PRIMED: Preparation & Requisition of Integrated Microscopy + Energy Data
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Molecular Hypernetworks for Exploration of Multi-Dimensional Metabolomics Data (Chyper)
Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and help address the challenge of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used, for example, in the Global Natural Products Social Molecular Networking platform, are an increasingly popular computational strategy for exploring and visualizing molecular relationships and improving annotation. MNs use graph representations to show the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated to a single molecular identity. However, more advanced methods may better represent the complexity present in samples. Our work aims to increase confidence in annotation propagation by extending molecular network methods to include “molecular hypernetworks” (MHNs), able to natively represent multiway relationships among observations supporting both human and analytical processing. In this paper we first introduce MHNs illustrated with simple examples, and demonstrate how to build them from liquid chromatography- and ion mobility spectrometry- separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.
59 BASIC BIOLOGICAL SCIENCES↗
Depth-resolved sagebrush root metabolomics, rhizosphere microbial communities, and geochemistry at the East River Watershed
This data set consists of results from soil nutrient profile, untargeted metabolomics, mass spec imaging, and amplicon sequencing. Data for soil nutrient profile includes common cations (Ca, Mg, Na, and K etc.) extracted from 3 digesting steps – ammonia acetate (for exchangeable cations), nitric acid (for acid dissolved fraction), and hydrofluoric acid/perchloric acid (HF/HClO4) for whole soil digestion. It also includes concentration of organic carbon, inorganic nitrogen (ammonia and nitrate) and phosphorus (Bray-1 P and nitric acid extract), and total nitrogen and phosphorus. Data for untargeted metabolomics includes metabolomic profile for root exudate/tissues and soil extracts from depths at surface soil to saprolite, that were measured using gas chromatography – mass spectrometry (GC-MS), and liquid chromatography – tandem mass spectrometry (LC-MS/MS). Data for mass spec imaging includes spatial distribution of metabolites that were detected and annotated with Fourier transformation ion cyclotron resonance mass spectrometer (FTICR-MS). Data for amplicon sequencing includes the base paired 16S and ITS ribosomal RNA sequences from Miseq Illumina sequencing. All samples were collected from 2 sampling campaign October 2022 and June 2023. Collectively, these datasets enable a mechanistic evaluation of how nutrient acquisition, especially nitrogen and phosphorus, differs between shallow roots operating in soil and deep roots functioning within the fractured bedrock zone. All files are provided as comma-separated values (CSV) fies (.csv) and (GZIP) file (.gz). The compressed .gz FASTQ files can be read directly in R using the dada2 package as part of the amplicon sequence analysis workflow. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. This research was performed on a project award 60563 (https://dx.doi.org/10.46936/expl.proj.2022.60563/60008727) from the Environmental Molecular Sciences Laboratory, a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830.
EARTH SCIENCE > AGRICULTURE > SOILS > CARBON↗
Make the Most Out of Genome Announcements with KBase
Genomics is a dynamic field driven by new technology, massive growth in data, and changes in how scientists share knowledge. This brings new challenges for researchers looking to maximize the impact of their science and ensure the longevity of their data. These changes are especially relevant for the practice of publishing genome announcements. Genome announcements are meant to share and promote newly sequenced organisms with the larger scientific community. A few decades ago, genome announcements were highly anticipated hallmarks of novel science. But, what was once a publishing coup is now an every day event. It is harder and harder to keep abreast of all the genomes that have been sequenced and promote your own new genomes to get the attention and recognition of your research community. Fortunately, KBase can help construct genomes from your sequencing data, generate reports for your announcement, and promote your data to your research community.
59 BASIC BIOLOGICAL SCIENCES↗
What you get is not always what you see—pitfalls in solar array assessment using overhead imagery
Effective integration planning for small, distributed solar photovoltaic (PV) arrays into electric power grids requires access to high quality data: the location and power capacity of individual solar PV arrays. Unfortunately, national databases of small-scale solar PV do not exist; those that do are limited in their spatial resolution, typically aggregated up to state or national levels. While several promising approaches for solar PV detection have been published, strategies for evaluating the performance of these models are often highly heterogeneous from study to study. The resulting comparison of these methods for practical applications for energy assessments becomes challenging and may imply that the reported performance evaluations overly optimistic. The heterogeneity comes in many forms, each of which we explore in this work: the degree of diversity of the locations and sensors (e.g. different satellites, aerial photography) from which the training and validation data originate, the validation of ground truth (manual annotation of imagery vs known solar PV locations), the level of spatial aggregation (e.g. array-level vs regional estimates), and inconsistencies in the training and validation datasets (e.g. different datasets are used for each study and those data are not always made accessible). For each, we discuss emerging practices from the literature to address them or suggest directions of future research. As part of our investigation, we evaluate solar PV identification performance in two large regions: the entire state of Connecticut and the city of San Diego, CA. In Connecticut, we also use 33,114 known parcel-level solar PV installations from Berkeley Lab’s Tracking the Sun dataset to evaluate parcel-level performance and evaluate capacity estimates using 169 municipalities. We also make our code (which we call SolarMapper), pre-trained models, training data, and predictions publicly available and provide a web portal for interactively inspecting each prediction that was made. Here our findings suggest that traditional performance evaluation of the automated identification of solar PV from satellite imagery may be optimistic due to common limitations in the validation process. The takeaways from this work are intended to inform and catalyze the large-scale practical application of automated solar PV assessment techniques by energy researchers and professionals.
14 SOLAR ENERGY↗
IsoMatchMS : Open-Source Software for Automated Annotation and Visualization of High Resolution MALDI-MS Spectra
Due to its speed, accuracy, and adaptability to various sample types, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has become a popular method to identify molecular isotope profiles from biological samples. Often MALDI-MS data do not include tandem MS fragmentation data, and thus the identification of compounds in samples requires external databases so that the accurate mass of detected signals can be matched to known molecular compounds. Most relevant MALDI-MS software tools developed to confirm compound identifications are focused on small molecules (e.g., metabolites, lipids) and cannot be easily adapted to protein data due to their more complex isotopic distributions. Here, we present an R package called IsoMatchMS for the automated annotation of MALDI-MS data for multiple datatypes: intact proteins, peptides, and glycans. This tool accepts already derived molecular formulas or, for proteomics applications, can derive molecular formulas from a list of input peptides or proteins including proteins with post-translational modifications. In conclusion, visualization of all matched isotopic profiles is provided in a highly accessible HTML format called a trelliscope display, which allows users to filter and sort by several parameters such as match scores and the number of peaks matched. IsoMatchMS simplifies the annotation and visualization of MALDI-MS data for downstream analyses.
47 OTHER INSTRUMENTATION↗
DeepAndes: A Self-Supervised Vision Foundation Model for Multispectral Remote Sensing Imagery of the Andes
By mapping sites at large scales usingremotely sensed data, archaeologists can generate unique insights into long-term demographic trends, interregional social networks, and human adaptations in the past. Remote sensing surveys complement field-based approaches, and their reach can be especially great when combined with deep learning and computer vision techniques. However, conventional supervised deep learning methods face challenges in annotating fine-grained archaeological features at scale. In addition, while recent vision foundation models have shown remarkable success in learning large-scale remote sensing data with minimal annotations, most off-the-shelf solutions are designed for RGB images rather than multispectral satellite imagery, such as the eight-band data used in our study. In this article, we introduce DeepAndes, a transformer-based vision foundation model trained on three million multispectral satellite images, specifically tailored for Andean archaeology. DeepAndes incorporates a customized DINOv2 self-supervised learning algorithm optimized for eight-band multispectral imagery, marking the first foundation model designed explicitly for the Andes region. We evaluate its image understanding performance through imbalanced image classification, image instance retrieval, and pixel-level semantic segmentation tasks. Our experiments show that DeepAndes achieves superior F1 scores, mean average precision, and Dice scores in few-shot learning scenarios, significantly outperforming models trained from scratch or pretrained on smaller datasets. This underscores the effectiveness of large-scale self-supervised pretraining in archaeological remote sensing.
Guo, Junlin [Vanderbilt Univ., Nashville, TN (Unit↗
The planet Mars - From antiquity to the present
Telescopic observations of Mars, from those of Galileo in 1610 to the late 19th century, were summarized by Flammarion (1892, 1909). Major compilations of knowledge of Mars were produced by Antoniadi (1930) and de Vaucouleurs (1954). Polar cap composition was debated until the discovery by the Viking mission that the north and south perennial polar caps are composed of different materials. Spacecraft data indicate a rich and diverse geologic history as well as many unsolved puzzles. An annotated list of books about Mars is provided. Basic physical and chemical data on Mars are summarized in tabular form, and a guide to the Martian seasons is given.
Kieffer, Hugh H.↗
Synthetic data-driven deep learning for label-free autonomous atomic force microscopy
Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.
Millan-Solsona, Ruben [Oak Ridge National Laborato↗
Addressing the pervasive scarcity of structural annotation in eukaryotic algae
Abstract Despite a continuous increase in algal genome sequencing, structural annotations of most algal genome assemblies remain unavailable. This pervasive scarcity of genome annotation has restricted rigorous investigation of these genomic resources and may have precipitated misleading biological interpretations. However, the annotation process for eukaryotic algal species is often challenging as genomic resources and transcriptomic evidence are not always available. To address this challenge, we benchmark the cutting-edge gene prediction methods that can be generalized for a broad range of non-model eukaryotes. Using the most accurate methods selected based on high-quality algal genomes, we predict structural annotations for 135 unannotated algal genomes. Using previously available genomic data pooled together with new data obtained in this study, we identified the core orthologous genes and the multi-gene phylogeny of eukaryotic algae, including of previously unexplored algal species. This study not only provides a benchmark for the use of structural annotation methods on a variety of non-model eukaryotes, but also compensates for missing data in the current spectrum of algal genomic resources. These results bring us one step closer to the full potential of eukaryotic algal genomics.
59 BASIC BIOLOGICAL SCIENCES↗
Data and scripts associated with a manuscript modeling microbial regulation of priming effects
This data package is associated with the publication “Modeling Microbial Regulatory Feedback in Organic Matter Decomposition Identifies Copiotrophic Traits as Key Drivers of Positive Priming” published as a preprint on BioRXiv by Ahamed et al. (2026); https://doi.org/10.1101/2024.08.11.607483. The package contains MATLAB scripts and saved simulation outputs used to implement a cybernetic model of microbial regulation during complex organic matter (OM) decomposition governing priming effects. It includes models of (i) single microbial functional groups (copiotrophic or oligotrophic degraders) and (ii) binary consortia composed of degraders and non-degraders with contrasting or common growth traits. Simulation results were generated using Monte Carlo analyses, with randomized key model parameters across a range of environmental mixing fractions of complex and labile OM. The dataset was created to provide a transparent and reusable computational framework for systematically exploring how microbial growth traits, metabolic regulation, and community composition influence OM decomposition dynamics and priming effects. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes the variable definitions. This package includes: (1) annotated MATLAB code implementing the system of ordinary differential equations and cybernetic control laws; (2) saved output files containing data (e.g., biomass, substrates, enzyme levels, priming metrics); and (3) scripts for processing saved outputs and regenerating figures. Specifically, the data package contains three main MATLAB scripts: runPrimingModel.m, runPlotData.m, and runPlotSuppFigS1.m, along with this readme and supporting documentation. Users should begin with runPrimingModel.m, which contains the annotated code implementing the system of ordinary differential equations and cybernetic control laws. This script runs the Monte Carlo simulations of microbial OM decomposition and allows users to modify microbial trait definitions, adjust parameter distributions, or define new community configurations. Simulation outputs are automatically saved as .mat files in the folder named SavedData, which stores all pre-generated results included in this package. The second script, runPlotData.m, reads files from the SavedData folder and processes them to regenerate the figures presented in the manuscript. The third script, runPlotSuppFigS1.m, specifically generates Figure S1 in the Supplementary Material of the manuscript. The package also includes the aforementioned files in non-proprietary .txt format. If users intend to use them, they should first save the files in their respective .m or .mat formats prior to execution in MATLAB.
Biomass concentration↗
Programming distributed memory architectures using Kali
Programming nonshared memory systems is more difficult than programming shared memory systems, in part because of the relatively low level of current programming environments for such machines. A new programming environment is presented, Kali, which provides a global name space and allows direct access to remote data values. In order to retain efficiency, Kali provides a system on annotations, allowing the user to control those aspects of the program critical to performance, such as data distribution and load balancing. The primitives and constructs provided by the language is described, and some of the issues raised in translating a Kali program for execution on distributed memory systems are also discussed.
Mehrotra, Piyush↗
Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images
Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ~80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. Further, we show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.
60 APPLIED LIFE SCIENCES↗
Scene correction (precision techniques) of ERTS sensor data using digital image processing techniques
Techniques have been developed, implemented, and evaluated to process ERTS Return Beam Vidicon (RBV) and Multispectral Scanner (MSS) sensor data using digital image processing techniques. The RBV radiometry has been corrected to remove shading effects, and the MSS geometry and radiometry have been corrected to remove internal and external radiometric and geometric errors. The results achieved show that geometric mapping accuracy of about one picture element RMS and two picture elements (maximum) can be achieved by the use of nine ground control points. Radiometric correction of MSS and RBV sensor data has been performed to eliminate striping and shading effects to about one count accuracy. Image processing times on general purpose computers of the IBM 370/145 to 168 class are in the range of 29 to 3.2 minutes per MSS scene (4 bands). Photographic images of the fully corrected and annotated scenes have been generated from the processed data and have demonstrated excellent quality and information extraction potential.
Bernstein, R.↗
Transcriptomics Processing Pipelines for Space Biology: An Open Source and Consensus-Driven Approach
Transcriptomics holds significant value in elucidating the relationship between gene expression, experimental factors, biological factors, and various types of omics data. Enhancing our understanding of these connections is paramount for foundational biology, which plays a pivotal role in devising solutions for challenges pertinent to both space travel and terrestrial life. The NASA GeneLab project, part of the Open Science Data Repository (OSDR.nasa.gov), seeks to accelerate space biology research through cataloging and democratizing ‘omics data, including transcriptomics. Since raw omics data are largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community via the Open Science Analysis Working Groups (AWGs) to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data have greater immediate value to diverse users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. As of June 2023, transcriptomics studies comprise over half of GeneLab datasets hosted on the OSDR, including data from bulk RNA-seq and Affymetrix or Agilent 1-Channel DNA microarray assays. In collaboration with the AWGs, GeneLab developed consensus processing pipelines for these transcriptomics data types that includes quality control, background correction (microarray only), data normalization and quantification, culminating in the detection and annotation of differentially expressed genes. The work presented here describes Nextflow implementations of GeneLab’s consensus transcriptomics pipelines that automates and accelerates processing of these datasets. In addition to the core data processing, these workflows also include raw data staging and a robust verification and validation program to identify errors in real-time, stop additional downstream computation, and preserve computational resources. These workflows are used to generate GeneLab processed data hosted on the OSDR, and are publicly available as open source software for others to use at: https://github.com/nasa/GeneLab_Data_Processing.
Jonathan Oribello↗
Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits
Abstract The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein–protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.
Genetics & Heredity↗
Tag you're it: Application of stable isotope labeling and LC-MS to identify the precursors of specialized metabolites in plants
Untargeted liquid chromatography/mass spectrometry (LC-MS) can contribute a comprehensive and unbiased picture of the metabolic space of plants. These data can be used to quantify natural metabolite variation for genome wide association studies, to compare global metabolic responses from environmental or genetic perturbations, and to identify previously undescribed metabolites in Nature. A major limitation with untargeted metabolomics is the classification and identification of the thousands of metabolite features that can be detected in a single analytical run. Isotopic labeling improves the informational value of these datasets by categorizing metabolites as being derived from specific upstream precursors and/or to known metabolic pathways. When a 13 C-labeled precursor is fed to either a plant or tissue, the downstream metabolites produced from it have a higher m/z value than the molecules in the pre-existing pool, generating an m/z peak pair that can be specifically identified within the MS data. In this paper, we outline methods and principles to consider when supplementing untargeted MS data with isotopic labeling, including how to choose the appropriate isotopic label, grow and feed plant tissues to maximize label uptake and incorporation into derivatives, optimize LC-MS methods, and interpret the resulting labeling data. Although the focus here is on annotation of amino acid-derived metabolites using LC-MS, we anticipate that the methods are generally adaptable to other precursors, plant species, and chromatographic approaches.
59 BASIC BIOLOGICAL SCIENCES↗
Mycoparasites, Gut Dwellers, and Saprotrophs: Phylogenomic Reconstructions and Comparative Analyses of Kickxellomycotina Fungi
Improved sequencing technologies have profoundly altered global views of fungal diversity and evolution. High-throughput sequencing methods are critical for studying fungi due to the cryptic, symbiotic nature of many species, particularly those that are difficult to culture. However, the low coverage genome sequencing (LCGS) approach to phylogenomic inference has not been widely applied to fungi. Here we analyzed 171 Kickxellomycotina fungi using LCGS methods to obtain hundreds of marker genes for robust phylogenomic reconstruction. Additionally, we mined our LCGS data for a set of nine rDNA and protein coding genes to enable analyses across species for which no LCGS data were obtained. The main goals of this study were to: 1) evaluate the quality and utility of LCGS data for both phylogenetic reconstruction and functional annotation, 2) test relationships among clades of Kickxellomycotina, and 3) perform comparative functional analyses between clades to gain insight into putative trophic modes. In opposition to previous studies, our nine-gene analyses support two clades of arthropod gut dwelling species and suggest a possible single evolutionary event leading to this symbiotic lifestyle. Furthermore, we resolve the mycoparasitic Dimargaritales as the earliest diverging clade in the subphylum and find four major clades of Coemansia species. Finally, functional analyses illustrate clear variation in predicted carbohydrate active enzymes and secondary metabolites (SM) based on ecology, that is biotroph versus saprotroph. Saprotrophic Kickxellales broadly lack many known pectinase families compared with saprotrophic Mucoromycota and are depauperate for SM but have similar numbers of predicted chitinases as mycoparasitic.
59 BASIC BIOLOGICAL SCIENCES↗