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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Voxel carving–based 3D reconstruction of sorghum identifies genetic determinants of light interception efficiency

Changes in canopy architecture traits have been shown to contribute to yield increases. Optimizing both light interception and light interception efficiency of agricultural crop canopies will be essential to meeting the growing food needs. Canopy architecture is inherently three-dimensional (3D), but many approaches to measuring canopy architecture component traits treat the canopy as a two-dimensional (2D) structure to make large scale measurement, selective breeding, and gene identification logistically feasible. We develop a high throughput voxel carving strategy to reconstruct 3D representations of sorghum from a small number of RGB photos. Our approach builds on the voxel carving algorithm to allow for fully automatic reconstruction of hundreds of plants. It was employed to generate 3D reconstructions of individual plants within a sorghum association population at the late vegetative stage of development. Light interception parameters estimated from these reconstructions enabled the identification of known and previously unreported loci controlling light interception efficiency in sorghum. The approach is generalizable and scalable, and it enables 3D reconstructions from existing plant high throughput phenotyping datasets. We also propose a set of best practices to increase 3D reconstructions’ accuracy.

3D plant reconstruction↗
DOE OSTI2020

Comparative genetics of Enterococcus faecalis intestinal tissue isolates before and after surgery in a rat model of colon anastomosis

We have recently demonstrated that collagenolytic Enterococcus faecalis plays a key and causative role in the pathogenesis of anastomotic leak, an uncommon but potentially lethal complication characterized by disruption of the intestinal wound following segmental removal of the colon (resection) and its reconnection (anastomosis). Here we hypothesized that comparative genetic analysis of E. faecalis isolates present at the anastomotic wound site before and after surgery would shed insight into the mechanisms by which collagenolytic strains are selected for and predominate at sites of anastomotic disruption. Whole genome optical mapping of four pairs of isolates from rat colonic tissue obtained following surgical resection (herein named "pre-op" isolates) and then 6 days later from the anastomotic site (herein named "post-op" isolates) demonstrated that the isolates with higher collagenolytic activity formed a distinct cluster. In order to perform analysis at a deeper level, a single pair of E. faecalis isolates (16A pre-op and 16A post-op) was selected for whole genome sequencing and assembled using a hybrid assembly algorithm. Comparative genomics demonstrated absence of multiple gene clusters, notably a pathogenicity island in the post-op isolate. No differences were found in the fsr-gelE-sprE genes (EF1817-1822) responsible for regulation and production of collagenolytic activity. Analysis of unique genes among the 16A pre-op and post-op isolates revealed the predominance of transporter systems-related genes in the pre-op isolate and phage-related and hydrolytic enzyme-encoding genes in the post-op isolate. Despite genetic differences observed between pre-op and post-op isolates, the precise genetic determinants responsible for their differential expression of collagenolytic activity remains unknown.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

High–throughput measurement of plant fitness traits with an object detection method using Faster R–CNN

Revealing the contributions of genes to plant phenotype is frequently challenging because loss-of-function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. An image segmentation-based method using the software ImageJ and an object detection-based method using the Faster Region-based Convolutional Neural Network (R-CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation-based method was error-prone (correlation between true and predicted seed counts, r 2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection-based algorithm yielded near perfect seed counts (r 2 = 0.9996) and highly accurate fruit counts (r 2 = 0.980). Comparing seed counts for wild-type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Expression profiles of cell-wall related genes vary broadly between two common maize inbreds during stem development

The cellular machinery for cell wall synthesis and metabolism is encoded by members of large multi-gene families. Maize is both a genetic model for grass species and a potential source of lignocellulosic biomass from crop residues. Genetic improvement of maize for its utility as a bioenergy feedstock depends on identification of the specific gene family members expressed during secondary wall development in stems. High-throughput sequencing of transcripts expressed in developing rind tissues of stem internodes provided a comprehensive inventory of cell wall-related genes in maize ( Zea mays , cultivar B73). Of 1239 of these genes, 854 were expressed among the internodes at =95 reads per 20 M, and 693 of them at =500 reads per 20 M. Grasses have cell wall compositions distinct from non-commelinid species; only one-quarter of maize cell wall-related genes expressed in stems were putatively orthologous with those of the eudicot Arabidopsis. Using a slope-metric algorithm, five distinct patterns for sub-sets of co-expressed genes were defined across a time course of stem development. For the subset of genes associated with secondary wall formation, fifteen sequence motifs were found in promoter regions. The same members of gene families were often expressed in two maize inbreds, B73 and Mo17, but levels of gene expression between them varied, with 30% of all genes exhibiting at least a 5-fold difference at any stage. Although presence-absence and copy-number variation might account for much of these differences, fold-changes of expression of a CADa and a FLA11 gene were attributed to polymorphisms in promoter response elements. Large genetic variation in maize as a species precludes the extrapolation of cell wall-related gene expression networks even from one common inbred line to another. Elucidation of genotype-specific expression patterns and their regulatory controls will be needed for association panels of inbreds and landraces to fully exploit genetic variation in maize and other bioenergy grass species.

09 BIOMASS FUELS↗
DOE OSTI2018

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specifc contextual clues and requires no training. This approach generalizes across diferent modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal refectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fdelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufcient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Publisher Correction: Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

Shahbazi, Ali↗
DOE OSTI2021

Image-Based Methods to Score Fungal Pathogen Symptom Progression and Severity in Excised Arabidopsis Leaves

Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (Fv/Fm) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks

Abstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.

Gonzalez, Emmanuel M.↗
DOE OSTI2023

Chaconne: A Statistical Approach to Nonlocal Compression for Supervised Learning, Semi-Supervised Learning, and Anomaly Detection

This project developed a novel statistical understanding of compression analytics (CA), which has challenged and clarified some core assumptions about CA, and enabled the development of novel techniques that address vital challenges of national security. Specifically, this project has yielded the development of novel capabilities including 1. Principled metrics for model selection in CA, 2. Techniques for deriving/applying optimal classification rules and decision theory to supervised CA, including how to properly handle class imbalance and differing costs of misclassification, 3. Two techniques for handling nonlocal information in CA, 4. A novel technique for unsupervised CA that is agnostic with regard to the underlying compression algorithm, 5. A framework for semisupervised CA when a small number of labels are known in an otherwise large unlabeled dataset. 6. The academic alliance component of this project has focused on the development of a novel exemplar-based Bayesian technique for estimating variable length Markov models (closely related to PPM [prediction by partial matching] compression techniques). We have developed examples illustrating the application of our work to text, video, genetic sequences, and unstructured cybersecurity log files.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

In‐depth comparative analysis of Illumina ® MiSeq run metrics: Development of a wet‐lab quality assessment tool

Abstract Whole genome sequencing of bacterial isolates has become a daily task in many laboratories, generating incredible amounts of data. However, data acquisition is not an end in itself; the goal is to acquire high‐quality data useful for understanding genetic relationships. Having a method that could rapidly determine which of the many available run metrics are the most important indicators of overall run quality and having a way to monitor these during a given sequencing run would be extremely helpful to this effect. Therefore, we compared various run metrics across 486 MiSeq runs, from five different machines. By performing a statistical analysis using principal components analysis and a K ‐means clustering algorithm of the metrics, we were able to validate metric comparisons among instruments, allowing for the development of a predictive algorithm, which permits one to observe whether a given MiSeq run has performed adequately. This algorithm is available in an Excel spreadsheet: that is, MiSeq Instrument & Run (In‐Run) Forecast. Our tool can help verify that the quantity/quality of the generated sequencing data consistently meets or exceeds recommended manufacturer expectations. Patterns of deviation from those expectations can be used to assess potential run problems and plan preventative maintenance, which can save valuable time and funding resources.

Kastanis, George John↗
DOE OSTI2020

A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates

ABSTRACT: Variation in the genome of Pseudomonas aeruginosa , an important pathogen, can have dramatic impacts on the bacterium’s ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm’s performance, suggesting instead that virulence predictions are derived from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single-nucleotide variants and whole-genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes. IMPORTANCE Pseudomonas aeruginosa is a clinically important Gram-negative opportunistic pathogen. P. aeruginosa shows a large degree of genomic heterogeneity both through variation in sequences found throughout the species (core genome) and through the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genomes are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Detecting macroevolutionary genotype–phenotype associations using error-corrected rates of protein convergence

On macroevolutionary timescales, extensive mutations and phylogenetic uncertainty mask the signals of genotype–phenotype associations underlying convergent evolution. To overcome this problem, we extended the widely used framework of non-synonymous to synonymous substitution rate ratios and developed the novel metric ω C , which measures the error-corrected convergence rate of protein evolution. While ω C distinguishes natural selection from genetic noise and phylogenetic errors in simulation and real examples, its accuracy allows an exploratory genome-wide search of adaptive molecular convergence without phenotypic hypothesis or candidate genes. Using gene expression data, we explored over 20 million branch combinations in vertebrate genes and identified the joint convergence of expression patterns and protein sequences with amino acid substitutions in functionally important sites, providing hypotheses on undiscovered phenotypes. We further extended our method with a heuristic algorithm to detect highly repetitive convergence among computationally non-trivial higher-order phylogenetic combinations. Our approach allows bidirectional searches for genotype–phenotype associations, even in lineages that diverged for hundreds of millions of years.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Quantitative phase measurements of human cell nuclei using X-ray ptychography

The human cell nucleus serves as an important organelle holding the genetic blueprint for life. In this work, X-ray ptychography was applied to assess the masses of human cell nuclei using its unique phase shift information. Measurements were carried out at the I13-1 beamline at the Diamond Light Source that has extremely large transverse coherence properties. Furthermore, the ptychographic diffractive imaging approach allowed imaging of large structures that gave quantitative measurements of the phase shift in 2D projections. In this paper a modified ptychography algorithm that improves the quality of the reconstruction for weak scattering samples is presented. The application of this approach to calculate the mass of several human nuclei is also demonstrated.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

SCOPe: improvements to the structural classification of proteins – extended database to facilitate variant interpretation and machine learning

Abstract The Structural Classification of Proteins—extended (SCOPe, https://scop.berkeley.edu) knowledgebase aims to provide an accurate, detailed, and comprehensive description of the structural and evolutionary relationships amongst the majority of proteins of known structure, along with resources for analyzing the protein structures and their sequences. Structures from the PDB are divided into domains and classified using a combination of manual curation and highly precise automated methods. In the current release of SCOPe, 2.08, we have developed search and display tools for analysis of genetic variants we mapped to structures classified in SCOPe. In order to improve the utility of SCOPe to automated methods such as deep learning classifiers that rely on multiple alignment of sequences of homologous proteins, we have introduced new machine-parseable annotations that indicate aberrant structures as well as domains that are distinguished by a smaller repeat unit. We also classified structures from 74 of the largest Pfam families not previously classified in SCOPe, and we improved our algorithm to remove N- and C-terminal cloning, expression and purification sequences from SCOPe domains. SCOPe 2.08-stable classifies 106 976 PDB entries (about 60% of PDB entries).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Impact of genotype‐calling methodologies on genome‐wide association and genomic prediction in polyploids

Abstract Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost‐efficient genotyping using next‐generation sequencing. However, accurate genotype calling from next‐generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome‐wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype‐calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum , and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype‐calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost‐efficiently implement GWAS and genomic prediction in polyploid crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Machine learning prediction of connectivity, biodiversity and resilience in the Coral Triangle

Abstract Even optimistic climate scenarios predict catastrophic consequences for coral reef ecosystems by 2100. Understanding how reef connectivity, biodiversity and resilience are shaped by climate variability would improve chances to establish sustainable management practices. In this regard, ecoregionalization and connectivity are pivotal to designating effective marine protected areas. Here, machine learning algorithms and physical intuition are applied to sea surface temperature anomaly data over a twenty-four-year period to extract ecoregions and assess connectivity and bleaching recovery potential in the Coral Triangle and surrounding oceans. Furthermore, the impacts of the El Niño Southern Oscillation (ENSO) on biodiversity and resilience are quantified. We find that resilience is higher for reefs north of the Equator and that the extraordinary biodiversity of the Coral Triangle is dynamic in time and space, and benefits from ENSO. The large-scale exchange of genetic material is enhanced between the Indian Ocean and the Coral Triangle during La Niña years, and between the Coral Triangle and the central Pacific in neutral conditions. Through machine learning the outstanding biodiversity of the Coral Triangle, its evolution and the increase of species richness are contextualized through geological times, while offering new hope for monitoring its future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Kraken2 Metagenomic Virus Database

The Database: Kraken2 [1] database built from a classification tree containing over 700k metagenomic viruses from JGI IMG/VR [2]. (1) Wood, D. E., Lu, J., and Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biol., 20(1), 1–13. doi: 10.1186/s13059-019-1891-0 (2) Paez-Espino D, Chen I-MA, Palaniappan K, Ratner A, Chu K, Szeto E, et al. IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses. Nucleic Acids Res. 2017;45:D457-65. For Paper: Title: A k-mer based approach for virus classification in metatranscriptomic and metagenomic samples identifies viral associations in the Populus phytobiome and autism brains Abstract Background Viruses are an underrepresented taxa in the study and identification of microbiome constituents; however, they play an important role in health, microbiome regulation, and transfer of genetic material. Only a few thousand viruses have been isolated, sequenced, and assigned a taxonomy, which further limits the ability to identify and quantify viruses in the microbiome. Additionally, the vast diversity of viruses represents a challenge for classification, not only in constructing a viral taxonomy, but also in identifying similarities between a virus' genotype and its phenotype. However, the diversity of viral sequences can be leveraged to classify their sequences in metagenomic and metatranscriptomic samples. Methods To identify viruses in transcriptomic and genomic samples, we developed a dynamic programming algorithm for creating a classification tree out of 715,672 metagenome viruses. To create the classification tree, we clustered proportional similarity scores generated from the k-mer profiles of each of the metagenome viruses. We then integrated the viral classification tree with the NCBI taxonomy for use with ParaKraken, a metagenomic/transcriptomic classifier. Results To illustrate the breadth of our utility for classifying viruses with ParaKraken, we analyzed data from a plant metagenome study identifying the differences between two Populus genotypes in three different compartments and on a human metatranscriptome study identifying the differences between Autism Spectrum Disorder patients and controls in post mortem brain biopsies. In the Populus study, we identified genotype and compartment specific viral signatures, while in the Autism study we identified a significant increased abundance of eight viral sequences in Autism brain biopsies. Conclusion Viruses represent an important aspect of the microbiome. The ability to classify viruses represents the first step in being able to better understand their role in the microbiome. The viral classification method presented here allows for more complete identification of viral sequences for use in identifying associations between viruses and the host and viruses and other microbiome members. Acknowledgements and Funding This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research was also supported by the Plant-Microbe Interfaces Scientific Focus Area in the Genomic Science Program, the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science, and by the Department of Energy, Laboratory Directed Research and Development funding (ProjectID 8321), at the Oak Ridge National Laboratory. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725. This research used resources of the Compute and Data Environment for Science (CADES).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A k-mer based approach for classifying viruses without taxonomy identifies viral associations in human autism and plant microbiomes

Viruses are an underrepresented taxa in the study and identification of microbiome constituents; however, they play an essential role in health, microbiome regulation, and transfer of genetic material. Only a few thousand viruses have been isolated, sequenced, and assigned a taxonomy, which limits the ability to identify and quantify viruses in the microbiome. Additionally, the vast diversity of viruses represents a challenge for classification, not only in constructing a viral taxonomy, but also in identifying similarities between a virus’ genotype and its phenotype. However, the diversity of viral sequences can be leveraged to classify their sequences in metagenomic and metatranscriptomic samples, even if they do not have a taxonomy. To identify and quantify viruses in transcriptomic and genomic samples, we developed a dynamic programming algorithm for creating a classification tree out of 715,672 metagenome viruses. To create the classification tree, we clustered proportional similarity scores generated from the k-mer profiles of each of the metagenome viruses to create a database of metagenomic viruses. The resulting Kraken2 database of the metagenomic viruses can be found here: https://www.osti.gov/biblio/1615774 and is compatible with Kraken2. We then integrated the viral classification database with databases created with genomes from NCBI for use with ParaKraken (a parallelized version of Kraken provided in Supplemental Zip 1), a metagenomic/transcriptomic classifier. To illustrate the breadth of our utility for classifying metagenome viruses, we analyzed data from a plant metagenome study identifying genotypic and compartment specific differences between two Populus genotypes in three different compartments. We also identified a significant increase in abundance of eight viral sequences in post mortem brains in a human metatranscriptome study comparing Autism Spectrum Disorder patients and controls. We also show the potential accuracy for classifying viruses by utilizing both the JGI and NCBI viral databases to identify the uniqueness of viral sequences. Finally, we validate the accuracy of viral classification with NCBI databases containing viruses with taxonomy to identify pathogenic viruses in known COVID-19 and cassava brown streak virus infection samples. Our method represents the compulsory first step in better understanding the role of viruses in the microbiome by allowing for a more complete identification of sequences without taxonomy. Better classification of viruses will improve identifying associations between viruses and their hosts as well as viruses and other microbiome members. Despite the lack of taxonomy, this database of metagenomic viruses can be used with any tool that utilizes a taxonomy, such as Kraken, for accurate classification of viruses.

59 BASIC BIOLOGICAL SCIENCES↗