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Making Heliophysics Research More Open and Accessible at the Community Coordinated Modeling Center (CCMC)

The Space Weather and Heliophysics modeling community seeks to improve our understanding of space weather events and their impact on human activities. The Community Coordinated Modeling Center’s (CCMC, https://ccmc.gsfc.nasa.gov) mission is to support the community by providing a convenient collaborative platform that brings together space weather models, model simulation data, curated datasets of solar events, and associated value-added services. Using these services, researchers and other end-users may exercise, evaluate, and intercompare contributed models, triage designated R2O models, as well as collaborate on a continuously updated archive of model run results. This presentation reports on CCMC’s ongoing efforts in making Heliophysics models and data more accessible, open, and reproducible. We will also explore interoperability within the ecosystem of CCMC services and how this ecosystem interoperates with external partner services and data streams.

space weather↗
DOE OSTI2013

Evaluation of clinical IMRT treatment planning using the GATE Monte Carlo simulation platform for absolute and relative dose calculations

Purpose: The objective of this study was to evaluate and validate the use of the Geant4 application for emission tomography (GATE) Monte Carlo simulation platform for clinical intensity modulated radiotherapy (IMRT) dosimetry studies. Methods: The first step consisted of modeling a 6 MV photon beam linear accelerator (LINAC), with its corresponding validation carried out using percent depth dose evaluation, transverse profiles, tissue phantom ratio, and output factor on water phantom. The IMRT evaluation was performed by comparing simulation and measurements in terms of absolute and relative doses using IMRT dedicated quality assurance phantoms considering seven different patient datasets. Results: Concerning the LINAC simulated model validation tissue phantom ratios at 20 and 10 cm in water TPR{sub 10}{sup 20} obtained from GATE and measurements were 0.672 {+-} 0.063 and 0.675, respectively. In terms of percent depth dose and transverse profiles, error ranges were, respectively: 1.472%{+-} 0.285% and 4.827%{+-} 1.323% for field size of 4 Multiplication-Sign 4, 5 Multiplication-Sign 5, 10 Multiplication-Sign 10, 15 Multiplication-Sign 15, 20 Multiplication-Sign 20, 25 Multiplication-Sign 25, 30 Multiplication-Sign 30, and 40 Multiplication-Sign 40 cm{sup 2}. Most errors were observed at the edge of radiation fields because of higher dose gradient in these areas. Output factors showed good agreement between simulation and measurements with a maximum error of 1.22%. Finally, for IMRT simulations considering seven patient datasets, GATE provided good results with a relative error of 0.43%{+-} 0.25% on absolute dose between simulated and measured beams (measurements at the isocenter, volume 0.125 cm{sup 3}). Planar dose comparisons were also performed using gamma-index analysis. For the whole set of beams considered the mean gamma-index value was 0.497 {+-} 0.152 and 90.8%{+-} 3.6% of the evaluated dose points satisfied the 5%/ 4 mm criterion. Conclusions: These results show that GATE allows reliable simulation of complex beams in radiotherapy after an accurate LINAC modeling is validated. A simple cross-calibration procedure proposed in this work allows obtaining absolute dose values even in complex fields.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1999

Southern Hemisphere Storm Zones on Mars: Implications of MOLA Topography

Using the NASA Ames Mars general circulation model and recent Mars orbiter laser altimeter (MOLA) global topography, annual-cycle simulations have been performed corresponding to a lowglobally-averaged atmospheric dust loading (tau= 0 - 3). Comparisons of key global circulation fields as simulated utilizing previous topography datasets and those using the new Mars global topography have been carried out. Values of globally and/or hemispheric averaged kinetic energy associated with the longitudinally averaged (i.e., zonal) circulation and longitudinal departures (i.e., eddy components) as a function of season for simulations using the different topography datasets are provided contributions arising frommeteorological variability associated with thermal tides, stationary circulation components and recurrent weather systems (transient baroclinc and/or barotropic eddies). It can be noted that eddy activity in the northern hemisphere (NH) is substantially diminished using the new topographic data, and that at some seasons, the southern hemisphere (SH) activity is moderately increased. Additional information is contained in the original extended abstract.

Hollingsworth, J. L.↗
DOE OSTI2022

Power System Waveform Classification Using Time-Frequency and CNN

Many modern reclosers and circuit breakers have microprocessor relays that record waveforms of system events. In some cases, utilities may record a half-a-dozen event captures for every event. This is thousands of events per year. The industry needs faster, more automated, more conclusive, and easy-to-use systems that can process massive amounts of event recordings without extensive input/support from power system engineers. To address the need for a commercially viable solution that can classify waveform data, energies were directed to develop a universal neural network (NN) structure (deep learning algorithm) that works for a wide variety of system event types. The structure that showed the most promise was one that included the use of spectrograms. The technique has shown positive results in audio engineering, particularly with respect to speech recognition. A waveform signature could be treated as a spoken word like audio waveforms for specific things such as “YES” or “UP”. No two people produce the exact same waveform when speaking each of these words, but audio processing algorithms based on spectrograms and convolutional neural networks (CNN) can still distinguish the word regardless of the speaker. No two circuits produce the exact same waveform for a given event, but the NN can be trained to classify the event type regardless of the circuit or location on the circuit. A Power System Neural Network (PSNN) has been developed to use a CNN to classify events within waveform data for power systems. The waveform is converted to an array of values by way of spectrograms and interpreted as an image. This image is passed into the CNN. The test results on independent simulated test and validation datasets show greater than 99% accuracy. While the results thus far are based on simulated data, the performance of the PSNN is very promising and should work for a wide variety of power system conditions of interest. Ultimately, much of the custom code and tools used today and much of the manual effort expended today may be automated using this PSNN.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2010

An Experimental Study of Small-Scale Variability of Raindrop Size Distribution

An experimental study of small-scale variability of raindrop size distributions (DSDs) has been carried out at Wallops Island, Virginia. Three Joss-Waldvogel disdrometers were operated at a distance of 0.65, 1.05, and 1.70 km in a nearly straight line. The main purpose of the study was to examine the variability of DSDs and its integral parameters of liquid water content, rainfall, and reflectivity within a 2-km array: a typical size of Cartesian radar pixel. The composite DSD of rain events showed very good agreement among the disdrometers except where there were noticeable differences in midsize and large drops in a few events. For consideration of partial beam filling where the radar pixel was not completely covered by rain, a single disdrometer reported just over 10% more rainy minutes than the rainy minutes when all three disdrometers reported rainfall. Similarly two out of three disdrometers reported5%more rainy minutes than when all three were reporting rainfall. These percentages were based on a 1-min average, and were less for longer averaging periods. Considering only the minutes when all three disdrometers were reporting rainfall, just over one quarter of the observations showed an increase in the difference in rainfall with distance. This finding was based on a 15-min average and was even less for shorter averaging periods. The probability and cumulative distributions of a gamma-fitted DSD and integral rain parameters between the three disdrometers had a very good agreement and no major variability. This was mainly due to the high percentage of light stratiform rain and to the number of storms that traveled along the track of the disdrometers. At a fixed time step, however, both DSDs and integral rain parameters showed substantial variability. The standard deviation (SD) of rain rate was near 3 mm/h, while the SD of reflectivity exceeded 3 dBZ at the longest separation distance. These standard deviations were at 6-min average and were higher at shorter averaging periods. The correlations decreased with increasing separation distance. For rain rate, the correlations were higher than previous gauge-based studies. This was attributed to the differences in data processing and the difference in rainfall characteristics in different climate regions. It was also considered that the gauge sampling errors could be a factor. In this regard, gauge measurements were simulated employing existing disdrometer dataset. While a difference was noticed in cumulative distribution of rain occurrence between the simulated gauge and disdrometer observations, the correlations in simulated gauge measurements did not differ from the disdrometer measurements.

Tokay, Ali↗
DOE OSTI2025

Towards robust surrogate models: Benchmarking machine learning approaches to expediting phase field simulations of brittle fracture

Data-driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning techniques have the potential to provide a much needed speedup that would enable progress in areas such as multi-scale modeling and uncertainty quantification. Currently, phase field modeling (PFM) of fracture is one such approach that offers a convenient variational formulation to model crack nucleation, branching and propagation. To date, machine learning techniques have shown promise in approximating PFM simulations. While standard fracture benchmarks represent realistic scenarios frequently observed in practice, they typically do not provide sufficiently challenging tests for data-driven methods. Here, to address this gap, we introduce a challenging dataset based on PFM simulations designed to benchmark and advance ML methods for fracture modeling. This dataset includes three energy decomposition methods, two boundary conditions, and 1000 random initial crack configurations for a total of 6000 simulations. Each sample contains 100 time steps capturing the temporal evolution of the crack field. Alongside this dataset, we also implement and evaluate Physics Informed Neural Networks (PINN), Fourier Neural Operators (FNO), and UNet models as baselines, and explore the impact of ensembling strategies on prediction accuracy. With this combination of our dataset and baseline models drawn from the literature we aim to provide a standardized and challenging benchmark for evaluating machine learning approaches to solid mechanics. Our results highlight both the promise and limitations of popular current models, and demonstrate the utility of this dataset as a testbed for advancing machine learning in fracture mechanics research.

Benchmark dataset↗
DOE OSTI2018

WearGP: A computationally efficient machine learning framework for local erosive wear predictions via nodal Gaussian processes

Computational fluid dynamics (CFD)-based wear predictions are computationally expensive to evaluate, even with a high-performance computing infrastructure. Thus, it is difficult to provide accurate local wear predictions in a timely manner. Data-driven approaches provide a more computationally efficient way to approximate the CFD wear predictions without running the actual CFD wear models. In this paper, a machine learning (ML) approach, termed WearGP, is presented to approximate the 3D local wear predictions, using numerical wear predictions from steady-state CFD simulations as training and testing datasets. The proposed framework is built on Gaussian process (GP) and utilized to predict wear in a much shorter time. The WearGP framework can be segmented into three stages. At the first stage, the training dataset is built by using a number of CFD simulations in the order of O (10 2 ). At the second stage, the data cleansing and data mining process are performed, where the nodal wear solutions are extracted from the solution database to build a training dataset. At the third stage, the wear predictions are made, using trained GP processes. Two CFD case studies including 3D slurry pump impeller and casing are used to demonstrate the WearGP framework, in which 144 training and 40 testing data points are used to train and test the proposed method, respectively. The numerical accuracy, computational effiency and effectiveness between the WearGP framework and CFD wear model for both slurry pump impellers and casings are compared. It is shown that the WearGP framework can achieve highly accurate results that are comparable with the CFD results, with a relatively small size training dataset, with a computational time reduction in an order of 10 5 to 10 6 .

42 ENGINEERING↗
DOE OSTI2024

Precise cosmological constraints from BOSS galaxy clustering with a simulation-based emulator of the wavelet scattering transform

For this study, we perform a reanalysis of the BOSS CMASS DR12 galaxy dataset using a simulation-based emulator for the wavelet scattering transform (WST) coefficients. Moving beyond our previous works, which laid the foundation for the first galaxy clustering application of this estimator, we construct a neural net-based emulator for the cosmological dependence of the WST coefficients and the 2-point correlation function multipoles, trained from the state-of-the-art suite of abacussummit simulations combined with a flexible halo occupation distribution (HOD) galaxy model. In order to confirm the accuracy of our pipeline, we subject it to a series of thorough internal and external mock parameter recovery tests, before applying it to reanalyze the CMASS observations in the redshift range 0.46 < z < 0.57. We find that a joint WST+2-point correlation function likelihood analysis allows us to obtain marginalized 1⁢σ errors on the Λ⁢ CDM parameters that are tighter by a factor of 2.5–6, compared to the 2-point correlation function, and by a factor of 1.4–2.5 compared to the WST-only results. This corresponds to a competitive 0.9%, 2.3% and 1% level of determination for parameters ω c , ⁢σ 8 &n s , respectively, and also to a 0.7% and 2.5% constraint on derived parameters h and ƒ⁡(z)⁢⁢σ 8 ⁡(z), in agreement with the Planck 2018 results. Our results reaffirm the constraining power of the WST and highlight the exciting prospect of employing higher-order statistics in order to fully exploit the power of upcoming stage-IV spectroscopic observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Typical and extreme weather datasets for studying the resilience of buildings to climate change and heatwaves

We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001–2020), future mid-term (2041–2060), and future long-term (2081–2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 “Resilient Cooling for Buildings”, are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Importance of Being Adaptable: An Exploration of the Power and Limitations of Domain Adaptation for Simulation-Based Inference with Galaxy Clusters

The application of deep machine learning methods in astronomy has exploded in the last decade, with new models showing remarkably improved performance on benchmark tasks. Not nearly enough attention is given to understanding the models' robustness, especially when the test data are systematically different from the training data, or "out of domain." Domain shift poses a significant challenge for simulation-based inference, where models are trained on simulated data but applied to real observational data. In this paper, we explore domain shift and test domain adaptation methods for a specific scientific case: simulation-based inference for estimating galaxy cluster masses from X-ray profiles. We build datasets to mimic simulation-based inference: a training set from the Magneticum simulation, a scatter-augmented training set to capture uncertainties in scaling relations, and a test set derived from the IllustrisTNG simulation. We demonstrate that the Test Set is out of domain in subtle ways that would be difficult to detect without careful analysis. We apply three deep learning methods: a standard neural network (NN), a neural network trained on the scatter-augmented input catalogs, and a Deep Reconstruction-Regression Network (DRRN), a semi-supervised deep model engineered to address domain shift. Although the NN improves results by 17% in the Training Data, it performs 40% worse on the out-of-domain Test Set. Surprisingly, the Scatter-Augmented Neural Network (SANN) performs similarly. While the DRRN is successful in mapping the training and Test Data onto the same latent space, it consistently underperforms compared to a straightforward Yx scaling relation. These results serve as a warning that simulation-based inference must be handled with extreme care, as subtle differences between training simulations and observational data can lead to unforeseen biases creeping into the results.

Ntampaka, Michelle [Baltimore, Space Telescope Sci↗
NASA NTRS2016

Comparison of Radiative Energy Flows in Observational Datasets and Climate Modeling

This study examines radiative flux distributions and local spread of values from three major observational datasets (CERES, ISCCP, and SRB) and compares them with results from climate modeling (CMIP3). Examinations of the spread and differences also differentiate among contributions from cloudy and clear-sky conditions. The spread among observational datasets is in large part caused by noncloud ancillary data. Average differences of at least 10Wm(exp -2) each for clear-sky downward solar, upward solar, and upward infrared fluxes at the surface demonstrate via spatial difference patterns major differences in assumptions for atmospheric aerosol, solar surface albedo and surface temperature, and/or emittance in observational datasets. At the top of the atmosphere (TOA), observational datasets are less influenced by the ancillary data errors than at the surface. Comparisons of spatial radiative flux distributions at the TOA between observations and climate modeling indicate large deficiencies in the strength and distribution of model-simulated cloud radiative effects. Differences are largest for lower-altitude clouds over low-latitude oceans. Global modeling simulates stronger cloud radiative effects (CRE) by +30Wmexp -2) over trade wind cumulus regions, yet smaller CRE by about -30Wm(exp -2) over (smaller in area) stratocumulus regions. At the surface, climate modeling simulates on average about 15Wm(exp -2) smaller radiative net flux imbalances, as if climate modeling underestimates latent heat release (and precipitation). Relative to observational datasets, simulated surface net fluxes are particularly lower over oceanic trade wind regions (where global modeling tends to overestimate the radiative impact of clouds). Still, with the uncertainty in noncloud ancillary data, observational data do not establish a reliable reference.

Raschke, Ehrhard↗
DOE OSTI2025

Deep Learning-based Surrogate Model for Efficient Reservoir Simulation in Large-scale Geological Carbon Storage: Application in IBDP Dataset

This project introduces an advanced deep learning (DL)-based surrogate modeling approach to enhance the efficiency and accuracy of large-scale geological carbon storage (GCS) simulations. Using the Illinois Basin Decatur Project (IBDP) dataset as training data, the study employs a residual U-Net architecture to predict critical state variables such as pressure and CO₂ saturation, as well as CO₂ plume migration. By incorporating key geological parameters (e.g., porosity, permeability, and rock facies) and physics-informed inputs like the diffusive time of flight and time step, the DL model effectively reduces computational complexity while maintaining robust physical constraints. Compared to traditional simulators like Eclipse, the DL model achieves remarkable accuracy, with a root mean square error (RMSE) of 1.57 psi for pressure and 0.007 for saturation, and dramatically reduces computational time from hours to just 69.9 seconds for 50-step simulations. These results demonstrate the potential of innovative DL methodologies to improve the predictivity and operational efficiency of GCS simulations, providing a reliable foundation for decision-making in CCS operations. Supported by the SMART initiative, this project underscores the success of leveraging computational innovations to advance CCS technologies.

advanced deep learning↗
DOE OSTI2023

Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis

Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this paper, we propose a technique that addresses storage costs while improving post-analysis accuracy through spatiotemporal adaptive, error-controlled lossy compression. We investigate the trade-off between data precision and temporal output rates, revealing that reducing data precision and increasing timestep frequency lead to more accurate analysis outcomes. Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor. To evaluate our approach, we conduct experiments using the well-known E3SM climate simulation code and apply our method to compress variables used for cyclone tracking. Our results show a significant reduction in storage size while enhancing the quality of cyclone tracking analysis, both quantitatively and qualitatively, in comparison to the prevalent timestep decimation approach. Compared to three state-of-the-art lossy compressors lacking feature preservation capabilities, our adaptive compression framework improves perfectly matched cases in TC tracking by 26.4-51.3% at medium compression ratios and by 77.3-571.1% at large compression ratios, with a merely 5–11% computational overhead.

Gong, Qian↗
DOE OSTI2012

A global coupled Eulerian-Lagrangian model and 1 1 km CO2 surface flux dataset for high-resolution atmospheric CO2 transport simulations

Abstract. We designed a method to simulate atmospheric CO2 concentrations at several continuous observation sites around the globe using surface fluxes at a very high spatial resolution. The simulations presented in this study were performed using the Global Eulerian-Lagrangian Coupled Atmospheric model (GELCA), comprising a Lagrangian particle dispersion model coupled to a global atmospheric tracer transport model with prescribed global surface CO2 flux maps at a 1 1 km resolution. The surface fluxes used in the simulations were prepared by assembling the individual components of terrestrial, oceanic and fossil fuel CO2 fluxes. This experimental setup (i.e. a transport model running at a medium resolution, coupled to a high-resolution Lagrangian particle dispersion model together with global surface fluxes at a very high resolution), which was designed to represent high-frequency variations in atmospheric CO2 concentration, has not been reported at a global scale previously. Two sensitivity experiments were performed: (a) using the global transport model without coupling to the Lagrangian dispersion model, and (b) using the coupled model with a reduced resolution of surface fluxes, in order to evaluate the performance of Eulerian-Lagrangian coupling and the role of high-resolution fluxes in simulating high-frequency variations in atmospheric CO2 concentrations. A correlation analysis between observed and simulated atmospheric CO2 concentrations at selected locations revealed that the inclusion of both Eulerian-Lagrangian coupling and highresolution fluxes improves the high-frequency simulations of the model. The results highlight the potential of a coupled Eulerian-Lagrangian model in simulating high-frequency atmospheric CO2 concentrations at many locations worldwide. The model performs well in representing observations of atmospheric CO2 concentrations at high spatial and temporal resolutions, especially for coastal sites and sites located close to sources of large anthropogenic emissions. While this study focused on simulations of CO2 concentrations, the model could be used for other atmospheric compounds with known estimated emissions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Patch-level CO2 and CH4 fluxes and porewater concentrations in experimental wetlands, 5 and 10 PPT saltwater intrusion simulations, Louisiana 2023-2024

This dataset containes carbon dioxide (CO2) and methane (CH4) flux measurements collected from wetland vegetation patches dominated by Typha domingensis and Panicum hemitomon to assess greenhouse gas flux responses to experimental saltwater intrusion (SWI) pulses. Measurements were conducted before, during, and after simulated SWI events at target salinities of approximately 5 parts per thousand (ppt) with durations of 6, 10, and 17 days and 10 ppt with a duration of 48 days, alongside a control wetland (with no salinity added, flood manipulation only). These data were generated to evaluate how the magnitude and duration of SWI alter wetland carbon exchange and related biogeochemical and plant responses. This data package includes flux measurements from the wetland surface (i.e, soil/water surface and enclosed vegetation) and from the soil/water surface only; porewater and surface water concentrations of CO2 and CH4; salinity, pH, electrical conductivity collected in porewater (at 5, 10, and 20 cm soil depths) and in surface water; soil redox potential; leaf spectral indices, leaf vapor pressure deficit, stomatal conductance; water level, salinity, and photosynthetically active radiation; and aboveground biomass.

EARTH SCIENCE > AGRICULTURE > SOILS > SOIL RESPIRA↗
DOE OSTI2026

From models to reality: a systematic review on simulated and measured residential heat pump energy savings

High-performance HVAC solutions are central to residential energy management. A substantial share of these are electric, reversible-cycle systems, with heat pumps representing the largest portion of current and near-term adoption. This review synthesizes peer-reviewed and grey literature on residential space heating and cooling heat pumps. The academic literature is dominated by modeling (73.8%), with limited field measurement (13.1%). Grey literature from United States serve as a supplemental resource providing measured savings. Conversions from electric-resistance heating consistently show the largest site energy reductions, while oil/propane baselines yield moderate savings, and gas baseline scenario often deliver small and region-dependent savings. This study cross-checks the grey literature measured data with simulation data filtered from the ResStock dataset. The comparison indicates a discrepancy between simulations and measured data: simulated site EUIs are typically lower than measured EUIs, but percentage energy savings fall in similar ranges, implying simulations capture directional effects while underestimating energy use. Factors associated with variability and model–measurement differences include system characterization and control representation (e.g., backup heat engagement, thermostat/setpoint strategies, commissioning/installation quality), occupant behavior, weather normalization, metering scope, and envelope characterization. This paper also outlines the proposed methodology for comparing simulation and measured data for heat pumps. It emphasizes the metrics used for comparison and units harmonization, building characteristics matching, and compact metadata are needed for simulations to match measured data. The proposed methodology is expected to improve the credibility of simulated savings as measured evidence grows.

Yu, Lili↗
DOE OSTI2012

Genometa - A Fast and Accurate Classifier for Short Metagenomic Shotgun Reads

Metagenomic studies use high-throughput sequence data to investigate microbial communities in situ. However, considerable challenges remain in the analysis of these data, particularly with regard to speed and reliable analysis of microbial species as opposed to higher level taxa such as phyla. We here present Genometa, a computationally undemanding graphical user interface program that enables identification of bacterial species and gene content from datasets generated by inexpensive high-throughput short read sequencing technologies. Our approach was first verified on two simulated metagenomic short read datasets, detecting 100% and 94% of the bacterial species included with few false positives or false negatives. Subsequent comparative benchmarking analysis against three popular metagenomic algorithms on an Illumina human gut dataset revealed Genometa to attribute the most reads to bacteria at species level (i.e. including all strains of that species) and demonstrate similar or better accuracy than the other programs. Lastly, speed was demonstrated to be many times that of BLAST due to the use of modern short read aligners. Our method is highly accurate if bacteria in the sample are represented by genomes in the reference sequence but cannot find species absent from the reference. This method is one of the most user-friendly and resource efficient approaches and is thus feasible for rapidly analysing millions of short reads on a personal computer.

59 BASIC BIOLOGICAL SCIENCES↗