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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.

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At least 109 records · Page 6

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO)

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

59 BASIC BIOLOGICAL SCIENCES↗

Prescreening-Based Subset Selection for Improving Predictions of Earth System Models With Application to Regional Prediction of Red Tide

We present the ensemble method of prescreening-based subset selection to improve ensemble predictions of Earth system models (ESMs). In the prescreening step, the independent ensemble members are categorized based on their ability to reproduce physically-interpretable features of interest that are regional and problem-specific. The ensemble size is then updated by selecting the subsets that improve the performance of the ensemble prediction using decision relevant metrics. We apply the method to improve the prediction of red tide along the West Florida Shelf in the Gulf of Mexico, which affects coastal water quality and has substantial environmental and socioeconomic impacts on the State of Florida. Red tide is a common name for harmful algal blooms that occur worldwide, which result from large concentrations of aquatic microorganisms, such as dinoflagellate Karenia brevis, a toxic single celled protist. We present ensemble method for improving red tide prediction using the high resolution ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and reanalysis data. The study results highlight the importance of prescreening-based subset selection with decision relevant metrics in identifying non-representative models, understanding their impact on ensemble prediction, and improving the ensemble prediction. These findings are pertinent to other regional environmental management applications and climate services. Additionally, our analysis follows the FAIR Guiding Principles for scientific data management and stewardship such that data and analysis tools are findable, accessible, interoperable, and reusable. As such, the interactive Colab notebooks developed for data analysis are annotated in the paper. This allows for efficient and transparent testing of the results’ sensitivity to different modeling assumptions. Moreover, this research serves as a starting point to build upon for red tide management, using the publicly available CMIP, Coordinated Regional Downscaling Experiment (CORDEX), and reanalysis data.

54 ENVIRONMENTAL SCIENCES↗

Accuracy-Based Annotation Quality Score (ABAQS) v1.0

Assessing genome annotation quality is crucial for downstream analyses, but current methods are inadequate for eukaryotes. We present Accuracy-Based Annotation Quality Score (ABAQS), a novel, minimal-data-driven method that comprehensively assesses annotation quality. ABAQS evaluates multiple factors, including genome completeness, gene model validity, and protein profile accuracy, outperforming other metrics like BUSCO and PSAURON. We applied ABAQS to over 2500 eukaryotic genomes and showed its robustness and effectiveness in evaluating genome annotation quality, making it a valuable tool for researchers working with genomic data. ABAQS reveals significant variation in annotation quality and highlights the importance of filtering in improving annotation quality and accuracy.

Haridas, Sajeet [Lawrence Berkeley National Labora↗

The landscape of regulatory element evolution in a C4 perennial grass

Gene regulatory evolution is a well-known source of phenotypic diversity and adaptive evolution. Although cis-regulatory elements (CREs) play a vital role in gene expression evolution, the molecular evolution of CREs remains mostly unknown due to the difficulty in identifying and characterizing these functional elements. Comparative genomic analyses of noncoding DNA can be leveraged to identify conserved noncoding sequences (CNS), many of which may harbor functional CREs conserved by purifying selection. However, purely computational inference of CREs from putative CNS can be erroneous due to the complex genomic architecture in plants. One promising experimental approach to identify CREs is by profiling accessible chromatin regions (ACRs) that are often associated with the location of CREs. In this study, we use comparative genomics along with the profiling of ACRs to study the molecular evolution of putative functional noncoding regulatory regions in Panicoid grasses. We identified sets of CNS that varied in relationship to the degree of evolutionary divergence among the studied taxa, including identifying core-Panicoid-CNS. We augmented this analysis by profiling ACRs in Panicum hallii ecotypes using ATAC-seq. ACRs had low SNP density at the summit, harbored a high frequency of core-Panicoid-CNS, and were enriched with expression QTL. These data help to annotate the P. hallii genome for putative functional elements and suggest that a large proportion of these ACRs are evolving under purifying selection. Turnover in CNS and ACR between ecotypes of P. hallii identifies a small set of putatively divergent CREs that may underlie differences in gene regulation between genotypes from inland and coastal habitats. In summary, we profiled ACRs in Panicoid grasses and integrated this data with our putative CNS prediction framework, which provides unique insight into patterns of polymorphism and divergence in CREs in C4 perennial grasses.

59 BASIC BIOLOGICAL SCIENCES↗

RCSB Protein Data Bank: supporting research and education worldwide through explorations of experimentally determined and computationally predicted atomic level 3D biostructures

The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules ( e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.

59 BASIC BIOLOGICAL SCIENCES↗

MassIVE MSV000087434 - Raw data files for NetID manuscript

This dataset is associated with this manuscript: Metabolite discovery through global annotation of untargeted metabolomics data. Li Chen, Wenyun Lu, Lin Wang, Xi Xing, Xin Teng, Xianfeng Zeng, Antonio D Muscarella, Yihui Shen, Alexis Cowan, Melanie R McReynolds, Brandon Kennedy, Ashley M Lato, Shawn R Campagna, Mona Singh, Joshua Rabinowitz. The dataset has six main folders: (1) Yeast-MS1 (2) Yeast-labeling (3) Liver-MS1 (4) Liver-targeted-MS2 (separate folders for mzxml files and matched csv files used as the inclusion list to set up PRM targeted-MS2 runs) (5) Mouse-labeling (6) Glucosyl_taurine-liver-vs-synthesis. Please refer to README-raw-data-NetID-20210512.doc for more details. [doi:10.25345/C5WV53] [dataset license: CC0 1.0 Universal (CC0 1.0)]

full scan MS1↗

Pickaxe: a Python library for the prediction of novel metabolic reactions

Abstract Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

59 BASIC BIOLOGICAL SCIENCES↗

DOE JGI Metagenome Workflow

The DOE Joint Genome Institute (JGI) Metagenome Workflow performs metagenome data processing, including assembly; structural, functional, and taxonomic annotation; and binning of metagenomic data sets that are subsequently included into the Integrated Microbial Genomes and Microbiomes (IMG/M) (I.-M. A. Chen, K. Chu, K. Palaniappan, A. Ratner, et al., Nucleic Acids Res, 49:D751–D763, 2021, https://doi.org/10.1093/nar/gkaa939) comparative analysis system and provided for download via the JGI data portal (https://genome.jgi.doe.gov/portal/). This workflow scales to run on thousands of metagenome samples per year, which can vary by the complexity of microbial communities and sequencing depth. Here, we describe the different tools, databases, and parameters used at different steps of the workflow to help with the interpretation of metagenome data available in IMG and to enable researchers to apply this workflow to their own data. We use 20 publicly available sediment metagenomes to illustrate the computing requirements for the different steps and highlight the typical results of data processing. The workflow modules for read filtering and metagenome assembly are available as a workflow description language (WDL) file (https://code.jgi.doe.gov/BFoster/jgi_meta_wdl). The workflow modules for annotation and binning are provided as a service to the user community at https://img.jgi.doe.gov/submit and require filling out the project and associated metadata descriptions in the Genomes OnLine Database (GOLD) (S. Mukherjee, D. Stamatis, J. Bertsch, G. Ovchinnikova, et al., Nucleic Acids Res, 49:D723–D733, 2021, https://doi.org/10.1093/nar/gkaa983).

59 BASIC BIOLOGICAL SCIENCES↗

Implementation of FAIR principles in the IPCC: the WGI AR6 Atlas repository

The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has adopted the FAIR Guiding Principles. We present the Atlas chapter of Working Group I (WGI) as a test case. We describe the application of the FAIR principles in the Atlas, the challenges faced during its implementation, and those that remain for the future. We introduce the open source repository resulting from this process, including coding (e.g., annotated Jupyter notebooks), data provenance, and some aggregated datasets used in some figures in the Atlas chapter and its interactive companion (the Interactive Atlas), open to scrutiny by the scientific community and the general public. We describe the informal pilot review conducted on this repository to gather recommendations that led to significant improvements. Finally, a working example illustrates the re-use of the repository resources to produce customized regional information, extending the Interactive Atlas products and running the code interactively in a web browser using Jupyter notebooks.

54 ENVIRONMENTAL SCIENCES↗

Thaumarchaea Genome Sequences from a High Arctic Active Layer

The role of archaeal ammonia oxidizers often exceeds that of bacterial ammonia oxidizers in marine and terrestrial environments but has been understudied in permafrost, where thawing has the potential to release ammonia. Here, three thaumarchaea genomes were assembled and annotated from metagenomic data sets from carbon-poor Canadian High Arctic active-layer cryosols.

Sun, Emily Wei-Hsin↗

Addressing the dynamic nature of reference data: a new nucleotide database for robust metagenomic classification

Accurate metagenomic classification relies on comprehensive, up-to-date, and validated reference databases. While the NCBI BLAST Nucleotide (nt) database, encompassing a vast collection of sequences from all domains of life, represents an invaluable resource, its massive size—currently exceeding 10 12 nucleotides—and exponential growth pose significant challenges for researchers seeking to maintain current nt-based indices for metagenomic classification. Recognizing that no current nt-based indices exist for the widely used Centrifuge classifier, and the last public version currently available was released in 2018, we addressed this critical gap by leveraging advanced high-performance computing resources. We present new Centrifuge-compatible nt databases, meticulously constructed using a novel pipeline incorporating different quality control measures, including reference decontamination and filtering. These measures demonstrably reduce spurious classifications, as shown through our reanalysis of published metagenomic data where Plasmodium annotations were dramatically reduced using our decontaminated database, highlighting how database quality can significantly impact research conclusions. Through temporal comparisons, we also reveal how our approach minimizes inconsistencies in taxonomic assignments stemming from asynchronous updates between public sequence and taxonomy databases. These discrepancies are particularly evident in taxa such as Listeria monocytogenes and Naegleria fowleri, where classification accuracy varied significantly across database versions. These new databases, made available as pre-built Centrifuge indexes, respond to the need for an open, robust, nt-based pipeline for taxonomic classification in metagenomics. Applications such as environmental metagenomics, forensics, and clinical metagenomics, which require comprehensive taxonomic coverage, will benefit from this resource. Our work highlights the importance of treating reference databases as dynamic entities, subject to ongoing quality control and validation akin to software development best practices. This approach is crucial for ensuring accuracy and reliability of metagenomic analysis, especially as databases continue to expand in size and complexity.

59 BASIC BIOLOGICAL SCIENCES↗

Database of virus genomes from ultra-deep sequencing of wastewater

Researchers at University of Missouri have conducted ultra-deep RNA sequencing of viral concentrates from wastewater (1 billion Illumina reads per sample). The resulting dataset spans 321 samples collected weekly from 11 cities between 2023-2025. As part of a tri-lab collaboration, scientists at LLNL and LANL cleaned, assembled, and annotated this metagenomic data, identifying nearly 200,000 viral genomes. Careful data curation resulted in a database containing 21,015 high-quality, near-complete viral genomes from wastewater. This database contains viruses predicted to infect a range of hosts including bacteria (most common viruses), plants (most abundant viruses), and vertebrates (rarest viruses). There are also numerous novel viruses that could not be well identified and whose host(s) are unknown. Just 7% of all genomes in the wastewater virus database had genus-level matches in the public NCBI database, and 17% matched to a recently created metagenomic virus database at that level (metaVR). The database will provide baseline information about viruses in wastewater that may be used to additional identify novel viruses during ongoing monitoring

Allen, Jonathan [Lawrence Livermore National Labor↗

A spectroradiometer for field use.

Description of a spectroradiometer system that measures the intensity of incident and reflected radiation from 0.37 to 2.52 microns and emitted radiation from 2.76 to 13.88 microns. The system is designed to operate in an outdoor environment. Provisions exist for photographic annotation of field data setups.

Leamer, R. W.↗

Efforts and Innovations to Promote Data Sharing and Data Accessibility in the EGS Collab Experiments

Large-scale scientific research programs such as the EGS Collab experiments and the FORGE program play extremely important roles in advancing geothermal technologies. Such efforts involve a large number of researchers from multiple institutions, last multiple years, and generate large, complex datasets. The value of the research efforts is only realized when the datasets are used by a large community of researchers in the decades to come. A challenge is that due to the complexity of the data, it could require a user to devote a serious effort into understanding the data before the data can be effectively utilized. In the EGS Collab experiments, the team has devoted remarkable efforts and developed many innovative solutions to make the data more accessible to broader team members and future users. This paper documents the experience gained and lessons learned by the EGS Collab team in disseminating the data in the most informative and inspiring forms to maximize the value of this precious dataset. "Accessibility"in the title does not only mean making the raw data available for download. Particularly, we want to emphasize the importance of organizing, annotating, and presenting the data in ways to make it easy to digest by prospective consumers of the data.

circulation↗

BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA

Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes, and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-seq or protein data, respectively, but not both. A further significant performance improvement integrating all three data types was made by the recently released GeneMark-ETP. We here present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS, and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under an assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperforms BRAKER1 and BRAKER2. The average transcript-level F1-score is increased by about 20 percentage points on average, whereas the difference is most pronounced for species with large and complex genomes. BRAKER3 also outperforms other existing tools, MAKER2, Funannotate, and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.

59 BASIC BIOLOGICAL SCIENCES↗

Visualizing and analyzing 3D biomolecular structures using Mol* at RCSB.org: Influenza A H5N1 virus proteome case study

The easiest and often most useful way to work with experimentally determined or computationally predicted structures of biomolecules is by viewing their three-dimensional (3D) shapes using a molecular visualization tool. Mol* was collaboratively developed by RCSB Protein Data Bank (RCSB PDB, RCSB.org) and Protein Data Bank in Europe (PDBe, PDBe.org) as an open-source, web-based, 3D visualization software suite for examination and analyses of biostructures. It is capable of displaying atomic coordinates and related experimental data of biomolecular structures together with a variety of annotations, facilitating basic and applied research, training, education, and information dissemination. Across RCSB.org, the RCSB PDB research-focused web portal, Mol* has been implemented to support single-mouse-click atomic-level visualization of biomolecules (e.g., proteins, nucleic acids, carbohydrates) with bound cofactors, small-molecule ligands, ions, water molecules, or other macromolecules. RCSB.org Mol* can seamlessly display 3D structures from various sources, allowing structure interrogation, superimposition, and comparison. Using influenza A H5N1 virus as a topical case study of an important pathogen, we exemplify how Mol* has been embedded within various RCSB.org tools—allowing users to view polymer sequence and structure-based annotations integrated from trusted bioinformatics data resources, assess patterns and trends in groups of structures, and view structures of any size and compositional complexity. In addition to being linked to every experimentally determined biostructure and Computed Structure Model made available at RCSB.org, Standalone Mol* is freely available for visualizing any atomic-level or multi-scale biostructure at rcsb.org/3d-view.

3D biostructure↗

NASA biological and physical sciences databases: who’s the FAIRest of them all?

Conceptual models are a key part of the foundation of scientific study. Scientific data discovery and retrieval are often inaccurate and incomplete because these models are not sufficiently well-incorporated into data retrieval systems. Systems often don’t provide the necessary tools to those producing scientific data to fully and unambiguously annotate them and the result is consumers of the data cannot find them efficiently. The capability of data archives to provide these tools to link data to underlying conceptual models is one of dimensions of the recently developed “FAIR” principles (https://www.go-fair.org/fair-principles/ ), and is key to many automated processes being able to operate on these data, particularly analytics involving artificial intelligence. We used an open-source web service to measure the FAIR compliance of the three data archives operated by NASA for the biological and physical sciences: the Life Sciences Data Archive, the Physical Sciences Informatics database, and GeneLab. The service ingests references to data sets in these archives, and then executes domain-non-specific examinations of these data and metadata that test compliance to the FAIR principles. Of the 22 metrics tested, GeneLab passed 11 (50%), and PSI and LSDA each passed 7 (32%). These data were gathered using only one representative data set from each archive and we anticipate variability in results as we continue to apply these metrics to other data. A preliminary study of the failure traces for each metric suggests there is a wide range of effort and complexity in the enhancements required for each system to elevate FAIR compliance, and this is the subject of continued investigation. This information has been and will likely continue to be important information in planning these enhancements, with the goal of increased readiness of the data for automated processes.

database↗