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DOE OSTI2026

nf-core/proteinfamilies: a scalable pipeline for the generation of protein families

The growth of metagenomics-derived amino acid sequence data has transformed our understanding of protein function, microbial diversity, and evolutionary relationships. However, the vast majority of these proteins remain functionally uncharacterized. Grouping the millions of such uncharacterized sequences with the few experimentally characterized ones allows the transfer of annotations, while the inspection of conserved residues with multiple sequence alignments can provide clues to function, even in the absence of existing functional information. To address the challenges associated with this data surge and the need to group sequences, we present a scalable, open-source, parametrizable Nextflow pipeline (nf-core/proteinfamilies) that generates nascent protein families or assigns new proteins to existing families. The computational benchmarks demonstrated that resource usage scales approximately linearly with input size, and the biological benchmarks showed that the generated protein families closely resemble manually curated families in widely used databases.

Nextflow↗

DOE OSTI2025

DancePartner: Python Package to Mine Multiomics Relationship Networks from Literature and Databases

A goal of multi-omics experiments is to understand how mechanistic molecular biology is altered between conditions, typically a control group and experimental groups. Oftentimes this involves studying changes in biomolecule relationships (e.g. interactions, metabolic relationships) of several types of biomolecules (e.g. proteins, lipids, metabolites). Though several databases contain relationships between biomolecules, understudied species may have little to no relationship information in databases and thus must be mined from literature. There are several challenges to literature mining, including automated full-text extraction, duplicate biomolecule term collapsing, and implementing complex machine learning tools. To make relationship extraction more accessible to the community, a python package called DancePartner was developed to allow for the extraction of relationships from literature and databases, with functions to map biomolecule synonyms to standardized identifiers and visualize and characterize the resulting multi-omics network. Here, in this study, an example dataset involving Caenorhabditis elegans is presented, where relationships are mined from 1443 publications using DancePartner. These relationships are combined with relationships from KEGG, WikiPathways, UniProt, and LipidMaps, and visualized.

BERT↗

DOE OSTI2025

Pivotal trial characteristics and types of endpoints used to support Food and Drug Administration rare disease drug approvals between 2013 and 2022

Background/aims Rare disease drug development faces unique challenges, such as genotypic and phenotypic heterogeneity within small patient populations and a lack of established outcome measures for conditions without previously successful drug development programs. These challenges complicate the process of selecting the appropriate trial endpoints and conducting clinical trials in rare diseases. In this descriptive study, we examined novel drug approvals for non-oncologic rare diseases by the U.S. Food and Drug Administration’s Center for Drug Evaluation and Research over the past decade and characterized key regulatory and trial design elements with a focus on the primary efficacy endpoint utilized as the basis of approval. Methods Using the Food and Drug Administration’s Data Analysis Search Host database, we identified novel new drug applications and biologics license applications with orphan drug designation that were approved between 2013 and 2022 for non-oncologic indications. From Food and Drug Administration review documents and other external databases, we examined characteristics of pivotal trials for the included drugs, such as therapeutic area, trial design, and type of primary efficacy endpoints. Differences in trial design elements associated with primary efficacy endpoint type were assessed such as randomization and blinding. Then, we summarized the primary efficacy endpoint types utilized in pivotal trials by therapeutic area, approval pathway, and whether the disease etiology is well defined. Results One hundred and seven drugs that met our inclusion criteria were approved between 2013 and 2022. Assessment of the 107 drug development programs identified 150 pivotal trials that were subsequently analyzed. The pivotal trials were mostly randomized (80%) and blinded (69.3%). Biomarkers (41.1%) and clinical outcomes (42.1%) were commonly utilized as primary efficacy endpoints. Analysis of the use of clinical trial design elements across trials that utilized biomarkers, clinical outcomes, or composite endpoints did not reveal statistically significant differences. The choice of primary efficacy endpoint varied by the drug’s therapeutic area, approval pathway, and whether the indicated disease etiology was well defined. For example, biomarkers were commonly selected as primary efficacy endpoints in hematology drug approvals (70.6%), whereas clinical outcomes were commonly selected in neurology drug approvals (69.6%). Further, if the disease etiology was well defined, biomarkers were more commonly used as primary efficacy endpoints in pivotal trials (44.7%) than if the disease etiology was not well defined (27.3%). Discussion In the past 10 years, numerous novel drugs have been approved to treat non-oncologic rare diseases in various therapeutic areas. To demonstrate their efficacy for regulatory approval, biomarkers and clinical outcomes were commonly utilized as primary efficacy endpoints. Biomarkers were not only frequently used as surrogate efficacy endpoints in accelerated approvals, but also in traditionally approved rare disease drugs. The choice of primary efficacy endpoints varied by therapeutic area, approval pathway, and understanding of disease etiology.

Hong, Kyungwan [Rare Diseases Team, Office of New ↗

DOE OSTI2024

An ontology-based knowledge graph for representing interactions involving RNA molecules

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

MjCyc: Rediscovering the pathway-genome landscape of the first sequenced archaeon, Methanocaldococcus (Methanococcus) jannaschii

The genome of Methanocaldococcus (Methanococcus) jannaschii DSM 2661 was the first Archaeal genome to be sequenced in 1996. Subsequent sequence-based annotation cycles led to its first metabolic reconstruction in 2005. Leveraging new experimental results and function assignments, we have now re-annotated M. jannaschii, creating an updated resource with novel information and testable predictions in a pathway-genome database available at BioCyc.org. This reannotation effort has resulted in 652 function assignments with enzyme roles, accounting for a third of the total protein-coding entries for this genome. The updated resource includes 883 reactions, 540 enzymes, and 142 individual pathways. Despite notable progress in computational genomics, more than a third of the genome remains functionally uncharacterized. The publicly available MjCyc pathway-genome database holds great potential for the wider community to conduct research on the biology of methanogenic Archaea.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Database of low‐temperature absorption and fluorescence spectra of native photosynthetic tetrapyrrole macrocycles

Low-temperature (77 K) absorption and fluorescence spectra of 12 naturally occurring photosynthetic tetrapyrrole macrocycles have been recorded in a frozen glass (2-methyltetrahydrofuran). The compounds encompass distinct chromophore classes: porphyrin, chlorophyll c 2 ; chlorin, chlorophylls a, b, d, f and bacteriochlorophylls c, d, e, f; and bacteriochlorin, bacteriochlorophylls a, b, g. The spectra are compared with those of the same pigment in liquid solution (predominantly 2-methyltetrahydrofuran) at room temperature (293 K). The measured Stokes shifts at 77 K across the 12 macrocycles range from ~30 to 300 cm −1 . The spectral data in digital form are made available as part of the PhotochemCAD databases. Literature searches have revealed extensive published data for Chl a (often in biological matrices) but at best rather limited data for less common macrocycles. The availability of a systematic collection of curated spectral data collected at low temperature should be useful for a variety of assessments, including reconstruction of absorption spectra of (bacterio)chlorophyll-containing protein complexes, vibrational analysis of absorption and fluorescence spectra, and calculations where knowledge of energy levels is important.

Niedzwiedzki, Dariusz M. [Washington University in↗

DOE OSTI2025

Fermented Foods Microbial Genomes Database

This database contains ~4,300 microbial genomes assembled from diverse fermented foods. These genomes were obtained from a larger set of 13,850 microbial genomes by clustering them at 99% average nucleotide identity (ANI) to create a "species"-representative database.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

The Natural Products Magnetic Resonance Database (NP-MRD) for 2025

The Natural Products Magnetic Resonance Database or NP-MRD (https://np-mrd.org) is a comprehensive, freely accessible, web-based resource for the deposition, distribution, extraction and retrieval of nuclear magnetic resonance (NMR) data on natural products. The NP-MRD was initially established to support compound de-replication and data dissemination for the natural products community. However, that community has now grown to include many users from the metabolomics, microbiomics, foodomics and nutrition science fields. Indeed, since its launch in 2021, the NP-MRD has expanded enormously in size, scope and popularity. The current version of NP-MRD now contains nearly 7X more compounds (281,859 vs. 40,908) and 7X more NMR spectra (5.1 million vs. 817,000) than the first release. More specifically, an additional 4.6 million predicted spectra and another 11,000 spectra simulated from experimental chemical shifts were deposited into the database. Likewise, the number of NMR raw spectral data depositions has grown from a 165 spectra per year to more than 10,000 per year. As a result of this expansion, the number of monthly webpage views has grown from 55 to 20,000 and the number of monthly visitors has increased from 7 to 2500. To address this growth and to better support the expanding needs of its diverse community of users, many additional improvements to the NP-MRD have been made. These include significant enhancements to the data submission process, important improvements to the visualization and display of NMR spectra, notable updates to the database’s spectral search utilities and useful additions to support better NMR spectral analysis/prediction. Significant efforts have also been undertaken to remediate and update many of NP-MRD’s database entries. This manuscript describes these database improvements and expansion efforts, along with how they have been implemented and what future upgrades to the NP-MRD are planned.

Artifical Intelligence↗

DOE OSTI2025

HBET V3.0 Installation Manual

The Hydropower Biological Evaluation Toolset (HBET) V3.0 now requires Python v3.11.0 to be installed, following the addition of the absolute fish injury rate prediction feature. This version introduces two new strike metrics—based on velocity and pressure—to provide a more precise understanding of the biological effects of fish collisions with rigid structures within the fish passage system. Additionally, SQL Server 2019 is the supported database for this release. This installation guide will walk users through the process of installing HBET V3.0 along with all necessary dependencies.

13 HYDRO ENERGY↗

DOE OSTI2025

Mining Thermophile Photosynthesis Genes: A Synthetic Operon Expressing Chloroflexota Species Reaction Center Genes in Rhodobacter sphaeroides

Photosynthesis is the foundation of the vast majority of life systems, and is therefore the most important bioenergetic process on earth. The greatest diversity of photosynthetic systems is found in microorganisms. However, our understanding of the biophysical and biochemical processes that transduce light into chemical energy is derived from a relatively small subset of proteins from microbes that are amenable to cultivation, in contrast to the huge number of predicted proteins that catalyze the initial photochemical reactions deposited in databases, such as from metagenomics. We describe the use of a Rhodobacter sphaeroides laboratory strain for the expression of heterologous photosynthesis genes to demonstrate the feasibility of mining this resource, focusing on hot spring Chloroflexota gene sequences. Using a synthetic operon of genes, we produced a photochemically active complex of reaction center proteins in our biological system. We also present bioinformatic analyses of anoxygenic type II reaction center sequences from metagenomic samples collected from hot (42–90 °C) springs available through the JGI IMG database, to generate a resource of diverse sequences that are potentially adapted to photosynthesis at such temperatures. These data provide a view into the natural diversity of anoxygenic photosynthesis, through a lens focused on high-temperature environments. The approach we took to express such genes can be applied for potential biotechnology purposes as well as for studies of fundamental catalytic properties of these heretofore inaccessible protein complexes.

Chloroflexota↗

DOE OSTI2026

Single-cell chromatin accessibility and cis -regulatory element analyses in plants using the scPlantReg platform

Understanding gene regulation is fundamental to plant improvement, but the lack of plant-specific single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) frameworks and cross-species databases has limited insights into cell-type-specific cellular regulation. Here we present ‘scPlantReg’, an integrated framework and database for plant scATAC-seq data. scPlantReg supports end-to-end analyses from raw data processing to biological interpretation and features ‘scATACtor’, a supervised machine-learning approach that outperforms existing tools for cell-type annotation. We applied scPlantReg to pearl millet to characterize cell-type-specific chromatin accessibility and identify validated activating and repressing accessible chromatin regions (ACRs), revealing WRKY transcription factors as potential regulators of xylem development. Furthermore, we reanalysed scATAC-seq datasets from 8 plant species, spanning 11 tissues and multiple developmental stages, enabling cross-species comparisons. Furthermore, these analyses uncovered conserved regulatory programmes, including AP2/EREBP-associated ACRs linked to cell wall development and cell-type-conserved TFs across grasses. Collectively, scPlantReg provides a general framework and resource for comparative regulatory analysis in plants.

Epigenomics↗

DOE OSTI2024

Chemoproteogenomic stratification of the missense variant cysteinome

Abstract Cancer genomes are rife with genetic variants; one key outcome of this variation is widespread gain-of-cysteine mutations. These acquired cysteines can be both driver mutations and sites targeted by precision therapies. However, despite their ubiquity, nearly all acquired cysteines remain unidentified via chemoproteomics; identification is a critical step to enable functional analysis, including assessment of potential druggability and susceptibility to oxidation. Here, we pair cysteine chemoproteomics—a technique that enables proteome-wide pinpointing of functional, redox sensitive, and potentially druggable residues—with genomics to reveal the hidden landscape of cysteine genetic variation. Our chemoproteogenomics platform integrates chemoproteomic, whole exome, and RNA-seq data, with a customized two-stage false discovery rate (FDR) error controlled proteomic search, which is further enhanced with a user-friendly FragPipe interface. Chemoproteogenomics analysis reveals that cysteine acquisition is a ubiquitous feature of both healthy and cancer genomes that is further elevated in the context of decreased DNA repair. Reference cysteines proximal to missense variants are also found to be pervasive, supporting heretofore untapped opportunities for variant-specific chemical probe development campaigns. As chemoproteogenomics is further distinguished by sample-matched combinatorial variant databases and is compatible with redox proteomics and small molecule screening, we expect widespread utility in guiding proteoform-specific biology and therapeutic discovery.

Desai, Heta (ORCID:0000000343621707)↗

DOE OSTI2025

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

Understanding the interactions and regulatory relationships among biomolecules is essential for deciphering complex biological systems and elucidating the mechanisms behind diverse biological functions. Traditionally, the collection of such molecular interaction data has relied on expert curation, a process that is both time-consuming and labor-intensive. To address these limitations, this study explores the use of large language models (LLMs) to automate the genome-scale extraction of molecular interaction knowledge. Here, we evaluate the performance of various LLMs on key biological tasks, including the identification of protein-protein interactions, detection of genes associated with pathways influenced by low-dose radiation, and inference of gene regulatory relationships. Our findings demonstrate that larger LLMs tend to perform better, particularly in extracting intricate gene and protein interactions. Despite their strengths, these models face challenges in recognizing functionally diverse gene groups and highly correlated regulatory relationships. Through a comprehensive analysis using established molecular interaction and pathway databases, we show that LLMs possess the potential to identify relevant biomolecules and predict their interactions, offering valuable insights and marking a significant step toward AI-driven biological knowledge discovery.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2026

Multimodal Approaches for Leveraging Domain Knowledge with State-of-the-Art Machine Learning to Engineer Biocatalysts

This grant aimed to accelerate the development of specialized enzymes—biological catalysts essential for sustainable manufacturing and medicine—by integrating traditional laboratory evolution with cutting-edge artificial intelligence. To achieve this, we developed a suite of high-throughput sequencing tools and a centralized database to bridge the gap between a protein’s genetic "code" and its physical function. By training machine learning models on large datasets, we also demonstrated the ability to move beyond slow, trial-and-error testing to a "generative" approach, where AI can independently design new, versatile enzymes like tryptophan synthases. Ultimately, these findings demonstrate that combining laboratory data with computer-guided design enables the engineering of highly efficient biological tools with unprecedented speed and precision.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting

Single-cell multiomics provides comprehensive insights into gene regulatory networks, cellular diversity, and temporal dynamics. Here, we introduce nanoSPLITS (nanodroplet SPlitting for Linked-multimodal Investigations of Trace Samples), an integrated platform that enables global profiling of the transcriptome and proteome from same single cells via RNA sequencing and mass spectrometry-based proteomics, respectively. Benchmarking of nanoSPLITS demonstrates high measurement precision with deep proteomic and transcriptomic profiling of single-cells. We apply nanoSPLITS to cyclin-dependent kinase 1 inhibited cells and found phospho-signaling events could be quantified alongside global protein and mRNA measurements, providing insights into cell cycle regulation. We extend nanoSPLITS to primary cells isolated from human pancreatic islets, introducing an efficient approach for facile identification of unknown cell types and their protein markers by mapping transcriptomic data to existing large-scale single-cell RNA sequencing reference databases. Accordingly, we establish nanoSPLITS as a multiomic technology incorporating global proteomics and anticipate the approach will be critical to furthering our understanding of biological systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

EvoNet: A phylogenomic and systems biology approach to identify genes underlying plant survival in marginal, low‐N soils

The DOE‐BER “EvoNet” project investigates the genetic and molecular basis of plant resilience in extreme environments. We do this by identifying key genes that enable “extreme survivor” species to thrive in the nitrogen-poor soils of Chile’s hyper-arid Atacama Desert. Our collections focus on 32 Atacama extremophile species, including seven grass species with potential biofuel applications. To identify genes-of-importance to survival we compared genomic and transcriptomic profiles of extremophile species that thrive in the Atacama to those of closely related “sister” species from nitrogen-rich arid and mesic regions of California. Deep RNA sequencing and de novo transcriptome assembly across these triplet species sets supported a phylogenomic framework for identifying positively selected genes associated with adaptive divergence. Our integrative analysis combined ecological and environmental data, metagenomics, evolutionary and systems biology, and metabolomics. This enabled us to create an unprecedented framework for systematically understanding how non-model plants have adapted to survive in extreme conditions. Our resulting database of positively selected ortholog groups in the extremophile plants offers promising targets for engineering crop and biofuel species with enhanced resilience to drought and extreme weather. Additionally, our newest dataset explores and exploits a complementary metabolomic approach. This new aspect provides innovative strategies to manipulate plant cell metabolism, further supporting efforts to improve agricultural productivity in the face of extreme climates. Importantly, our combined evolutionary- and metabolomic-based strategies focused on convergent patterns of adaptation, providing a genetic and metabolomic toolkit for improving crop and biofuel resilience across diverse plant species. Finally, our novel exploration of ecological and evolutionary dynamics delivered to the community a phylogenomic computational pipeline called “PhyloGeneious.” Our continued adaptations of this pipeline are publicly available to expedite evolutionary genomic research for future scientific discoveries. In total, our DOE-BER has provided genomic, metabolomic, and computational strategies to understand how extremophile plants provide evolutionary and physiological targets for improving agricultural and biofuel production.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

VISTA Enhancer browser: an updated database of tissue-specific developmental enhancers

Regulatory elements (enhancers) are major drivers of gene expression in mammals and harbor many genetic variants associated with human diseases. Here, we present an updated VISTA Enhancer Browser (https://enhancer.lbl.gov), a database of transgenic enhancer assays conducted in developing mouse embryos in vivo. Since the original publication in 2007, the database grew nearly 20-fold from 250 to over 4500 experiments and currently harbors over 23 500 images. The updated database provides structured information on experiments conducted at different stages of embryonic development, including enhancer activities of human pathogenic and synthetic variants and sequences derived from a variety of species. In addition to manually curated results of thousands of individual experiments, the new database also features hundreds of manually curated comparisons between alleles. The VISTA Enhancer Browser provides a crucial resource for study of human genetic variation, gene regulation and developmental biology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Protein–Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools

The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to significant biological breakthroughs. For example, the study of PPIs involved in the human:SARS-CoV-2 viral infection mechanism aided in the development of the SARS-CoV-2 vaccines. Though several databases exist for the manual curation of PPI networks, text mining methods have been routinely demonstrated as useful alternatives for newly studied or understudied species where databases are incomplete. Here, the relationship extraction (RE) performance of several open-source classical text processing, machine learning (ML)-based natural language processing (NLP), and large language model (LLM)-based NLP tools were compared. Overall, our results indicated that networks derived from classical methods tend to have high true positive rates at the expense of having overconnected-networks, ML-based NLP methods have lower true positive rates but networks with the closest structures to the target network, and LLM-based NLP methods tend to exist in-between the two other approaches, with variable performances. Finally, the selection of a specific NLP approach should be tied to the needs of a study and text availability, as models varied in performance due to the amount of text provided.

59 BASIC BIOLOGICAL SCIENCES↗