.99 || QD < 10.0 || ExcessHet > 20.0 || FS > 10.0 || MQ < 58.0”. SNPs with severe departures from Hardy−Weinberg expectations (exact-test p< 0.01) were also removed using vcftools --hwe flag (Danecek et al. 2011), resulting in 15,627,211 bi-allelic SNPs. The data included here consist of 141,903 high quality bi-allelic genome-wide SNPs obtained by further filtering the original SNP dataset using vcftools with flags --maf 0.05, --max-maf 0.95, --max-missing 0.95, --min-meanDP 10.75, --max-meanDP 43.00, --thin 2000. Collectively, these filtering parameters removed SNPs with 1) a minor allele frequency ≤ 0.05; 2) proportion of missing data for individual loci exceeding 5%; 3) sequencing depth more than 2X mean-depth or less than 0.5X mean-depth; or 4) a distance of
This dataset contains the stem RNA-seq data of Populus tremula x P. alba genotypes of RGA triple knockout. Populus genotypes in this study includes the empty vector control (EV, Cas9-19) and three independent RGA triple mutants, 267-17 (RGAX4KO_17), 267-2
This dataset contains single-nucleus RNA sequencing data from poplar (Populusspp.) roots under control and methyl jasmonate (MeJA) treatment conditions. It was generated to investigate cell type-specific stress responses and to establish a high-resolution transcriptomic atlas in a perennial woody species.
We evaluated how harvest frequency affected plant C allocation and soil-water nutrient concentrations in a switchgrass (Panicum virgatumL.) system on the Eastern Shore of Maryland, U.S.A. Switchgrass established in 2023 was harvested once (1Cut), twice (2Cut), or three times (3Cut) during the 2024 growing season to represent potential feedstock-management regimes for anaerobic digestion. Aboveground biomass was measured at each harvest, and root biomass distribution, root C stocks, and soil properties were assessed to 60 cm after the growing season. During 2025, rainfall-synchronized soil water collected with tension lysimeters was analyzed for dissolved inorganic nitrogen (DIN) and inorganic phosphate (Pi) concentrations.
This dataset contains solid-state 1H and 13C NMR relaxometry data, differential scanning calorimetry (DSC) data, and size exclusion chromatography (SEC/GPC) data supporting the study of sub-glass-transition (sub-Tg) molecular dynamics in a composition- and sequence-controlled series of diphenyl-substituted polysiloxanes (PDMS, 14Ph, 33Ph, 50Ph, 67Ph, and 100Ph; 0–100% diphenylsiloxane content by mole).All solid-state NMR data were acquired on a 200 MHz Bruker Avance III HD spectrometer using a static 7 mm HX probe or a 4 mm HX probe under 4 kHz magic-angle spinning. Raw Bruker TopSpin experiment folders are included for: (1) variable-temperature 1H lineshape measurements used to determine linewidth (FWHM) as a function of temperature across the glass transition; (2) 1H T1 (saturation recovery with solid-echo detection), probing nanosecond-scale dynamics near the 1H Larmor frequency; (3) 1H T1rho (direct spin-lock, 62.5 kHz), probing microsecond-scale segmental dynamics; (4) 13C-detected Lee–Goldburg cross-polarization 1H T1rho (LGCPH T1rho) for 33Ph and 50Ph, resolving aromatic and aliphatic proton environments; and (5) 13C T1 relaxation for 33Ph and 50Ph. Differential scanning calorimetry data (TA Instruments DSC 25, −150 to +120 °C, up to +300 °C for 100Ph, 10 °C/min) are included for all six compositions and support the glass-transition temperatures in Table 1 and Figure 1. Size exclusion chromatography data (Agilent 1200 Series, PL-Gel 300 mixed-C column, THF mobile phase, polystyrene calibration standards) are included for the three synthesized copolymers (33Ph, 50Ph, 67Ph) and support the number-average molecular weights in Table 1. Processed data include per-composition relaxation-time summaries (Excel), curve-fitting and Bloembergen-Purcell-Pound (BPP) model analysis notebooks (Jupyter/Python), and Igor Pro (.pxp) master files used to generate the manuscript's figures.
This manuscript will present an advancement of transformative research that has been conducted at Oak Ridge National Laboratory (ORNL) over a 25-year period (2000–2025) on a variety of environmental and biological matrices. These investigations derived a fundamental understanding of how elemental detection and analysis of these matrices led to the knowledge and discovery of natural processes in plants and the environment. Each project led to the initiation of a new research area which unearthed awesome and novel breakthroughs. Highlights are listed below: 1. The preliminary research at ORNL centered on the detection of aerosols utilizing Laser-induced Breakdown Spectroscopy (LIBS) technology. The Clean Air Act Amendment (CAAA) of 1990 highlighted the importance of identifying hazardous air pollutants (HAPs) due to their impact on environmental and human health, thereby underscoring the need to detect various toxic elements. Research in aerosol chemistry aimed to identify these harmful elements released by factories during periods of increased emissions in their manufacturing processes. LIBS emerged as the most effective method for real-time, in situ measurements of metal species in both gaseous and aerosol phases. 2. An understanding of the presence of total carbon in soils gives perspective on how to develop carbon sequestration strategies. The recognition that carbon sinks can evolve back to carbon sources to emit back to the atmosphere was an important consideration. Also, the concentration of carbon in soil indicates the health of land areas for growing crops successfully. 3. The direct detection of most of the elements in a wood sample in a single emission spectrum, without sample preparation, encouraged the research to use the LIBS technique for preservative treated wood coupled with use of multivariate statistical methodology. Additionally, it encouraged the researchers to try to differentiate natural woods from different parts of the country, and it was successfully demonstrated that LIBS coupled with MVA analysis could differentiate wood of different species from each other and of similar species grown in different environments based on their elemental spectra. This was a breakthrough since it revealed a systematic approach to connect elemental scarcity and abundance to either drought or typical rainfall conditions for the hardwood trees grown in specific areas. 4. Furthermore, the research progressed to reveal physiological and developmental processes contributing to biomass production such that the variation in leaf elemental composition increases our understanding of terrestrial nutrient cycles, as well as tracking the transfer of toxic elements from soils to living organisms. 5. Recently another breakthrough viz., ionomics initiated the correlation of elements to specific genes, uncovering the function that the element performed in the plant. More recently, this has been extended from plants to fungi as well as fungi growing in symbiotic relations with plants.
Crassulacean acid metabolism (CAM) is a specialized photosynthetic pathway that enhances water-use efficiency by temporally separating nocturnal CO 2 uptake from daytime decarboxylation and carbon fixation. To uncover the regulatory mechanisms coordinating these temporal dynamics, we generated high-resolution, 48 h time-course transcriptomes for the CAM model Kalanchoe fedtschenkoi under both 12 h/12 h light/dark (LD) cycles and continuous light (LL). A rhythmicity analysis revealed that diel light cues are the dominant driver of transcript oscillations: 16,810 genes (54.3% of annotated genes) exhibited rhythmic expression only under LD, whereas just 399 genes (1.3%) remained rhythmic under LL. A smaller set of 3009 genes (9.7%) oscillated in both conditions, indicating that the intrinsic circadian clock sustains rhythmicity for a limited subset of the transcriptome. A gene co-expression network analysis revealed extensive integration between circadian clock components, core CAM pathway enzymes, and stomatal regulators, defining regulatory modules that coordinate metabolic and physiological timing. Notably, key hub genes associated with post-translational and post-transcriptional regulation, including the E3 ubiquitin ligase HUB2 and several pentatricopeptide repeat (PPR) proteins, act as central nodes in CAM-associated networks. This discovery implicates epigenetic and organellar regulation as previously unrecognized critical tiers of control in CAM. Together, our results support a regulatory model in which CAM rhythmicity is governed by both external light/dark cues and the endogenous circadian clock through multi-level control spanning transcriptional and protein-level regulation. To support community exploration, we also provide an interactive eFP (electronic Fluorescent Pictograph) browser for visualizing time-resolved gene expression profiles.
Data here corresponds to the data presented in the academic publication titled "1H NMR quantification of aromatic monomers from reductive catalytic fractionation" set to be published in ACS Sustainable Chemistry and Engineering. Data was gathered at the National Renewable Energy Laboratory From 2022-2025.
Plant bioengineering has generated tens of thousands of genotype-to-phenotype relationships, but this knowledge remains fragmented across narrative literature and difficult to use computationally. Inconsistent descriptions of DNA constructs, host species, and traits, including variable species names, omitted regulatory elements, and inconsistent gene symbols, impede data reuse, comparative analysis, and design-build-test-learn cycles. Here, we present the Plant Bioengineering Atlas, a literature-mined, ontology-grounded knowledge base assembled using an artificial intelligence (AI)-aided extraction pipeline. A large language model parsed open-access primary research articles to generate structured, provenance-anchored records of engineered genes, modification types, promoter-gene-terminator constructs, host species, target traits, and reported phenotypes, with every record traceable to its source. The current release contains 14,358 curated records encompassing 6,998 distinct genes across 436 plant species from 6,452 papers published between 2000 and 2026. Corpus analysis reveals that experiments are concentrated in a small group of model and crop species, disease and pathogen resistance is the most frequently engineered trait class, and constitutive regulatory parts (particularly the CaMV 35S promoter and NOS terminator) remain pervasive. Two in five records omit one or both flanking regulatory elements (i.e., promoter and terminator), while only 23.4% describe cassettes in which both elements resolve to named part classes, exposing a systematic reproducibility gap. We organize these data into a knowledge graph linking genes, constructs, species, and traits; provide access through an interactive web portal; and propose an AI-compatible documentation standard for AI-ready reporting. The Plant Bioengineering Atlas provides a foundation for data-driven hypothesis generation and AI-aided plant biodesign.