Search NASA⌕ Search

SEARCH · Search NASA

Results for “annotated data”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

ROI-Finder : machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy

The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities. The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli ( E. Coli ), also serve as label-free biological fingerprints to identify E. coli cells that have been treated differently. The key limitations of achieving good identification performance are the extraction of cells from raw XRF measurements via binary conversion, definition of features, noise floor and proportion of cells treated differently in the measurement. Automating cell extraction from raw XRF measurements across different types of chemical treatment and the implementation of machine-learning models to distinguish cells from the background and their differing treatments are described. Principal components are calculated from domain knowledge specific features and clustered to distinguish healthy and poisoned cells from the background without manual annotation. The cells are ranked via fuzzy clustering to recommend regions of interest for automated experimentation. The effects of dwell time and the amount of data required on the usability of the software are also discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

DiMER

SAND2025-04145O DiMER is a Python based tool that helps researchers understand the functions of genes by searching through multiple biological databases. It takes user-provided data and scans various databases to find the best matches for gene functions, generating a clear summary of results. DiMER identifies the most relevant functional annotations and improves upon previous annotations by replacing instances of "unknown protein function" with more accurate descriptions. DiMER requires minimal setup. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Mageeney, Catherine [Sandia National Lab. (SNL-CA)↗
DOE OSTI2023

Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography–Mass Spectrometry Small Molecule Identification

Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this “molecular snapshot” is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1974

The application of large numbers of pleasure boats to collect synoptic sea-truth for ERTS-1 overpasses

The author has identified the following significant results. In order to interpret and annotate current circulation and suspended sediment concentration maps derived from ERTS-1 digital tapes, the University of Delaware has been collecting water samples and other data from boats and helicopters. In order to increase the number of samples at the exact time of the ERTS-1 pass over Delaware Bay, pleasure craft were organized to obtain samples of the entire test site. On the ERTS-1 pass of July second, scientists were stationed at three public boat launches along the Bay to hand out sampling packets to interested boaters. The packets contained two litre sampling bottles, a map, data card, and a pen. The boaters were asked to fill the two bottles between 11 and 11:15 a.m., mark their location on the map, and fill out the data card. Forty-nine packets were handed out of which 40 were returned (82%). Only four of the 40 were not in the alloted time range. This gave 36 real time data points covering approximately 30 nautical miles. The samples are being analyzed for sediment concentration, particle size, and salinity. Participating boaters will receive a copy of an ERTS image of the Delaware Bay and a summary report of the project. Because of the success of the project, future use of pleasure boaters is being planned.

Klemas, V.↗
NASA NTRS1981

The graphics and data acquisition software package

A software package was developed for use with micro and minicomputers, particularly the LSI-11/DPD-11 series. The package has a number of Fortran-callable subroutines which perform a variety of frequently needed tasks for biomedical applications. All routines are well documented, flexible, easy to use and modify, and require minimal programmer knowledge of peripheral hardware. The package is also economical of memory and CPU time. A single subroutine call can perform any one of the following functions: (1) plot an array of integer values from sampled A/D data, (2) plot an array of Y values versus an array of X values; (3) draw horizontal and/or vertical grid lines of selectable type; (4) annotate grid lines with user units; (5) get coordinates of user controlled crosshairs from the terminal for interactive graphics; (6) sample any analog channel with program selectable gain; (7) wait a specified time interval, and (8) perform random access I/O of one or more blocks of a sequential disk file. Several miscellaneous functions are also provided.

Crosier, W. G.↗
DOE OSTI2020

CYPminer: an automated cytochrome P450 identification, classification, and data analysis tool for genome data sets across kingdoms

Background: Cytochrome P450 monooxygenases (termed CYPs or P450s) are hemoproteins ubiquitously found across all kingdoms, playing a central role in intracellular metabolism, especially in metabolism of drugs and xenobiotics. The explosive growth of genome sequencing brings a new set of challenges and issues for researchers, such as a systematic investigation of CYPs across all kingdoms in terms of identification, classification, and pan-CYPome analyses. Such investigation requires an automated tool that can handle an enormous amount of sequencing data in a timely manner. Results: CYPminer was developed in the Python language to facilitate rapid, comprehensive analysis of CYPs from genomes of all kingdoms. CYPminer consists of two procedures i) to generate the Genome-CYP Matrix (GCM) that lists all occurrences of CYPs across the genomes, and ii) to perform analyses and visualization of the GCM, including pan-CYPomes (pan- and core-CYPome), CYP co-occurrence networks, CYP clouds, and genome clustering data. The performance of CYPminer was evaluated with three datasets from fungal and bacterial genome sequences. Conclusions: CYPminer completes CYP analyses for large-scale genomes from all kingdoms, which allows systematic genome annotation and comparative insights for CYPs. CYPminer also can be extended and adapted easily for broader usage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing

Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to sequencing technology limitations. Long-read sequencing enables comprehensive detection of SVs, but approaches for prioritization of candidate SVs are needed. Structural variant Annotation and analysis (SvAnna) assesses all classes of SVs and their intersection with transcripts and regulatory sequences, relating predicted effects on gene function with clinical phenotype data. SvAnna places 87% of deleterious SVs in the top ten ranks. The interpretable prioritizations offered by SvAnna will facilitate the widespread adoption of long-read sequencing in diagnostic genomics. SvAnna is available at https://github.com/TheJacksonLaboratory/SvAnna.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

SEGUID v2: Extending SEGUID checksums for circular, linear, single- and double-stranded biological sequences

Background Synthetic biology involves combining different DNA fragments, each containing functional biological parts, to address specific problems. Fundamental gene-function research often requires cloning and propagating DNA fragments, such as those from the iGEM Parts Registry or Addgene, typically distributed as circular plasmids. Addgene’s repository alone offers around 150,000 plasmids. To ensure data integrity, cryptographic checksums can be calculated for the sequences. Each sequence has a unique checksum, making checksums useful for validation and quick lookups of associated annotations. For example, the SEGUID checksum uniquely identifies protein sequences with a 27-character string. Objectives The original SEGUID, while effective for protein sequences and single-stranded DNA (ssDNA), is not suitable for circular DNA since there is no natural starting position nor for double-stranded DNA (dsDNA) since two separate sequences are present. Challenges include how to uniquely represent linear dsDNA, circular ssDNA, and circular dsDNA. To meet these needs, we propose SEGUID v2, which extends the original SEGUID to handle additional types of sequences. Conclusions SEGUID v2 produces orientation and rotation invariant checksums for single-stranded, double-stranded, possibly staggered, linear, and circular DNA and RNA sequences. Customizable alphabets allow for other types of sequences. In contrast to the original SEGUID, which uses Base64, SEGUID v2 uses Base64url to encode the SHA-1 hash. This ensures SEGUID v2 checksums can be used as-is in filenames, regardless of platform, and in URLs, with minimal friction. Availability SEGUID v2 is readily available for major programming languages, distributed under the MIT license. JavaScript package seguid is available on npm, Python package seguid on PyPi, R package seguid on CRAN, and a Tcl script on GitHub. These tools, along with documentation, examples, and an online SEGUID Calculator , can be found at https://www.seguid.org .

Pereira, Humberto↗
NASA NTRS2004

Using Block-local Atomicity to Detect Stale-value Concurrency Errors

Data races do not cover all kinds of concurrency errors. This paper presents a data-flow-based technique to find stale-value errors, which are not found by low-level and high-level data race algorithms. Stale values denote copies of shared data where the copy is no longer synchronized. The algorithm to detect such values works as a consistency check that does not require any assumptions or annotations of the program. It has been implemented as a static analysis in JNuke. The analysis is sound and requires only a single execution trace if implemented as a run-time checking algorithm. Being based on an analysis of Java bytecode, it encompasses the full program semantics, including arbitrarily complex expressions. Related techniques are more complex and more prone to over-reporting.

Artho, Cyrille↗
NASA NTRS1973

Earth Resources Technology Satellite: US standard catalog No. U-12

To provide dissemination of information regarding the availability of Earth Resources Technology Satellite (ERTS) imagery, a U.S. Standard Catalog is published on a monthly schedule. The catalogs identify imagery which has been processed and input to the data files during the preceding month. The U.S. Standard Catalog includes imagery covering the Continental United States, Alaska, and Hawaii. As a supplement to these catalogs, an inventory of ERTS imagery on 16 millimeter microfilm is available. The catalogs consist of four parts: (1) annotated maps which graphically depict the geographic areas covered by the imagery listed in the current catalog, (2) a computer-generated listing organized by observation identification number (D) with pertinent information on each image, (3) a computer listing of observations organized by longitude and latitude, and (4) observations which have had changes made in their catalog information since the original entry in the data base.

Source record↗
DOE OSTI2022

RCSB Protein Data Bank 1D3D module: displaying positional features on macromolecular assemblies

Mapping positional features from one-dimensional (1D) sequences onto three-dimensional (3D) structures of biological macromolecules is a powerful tool to show geometric patterns of biochemical annotations and provide a better understanding of the mechanisms underpinning protein and nucleic acid function at the atomic level. We present a new library designed to display fully customizable interactive views between 1D positional features of protein and/or nucleic acid sequences and their 3D structures as isolated chains or components of macromolecular assemblies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

PubChemLite Plus Collision Cross Section (CCS) Values for Enhanced Interpretation of Nontarget Environmental Data

Finding relevant chemicals in the vast (known) chemical space is a major challenge for environmental and exposomics studies leveraging nontarget high resolution mass spectrometry (NT-HRMS) methods. Chemical databases now contain hundreds of millions of chemicals, yet many are not relevant. This article details an extensive collaborative, open science effort to provide a dynamic collection of chemicals for environmental, metabolomics, and exposomics research, along with supporting information about their relevance to assist researchers in the interpretation of candidate hits. The PubChemLite for Exposomics collection is compiled from ten annotation categories within PubChem, enhanced with patent, literature and annotation counts, predicted partition coefficient (logP) values, as well as predicted collision cross section (CCS) values using CCSbase. Monthly versions are archived on Zenodo under a CC-BY license, supporting reproducible research, and a new interface has been developed, including historical trends of patent and literature data, for researchers to browse the collection. This article details how PubChemLite can support researchers in environmental and exposomics studies, describes efforts to increase the availability of experimental CCS values, and explores known limitations and potential for future developments. The data and code behind these efforts are openly available.

PubChem↗
DOE OSTI2026

A Step-by-Step Protocol from METASPACE to Biological Interpretation

Mass spectrometry imaging (MSI) represents an exceptional tool for exploring complex biological systems spatially at the molecular level. However, due to its multidimensional nature and large-scale data output, it presents considerable challenges when it comes to extracting meaningful biological insights. Recent advancements, such as the METASPACE platform, have enabled researchers to efficiently process, annotate, and interpret MSI datasets by leveraging machine learning and cloud-based infrastructure. In this tutorial, we present a detailed and user-friendly R-pipeline designed to help METASPACE users navigate untargeted metabolomic annotations and transform them into practical insights about their biological systems. By combining METASPACE annotations with rapid R-based screening, this workflow not only streamlined the analytical process but also enhanced the understanding of spatial molecular distribution, especially for complex systems. Here, this easy-to-follow approach has the potential for applications in diagnostics, drug discovery, environmental and ecological processes, and more. We envision this pipeline to be particularly useful for newcomers to the field of MSI and

Moreno Pedraza, Abigail↗
DOE OSTI2020

Perimatrix of middle ear cholesteatoma: A granulation tissue with a specific transcriptomic signature

Objectives/Hypothesis: To establish comprehensive transcriptomic profiles of cholesteatoma perimatrix tissue and granulation tissue from chronic otitis media (COM) that did not develop cholesteatoma, which can indicate molecular pathways involved in the cholesteatoma perimatrix pathology and invasiveness. Study Design: Retrospective Case Series. Methods: Transcriptome data were obtained from cholesteatoma perimatrix tissue and COM granulation tissue by an Illumina iScan microarray. Differentially expressed genes (DEGs) were subsequently analyzed using both bioinformatical functional annotation and network analysis. Expression of candidate genes (MMP9 and LCN2) was validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) on a larger group of samples. Results: Analysis of the transcriptome led to the identification of 169 differentially expressed genes between investigated tissues. Bioinformatic analysis suggested that most significant biological processes involving DEGs were previously described in cholesteatoma pathology. Network analysis identified ERBB2, TFAP2A, and TP63 as major hubs of the DEGs molecular network. Furthermore, it was observed that the cellular component most significantly enriched in DEGs was extracellular space containing 47 DEGs. Using qRT-PCR, it was confirmed that mRNA levels of the major extracellular hub (MMP9) are increased, whereas its interacting molecule (LCN2) mRNA levels were decreased in cholesteatoma perimatrix tissue compared to COM granulation tissue. Conclusions: The current study approach offers an overall look at molecular mechanisms that describe the cholesteatoma entity by focusing exclusively on the perimatrix processes in comparison to COM granulation tissue. The observed differences in gene expression between cholesteatoma perimatrix and COM granulation tissue could suggest novel markers potentially influenced by the perimatrix–matrix molecular interplay, which is not present in COM without cholesteatoma. Level of Evidence: NA. Laryngoscope, 130:E220–E227, 2020. © 2019 The American Laryngological, Rhinological and Otological Society, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Phosphate amendment drives bloom of RNA viruses after soil wet-up

Soil rewetting after a dry period results in a surge of activity and succession in both microbial and DNA virus communities. Less is known about the response of RNA viruses to soil rewetting—while they are highly diverse and widely distributed in soil, they remain understudied. We hypothesized that RNA viruses would show temporal succession following rewetting and that phosphate amendment would influence their trajectory, as viral proliferation may cause phosphorus limitation. Using 39 time-resolved metatranscriptomes and amplicon data, 2190 RNA viral populations were identified across five phyla, with 26 % of these predicted to infect bacteria, and 11 % fungi. Only 1.2 % of viral populations had annotated capsid genes, suggesting most persist via intracellular replication without a free virion phase. Phosphate amendment altered RNA viral community composition within the first week and amended vs. unamended communities remained distinguishable for up to three weeks. While the overall host community remained stable, certain bacterial populations showed reduced abundance in phosphate-amended soils, likely due to increased viral lysis, as RNA bacteriophages proliferated significantly. Notably, 60 % of the viruses with increased abundance under phosphate amendment belonged to basal Lenarviricota clades rather than well-known groups like Leviviricetes. We estimate RNA bacteriophage infections may affect 10 7 –10 9 bacteria per gram of soil, aligning with the total bacterial population (10 7 –10 10 g -1 soil), suggesting that RNA phages significantly influence bacterial communities post-wet-up, with phosphorus availability modulating this effect.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Increasing the Scale of the Mass Spectrometry Query Language Compendium with Explainable AI

A significant bottleneck in metabolomics data interpretation is the effective use of domain knowledge to assign structural information based on fragmentation patterns. The mass spectrometry query language (MassQL) aims to make this process accessible and applicable across multiple analysis platforms. While advanced computational methods are capable of predicting compound structures from fragmentation data, AI/ML approaches often rely on complex, opaque criteria that are difficult to interpret or modify. As a result, their predictive patterns cannot be readily translated into human-readable rules, such as those used in MassQL. Here, in this study, we introduce ChemEcho, a machine learning embedding method that converts tandem mass spectrometry data into sparse feature vectors containing peak and neutral mass subformulae to enhance explainable AI/ML-based methods. An advantage of this approach is that decision trees trained using these feature vectors can be directly translated to MassQL. Using a battery of decision trees trained using ChemEcho embeddings to predict molecular attributes, we generated over 1500 MassQL queries for 765 molecular features and evaluated their precision and recall. From these queries, the 50 highest-performing queries were integrated into the MassQL compendium. This set of generated MassQL queries included environmentally and biologically relevant classes such as PFAS and molecules containing phosphate or sulfate substructures. To illustrate the impact these queries would have on a typical metabolomics experiment, these MassQL queries were applied to a public metabolomics data set─resulting in a marked increase in the structural information derived from tandem mass spectra. Access and reuse of these queries is expected to enhance structural annotation in untargeted experiments, leading to more specific claims and advancing many applications in metabolomics.

Harwood, Thomas V. [USDOE Joint Genome Institute (↗
DOE OSTI2021

Cognitive analysis of metabolomics data for systems biology

Cognitive computing is revolutionizing the way big data are processed and integrated, with artificial intelligence (AI) natural language processing (NLP) platforms helping researchers to efficiently search and digest the vast scientific literature. Most available platforms have been developed for biomedical researchers, but new NLP tools are emerging for biologists in other fields and an important example is metabolomics. NLP provides literature-based contextualization of metabolic features that decreases the time and expert-level subject knowledge required during the prioritization, identification and interpretation steps in the metabolomics data analysis pipeline. Here, we describe and demonstrate four workflows that combine metabolomics data with NLP-based literature searches of scientific databases to aid in the analysis of metabolomics data and their biological interpretation. Additionally, the four procedures can be used in isolation or consecutively, depending on the research questions. The first, used for initial metabolite annotation and prioritization, creates a list of metabolites that would be interesting for follow-up. The second workflow finds literature evidence of the activity of metabolites and metabolic pathways in governing the biological condition on a systems biology level. The third is used to identify candidate biomarkers, and the fourth looks for metabolic conditions or drug-repurposing targets that the two diseases have in common. The protocol can take 1–4 h or more to complete, depending on the processing time of the various software used.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

19-LW-045 Full Length Final Report. Molecular Mechanisms of Bacterial Pathogenesis: Waging the Arms Race with Superbugs

As the current global pandemic makes abundantly clear, we need a better understanding of infectious disease to safeguard human health, the economy and global security. Modern omics techniques hold the promise of providing a comprehensive understanding of the molecular mechanisms of life, including causes of pathogenesis from infectious disease at the molecular level, but we there is a serious gap in annotation of gene function. For as much as half of the genes and gene products encoded in genomes the molecular and/or cellular function is unknown or only partially understood. Recent innovations in fluorescence microscopy for live cell imaging and genetic engineering make it possible to determine the temporal correlation between molecular events, such as a gene being expressed due to host-pathogen interaction, and cellular events, such as bacterial invasion of immune cells. This is turn allows us to gain new insight as to the molecular and cellular role of individual genes and will enable the discovery and validation of new molecular mechanisms essential for infectious disease. Knowing the molecular mechanisms of disease processes will provide new therapeutic targets or novel countermeasure strategies. We aimed to develop a lattice light sheet fluorescence microscope as a unique resource at LLNL for long time course live cell imaging experiments; to develop the reagents and cell lines needed to monitor molecular events during the course pathogenic bacteria infecting mammalian immune cells; and to demonstrate that we could capture molecular events during an infection. We fully commissioned the LLNL lattice light sheet microscope and conducted initial proof of principle imaging experiments on mammalian immune cells and pathogenic bacteria. It is clear from the experience gained that long time course live cell imaging has tremendous potential to help elucidate molecular mechanisms of host-pathogen interactions and to help annotate gene function, which would establish a basis for new countermeasures. It is also clear that if live cell imaging is to realize its full potential new data processing and analysis tools will need to be developed to facilitate analysis of molecular events within cells; new sample chambers and stages could facilitate studies with a wider range of cell and tissue types; and alternative molecular tagging methods need to be explored to enable more facile engineering of cells labeled with molecular specificity.

59 BASIC BIOLOGICAL SCIENCES↗