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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Multicriterion benefit evaluation methodology for safety enhancements in nuclear power plants and application for FLEX strategies

Here we propose a multicriterion benefit evaluation (MCBE) methodology for evaluating costs and benefits of implementing safety enhancements in nuclear power plants. The MCBE methodology establishes a comprehensive evaluation scope using multiple evaluation criteria (including public risk, occupational risk, plant revenue, and plant cost) and multiple contexts (including plant normal operations, incidents, and accidents). The MCBE methodology formulates a benefit or cost element as the product of a monetary consequence and its occurrence-likelihood change. The MCBE methodology also incorporates a decision maker’ preferences toward occurrence likelihood and monetary consequence into cost and benefit estimations through a modified usage of cumulative prospect theory. This paper applies the MCBE methodology to the implementation of diverse and flexible coping strategy (FLEX) in a generic nuclear power plant as an illustrative example. The FLEX benefits are evaluated through three types of influence paths: plant safety enhancement, risk-informed activities, and potential additional credit of FLEX in supporting plant operation and maintenance-efficiency improvement. The benefit-to-cost ratio (BCR) of FLEX implementation is calculated. Sensitivity analyses are conducted to examine the impacts of modeling assumptions and input parameter values on the BCR.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2026

Adaptive Cybersecurity for Distributed Energy Resources (AdCyDER): Online Reinforcement Learning with Stackelberg-Optimized Defenses — Pipeline Architecture, Evaluation Methodology, and Findings from a Synthetic-Data Evaluation

This report documents the design and evaluation of an integrated online-learning pipeline developed within the AdCyDER project for Distributed Energy Resource (DER) cybersecurity. The pipeline couples a Reinforcement Learning (RL) attack classifier — which produces an attack-type probability distribution — with a Stackelberg game-theoretic (GT) defense selector that consumes those distributions alongside SME-encoded priors over (defense, attack) effectiveness pairings and perdefense costs to choose grid-health-preserving defenses. The objective is not attack classification per se but production of distributions that drive effective defense selection through the Stackelberg layer, learned from delayed grid-health feedback rather than labeled attack data. AdCyDER as a whole is broader than the work presented here; this report covers the specific RL/GT loop integration and its evaluation. We present the integrated pipeline (SCADA telemetry with Fronius inverter physics, Suricata IDS, time-windowed aggregation, per-facility LSTM classifier, Stackelberg optimizer, OpenC2 actuators), an experimental campaign of 28 eight-hour iterations across three baseline modes, and a pipeline-ordered diagnostic protocol. The protocol identifies two distinct failure modes within the loop: paired supervised ceilings on the same features establish that the deployed online RL classifier (macro F1 ≈ 0.07) sits at least 4.7× below a same-architecture supervised LSTM (≈ 0.34) and 10–11× below a linear feature-signal ceiling (≈ 0.70–0.79 depending on per-facility isolation), localizing the dominant failure to the training procedure; and the reward signal driving online updates carries weak directional coupling with classifier correctness in the methodology-expected direction (multi-lens convergent: top-decile P(true) records produce more frequent state changes and slightly larger improvements, top-vs-bot Cohen’s 𝑑 ≈ −0.19), but at effect magnitudes too small to drive gradient-based learning at the campaign sample size. The original learning hypothesis is not supported by the data. The primary contributions are the diagnostic methodology — proposed as a transferable falsification protocol for online RL/GT defense pipelines learning from delayed environmental reward — and the open, reproducible experimental infrastructure. We outline reward reformulation as the highest-priority aspirational next step given the underpowered-but-aligned Q6 reading, with hardware-in-the-loop evaluation as the broadest scope-expansion option.

Blakely, Benjamin [Argonne National Laboratory (AN↗

DOE OSTI2020

A Multi-Criteria Impact Evaluation Methodology Applied to FLEX Strategies in Nuclear Power Plants

As responses to Fukushima Dai-ichi nuclear accident, several advanced, near-term technologies have been or are being introduced to enhance safety of operating nuclear power plants (NPPs) as well as improving their sustainability. Examples include Accident-Tolerant Fuel designs, Diverse and Flexible Coping Strategies (FLEX), and passive cooling system designs. The combinations of these technologies, coupled with enhancements to plant components and systems and improved fuel cycle efficiency, constitute a concept of Enhanced Resilient Plant Systems, which is proposed and being explored by Idaho National Laboratory (INL) under the Risk-Informed Systems Analysis Pathway of the U.S. Department of Energy’s Light Water Reactor Sustainability Program. To decide if introducing a technology is necessary or worthy, the impacts of this technology on NPPs should be evaluated, preferably in a quantitative manner, and provided to decision makers. This paper summarizes an on-going work conducted by the authors on developing a multi-criteria impact evaluation methodology. This methodology is expected to be applicable to evaluate a variety of technologies, while FLEX strategy is adopted as a case study for this paper.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Development and testing of a performance evaluation methodology to assess the reliability of occupancy sensor systems in residential buildings

With the emergence of advanced occupancy sensor technologies to better detect occupancy in buildings, a universal methodology and metrics are required to evaluate and report sensor systems’ reliability and compare the performance across multiple sensor systems. Herein this research presents a methodology to assess the reliability of occupancy sensor systems in residential buildings in a controlled laboratory environment, including both “typical” and “failure” testing scenarios. The developed methodology was then implemented to evaluate a novel occupancy detection sensor system’s reliability. “Typical” testing evaluates the overall accuracy of the sensor system, which suggest how reliable the occupancy sensor system is over time. Results show that on average, the precision and recall are 0.75 and 0.70, indicating similar numbers of false positives and false negatives across the dataset. The overall accuracy of the tested sensor system was 62.4% to 76.4%. Failure testing results indicate whether there are influential variables impacting the sensor performance. For the tested sensor system, the number of occupants, presence of large objects, presence of interior light sources, and number of doors are not influential, while lighting level, location of occupants, additional door in the entry/exit area, and having the TV on are variables determined to impact the sensor system performance.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

An Overview of Argonne’s Advanced Mobility Technology Laboratory Vehicle Systems Instrumentation and Evaluation Methodology

This report will provide a general overview of the testing facilities, research equipment, and general testing methodologies Argonne utilizes to conduct vehicle technology evaluations of advanced technology research vehicles. Data is captured from vehicle evaluations that provide powertrain operation and corresponding energy consumption based on a combination of in-depth instrumentation and focused testing. This resulting dataset of hundreds of time-resolved vehicle signals provide a basis for direct analysis and model validation of vehicle specific technologies. Argonne has attempted to standardize the approach to vehicle instrumentation and testing at Argonne, but it should be noted that each vehicle, and the corresponding assessment, remains unique. It is suggested that the reader reference vehicle specific testing reports for an overview of the unique aspects for each test vehicle. Additionally, the datasets for research vehicles are made publicly available through the Advanced Mobility Technology Laboratory’s Downloadable Driving Database (D3) at www.anl.gov/d3.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Glauber’s Salt Composites for HVAC Applications: A Study on the Use of the T-History Method with a Modified Data Evaluation Methodology

Phase change materials (PCMs) can be utilized in buildings for peak load shifting in air conditioning systems, and the use of salt hydrate-based PCMs can reduce the cost of thermal energy storage devices. Glauber’s salt is an economical salt hydrate PCM with a melting point of around 32 °C. However, the desired melting range typically falls between 18 and 22 °C for building air conditioning applications. Although many researchers have characterized Glauber’s salt and its composites with modified melting points, enthalpy–temperature curves for composites of Glauber’s salt and NaCl are unavailable. In this study, we report the melting and solidification enthalpy–temperature curves for two different composites of Glauber’s salt and NaCl with a melting point of 21 °C obtained by the T-history method. Both composites contain NaCl to suppress the melting point, borax to reduce supercooling, and sodium polyacrylate as a thickener to enhance cyclic stability. The first composite with 12 wt.% NaCl demonstrated 139 kJ·kg−1 of latent heat of fusion, and the second composite with 9 wt.% NaCl demonstrated 171 kJ·kg−1. Both the composites have high volumetric energy densities compared to their organic counterparts with similar melting points.

Chemistry↗

DOE OSTI2022

Energy-dependent optimization of the prompt fission neutron spectrum with CGMF

Throughout the course of FY21, significant effort was put into investigating models within the LANL developed Hauser-Feshbach fission fragment decay code, CGMF, to understand and potentially solve the long-standing challenge of a too-soft prompt fission neutron spectrum, PFNS. Several inputs and models to CGMF were investigated, including the discrete nuclear levels, the optical model potential, level densities, and the fission fragment initial conditions. Some of the global models within CGMF led to a slight hardening of the neutron spectrum—particularly the likely incomplete discrete levels in through which γ-rays decay—but none of the changes where large enough for the tail of the PFNS to reproduce experimental data. A significant hardening of the spectrum tail was observed when the fission fragment initial conditions were optimized based on their sensitivities to the PFNS data for thermal incident neutrons. In this way, the parameters for the CGMF mass and total kinetic energy distributions, along with the spin cutoff factor were adjusted to better reproduce the experimental PFNS measurements. This optimization hardened the tail of the PFNS slightly but led to unphysical mass distributions for the fission fragments before neutron emission. It was clear from the above that we do not expect to be able to produce an evaluation-quality PFNS with CGMF in the near future. Challenges at thermal will persist–and possibly worsen–with increasing incident energy, where more models are needed to completely describe the fission. Basic-science research funding exceeding the amount available and scope of our NCSP funds would be needed to tackle this decade-long challenge impacting many fission-fragment event generator. And, in fact, Amy Lovell won LDRD ECR funding to do so over the next few years. Therefore, we focused in FY22 on extending evaluation capabilities beyond thermal incident neutrons, to take into account the incident energy dependence of the PFNS and fission fragment initial condition distributions in CGMF. We chose to set up the evaluation methodology to perform PFNS evaluations with CGMF across incident-neutron energies, in order to have it readily available for future NCSP evaluations when the PFNS from CGMF has improved. In this report, we outline the evaluation methodology, along with the results of the optimization, including full model calculations with CGMF using the evaluated parameters.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Methodology for Evaluating Commercial Energy Code Updates

This document lays out the Department of Energy’s (DOE’s) methodology for evaluating the cost-effectiveness of energy code and standard proposals and editions. The evaluation is applied to new provisions or editions of ANSI/ASHRAE/IES Standard 90.1 and the International Energy Conservation Code. The methodology follows standard lifecycle cost (LCC) economic analysis procedures. A cost-effectiveness evaluation requires three steps: 1) evaluating the energy and energy cost savings of code changes; 2) evaluating the incremental and replacement costs related to the changes; and 3) determining the cost-effectiveness of energy code changes based on those costs and savings over time.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Applying Methodology for Evaluating and Validating TSLs to Materials of Interest to NCSP [Slides]

This presentation covers the topic of applying methodology for evaluation validation of TSLs to material of interest to the Nuclear Criticality Safety Program (NCSP). The talk covers recent efforts in validating thermal neutron scattering cross sections. The presentation also proposes a methodology for not only validating thermal scattering files that utilizes all available experimental data, but also for evaluating new libraries as demonstrated on Polystyrene and Lucite

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Methodology for Evaluating Energy Savings, Cost-Effectiveness, and Societal Impacts of Commercial Energy Code Changes

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Development and demonstration of a methodology to evaluate high burnup fuel susceptibility to pulverization under a loss of coolant transient

For economic reasons, the US nuclear industry is renewing efforts to build a technical basis to extend rod average burnup limits above the current regulatory burnup limit of 62 GWd/MTU. The primary driver is to increase pressurized water reactor cycle lengths to 24 months, reducing the number of fresh fuel assemblies and core design constraints, thereby making core energy utilization more efficient. However, fuel pellet fragmentation and pulverization, termed high burnup fuel fragmentation (HBFF), has been observed in the high burnup (>90 GWd/MTU) Halden loss-of-coolant-accident (LOCA) integral test series. The issue gained attention when fuel fragmentation and pulverization were also observed closer to the current US regulatory limit during the US Nuclear Regulatory Commission (NRC) sponsored out-of-core integral test at Studsvik Nuclear in early 2011. This led to NRC concerns with potential changes to fuel and core designs relative to fuel pellet pulverization. In a letter to the NRC Commissioners, the staff specifically identified a need to “…define the boundary of safe operation for key fuel design and operating parameters,” stating that “the staff is challenged to evaluate the acceptability of future fuel design advancements and fuel utilization changes.” As such, it can be concluded that HBFF and potential dispersal into the reactor coolant system introduces additional complications in light-water reactor (LWR) fuel safety evaluations. However, it is not clear how much fuel will be susceptible to HBFF; nor has there been a methodology developed to evaluate fuel susceptibility to HBFF. To that end, this paper proposes an analysis methodology to assess fuel susceptibility to HBFF during LOCA scenarios. The work presented here uses the BISON fuel performance code to evaluate a representative pressurized water reactor fuel rod exposed to a rod average burnup of 75 GWd/MTU. Sensitivity studies investigated the impact of the peak cladding temperature, transient fission gas released, and pre-transient fission gas release on cladding ballooning and burst timing. Subsequently, a methodology to assess fuel susceptibility to HBFF will be developed based on experimental data published in the open literature. The methodology will then be demonstrated by calculating the mass of fuel susceptibility to HBFF. The BISON results conclude that increasing peak cladding temperature drastically decreased time to failure, and decreased balloon size both of which have been confirmed experimentally. Additionally, the effect of pre-transient and transient fission gas release affected cladding balloon size and burst timing. Finally, fuel susceptibility to HBFF significantly decreased as a function of peak cladding temperature.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis

This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Safety in Artificial Intelligence: Challenges and Opportunities for the U.S. National Labs and Beyond

This report discusses the importance of the critical and underexplored topic of artificial intelligence (AI) safety, as highlighted during the “Strategy Alignment on AI Safety” workshop convened at Lawrence Livermore National Laboratory (LLNL) in April 2024. Through a summary of keynote talks, panel discussions, and breakout sessions, world-leading AI safety experts from academic, industry, national labs, and government agencies clearly agree on the need for and importance of large-scale investments for research and capabilities in AI safety. With the field innovating at unprecedented rates, there is increasing urgency to develop novel evaluation methodologies that allow full considerations of risks/threats of AI technologies in different domains. Quantitative metrics and effective methodologies that can evaluate and audit the “safeness” of how a given AI technology is trained, deployed, or regulated are, at best, nascent for certain scenarios or, more commonly, nonexistent. This maturation gap presents the possibility of serious threats to national security, and further inaction may have serious consequences. Additionally, the gap between the public’s and research community’s perceptions of AI risks/rewards is significant. While numerous voices from the AI community have expressed concern that the risks could be so high that future AI systems could inflict extinction-level damage to humanity if deployed incorrectly, the public largely is aware only of risk in low-impact scenarios. This discrepancy highlights the crucial need for researchers to articulate to governmental bodies what, why, and when various AI risks matter as part of motivating funding requests. Thus, the call to action for this community is to pursue AI safety as a “Big Science” project on a scale comparable to the Manhattan Project. High risks and high payoffs are on the table, but safe AI is a fast-moving target, and large-scale investments are needed to guide development of this technology in a responsible way. We highlight the need for a multilayered solution combining the development of new methods and algorithmic approaches to mitigate threats with an active participation of the government(s) in setting high industry standards and regulations based on state-of-the-art technology. The U.S. Department of Energy (DOE) national laboratories have served as leading institutions for scientific innovation in the U.S. for more than 70 years. Drawing on their expertise in the AI community and their history of safeguarding critical and sensitive information, and as we look to the future, national labs are the best choice for evaluating and safeguarding AI technologies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Proposed Methodology for Evaluating and Validating TSLs [Slides]

This presentation begins by providing an overview of the research and discusses recent efforts in validating thermal neutron scattering cross sections, including differential cross section measurements at ORNL for evaluation and validation, total cross section measurements at RPI, pulsed-neutron die-away experiments at LLNL, and integral criticality experiments by LLNL. The presentation also proposes a methodology not only for validating thermal scattering files that utilizes all available experimental data, but also for evaluating new libraries as demonstrated on polystyrene. In conclusion, polystyrene evaluation was conducted using multiple experiments, including differential and integral. The proposed methodology has been shown to improve neutron transport and files have been submitted to NNDC for inclusion in ENDF/B-VIII.1 release. MCT-012 & PCM-002 has been relatively insensitive to changes in $\mathcal{S}(α,β)$. The research found that differences in differential results don’t always propagate to differences in integral results, hence why evaluation of differential data important. Future work on the project is also discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Simulation, Challenge Testing and Validation of Solutions for Residential and Commercial Occupancy Sensing/Counting and CO2 Sensing (Final Report)

Carbon dioxide sensors used for monitoring and control applications in buildings are known to be sensitive to long term drift and can be affected by variations in temperature, humidity, other gas interferents, pressure and other factors that cannot be corrected through the typical auto-calibration methods often embedded in the sensor electronics. This investigation was part of the Category D defined in the ARPA-E Saving Energy Nationwide in Structures With Occupancy Recognition (SENSOR) Financial Assistance Funding Opportunity Announcement No. DE-FOA-0001737, FFDA No. 81.135, January 18, 2017. The FOA defined target criteria for the evaluation of the Category C commercial CO2 sensors selected for review and development through the ARPA-E SENSOR program and charged the Iowa State Category D team with developing an evaluation methodology for Category C sensors in accordance with these criteria. As part of the work, the Iowa State team chose to engage the standards community in the development of the evaluation methodology. Standard development work in the D22.05 Indoor Air and D22.03 Ambient Atmospheres and Source Emissions subcommittees of ASTM International Committee D22 on Air Quality on the evaluation of low cost sensors and provision of guidance for using indoor carbon dioxide concentrations to evaluate indoor air quality and ventilation is of particular relevance to the Category D efforts. To date, three draft standards with specific input by the Iowa State Category D team have passed the subcommittee balloting stage and are being balloted in December 2022 and January 2023 in the main D22 committee. In addition to the aforementioned standards to which the PI was a contributing author, intellectual property developed in this project pertaining to “An Automated System for Evaluation of the Long Term Performance of Carbon Dioxide Sensors” and “An Automated Ground Truth System for Real-Time Reliability Assessment, Capture of Control Decisions and Energy Savings Measurements for Occupancy Recognition Systems and Other Applications” are the other major deliverables of the project. These standards and the developed intellectual property are expected to benefit the public in a number of ways stemming from their provision of means to benchmark the performance of carbon dioxide and other sensor systems. In particular, there is high interest in techniques to demonstrate the performance of low-cost carbon dioxide sensors which can be used for typical HVAC applications (e.g., demand controlled ventilation, outdoor and indoor air monitoring, estimation of occupancy, etc.).

42 ENGINEERING↗

DOE OSTI2024

Methodology to evaluate design modifications intended to eliminate frosting and high discharge temperatures in air-source heat pumps (ASHPs) in cold climates

Air-source heat pumps (ASHPs) operating in cold climates experience problems with frosting and high refrigerant temperatures. These problems increase energy consumption, and their severity depends on the climatic conditions. In the present paper, a methodology for identifying the prevailing problem between frosting and high discharge temperatures is presented. Three performance indices, the frosting index (FI), the discharge index (DI), and the total loss index (TLI), are proposed to quantify the impacts of frosting and high discharge temperatures on the annual performance of ASHPs in different climatic conditions. The FI and DI show which problem (frosting or high discharge temperature) dominates, and the TLI indicates the combined effect of frosting and high discharge temperatures on the performance of an ASHP. A thermodynamic model of an ASHP coupled with the TRNSYS building simulation tool is used to estimate the performance of an ASHP and the proposed loss indices to estimate the impact of both frosting and high discharge temperatures for 45 cities in Canada. The results can be extended to other parts of the world that experience similar climatic conditions The results reveal that in cities in ASHRAE climatic zones 5 and 6 (classified as cold regions) where the ambient air temperatures are predominantly between -15 °C to 6 °C, ASHPs are heavily impacted by frosting. The problem of high discharge temperatures in ASHPs is predominant in cities in climate zones 7 and 8 (classified as very cold and subarctic regions) where the temperatures are frequently below -20 °C in winter. Among the cities considered, St. John, NL has the highest fraction of heating hours experiencing frosting (90 %), where the annual increase in energy consumption due to frosting is 13.5 % of the annual heating energy consumption. The highest annual increase in energy consumption due to high discharge temperatures is in Isachsen, NU (zone 8), where the increase is 30 % of the annual heating energy consumption. Based on the proposed indices, another index called the performance gain index (PGI) is created, which can be used as a first step to assess the energy-saving potential of design modifications applied to ASHPs to solve the problems of frosting and high discharge temperatures. The PGI will aid in developing climate specific ASHPs. One possible design modification is the use of a two-stage ASHP with an economizer. It is observed that the two-stage ASHP with economizer can mitigate high discharge temperatures and improve performance in very cold and subarctic regions (zones 7 and 8). However, it is not as beneficial in zones 5 and 6, where the impact of high discharge temperatures on performance is minimal and frosting dominates. Finally, a case study, using the PGI to evaluate the economic and environmental effectiveness of a two-stage ASHP with economizer is presented for the city of Saskatoon.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

A Methodology to Evaluate the Grid Reliability Impact of Oscillations Induced by Large Loads

The rapid growth of hyperscale AI data centers is bringing renewed attention to the reliability risk that sustained forced oscillations pose to bulk power systems, with cyclic computational workloads emerging as a new forcing source. Unlike the broadband, stochastic disturbances from traditional industrial loads such as arc furnaces, AI training and inference facilities can inject large active power swings concentrated at specific frequencies over extended durations - characteristics that existing grid planning practices do not account for. While the North American Electric Reliability Corporation (NERC) has recognized this gap and called for system-level studies of large load interconnections, no standardized methodology exists to screen, simulate, and quantify these risks at the planning stage. This report presents the Risk Assessment Tool for Large Load-induced Events (RATLLE), a Python-based, publicly available script suite developed at the Pacific Northwest National Laboratory to evaluate bulk power system reliability risks from data center-induced oscillations. RATLLE implements a three-module workflow: a screening module that identifies vulnerable interconnection locations and excitable system modes; a simulation module that models cyclic data center load behavior using a commercial positive sequence simulation platform; and an analysis module that computes risk metrics and generates interactive visualization dashboards. The risk metrics, formulated around simulation observables, map oscillation impacts to a three-stage severity scale spanning latent equipment fatigue through imminent cascading failure. The methodology is demonstrated on two Western Electricity Coordinating Council (WECC) system models: a publicly available 240-bus reduced representation and a detailed 2031 Heavy Winter planning case. Case studies illustrate that even modest 50 MW forced oscillations at resonant frequencies can produce wide-area power swings, N-1 security constraint violations, and cascading generator trips through protection actions - outcomes that would not occur under normal operating conditions without oscillations present. The results underscore the need for standardized oscillation impact assessment in large load interconnection studies and provide a reproducible, extensible framework for utilities to adopt or customize within their existing planning workflows.

Biswas, Shuchismita↗

DOE OSTI2020

An evaluation of methodology to determine algal genome completeness

The advancement of sequencing technologies has resulted in a rapid expansion of genome sequencing. One challenge in processing and analyzing this abundance of genomics data is development and application of tools to accurately assess the quality of novel genomes. The quality of a given genome assembly is commonly assessed using the presence of conserved orthologs. However, the applicability and accuracy of identifying conserved orthologs applied to algal genome assemblies, which are uniquely diverse, is unclear. Here, in this study, we analyze the utility of a genome content assessment tool, Benchmarking Universal Single-Copy Orthologs (BUSCO), to evaluate algal genome assembly completeness. We find that Chlorophyta and Stramenopile BUSCO databases are effective tools to analyze genome completeness of these lineages. For other lineages, the Eukaryota database should be utilized until additional lineage-specific databases are developed. Based on these results, suggested best practices for current usage and necessary future improvements are provided.

59 BASIC BIOLOGICAL SCIENCES↗