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Olson, Robert

Publications and source records attributed to Olson, Robert.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Analysis of the ARTIC Version 3 and Version 4 SARS-CoV-2 Primers and Their Impact on the Detection of the G142D Amino Acid Substitution in the Spike Protein

The ARTIC Network provides a common resource of PCR primer sequences and recommendations for amplifying SARS-CoV-2 genomes. The initial tiling strategy was developed with the reference genome Wuhan-01, and subsequent iterations have addressed areas of low amplification and sequence drop out. Recently, a new version (V4) was released, based on new variant genome sequences, in response to the realization that some V3 primers were located in regions with key mutations. Herein, we compare the performance of the ARTIC V3 and V4 primer sets with a matched set of 663 SARS-CoV-2 clinical samples sequenced with an Illumina NovaSeq 6000 instrument. We observe general improvements in sequencing depth and quality, and improved resolution of the SNP causing the D950N variation in the spike protein. Importantly, we also find nearly universal presence of spike protein substitution G142D in Delta-lineage samples. Due to the prior release and widespread use of the ARTIC V3 primers during the initial surge of the Delta variant, it is likely that the G142D amino acid substitution is substantially underrepresented among early Delta variant genomes deposited in public repositories. In addition to the improved performance of the ARTIC V4 primer set, this study also illustrates the importance of the primer scheme in downstream analyses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes

Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. Further, we demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Trajectory of Growth of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants in Houston, Texas, January through May 2021, Based on 12,476 Genome Sequences

Certain genetic variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are of substantial concern because they may be more transmissible or detrimentally alter the pandemic course and disease features in individual patients. SARS-CoV-2 genome sequences from 12,476 patients in the Houston Methodist health care system diagnosed from January 1 through May 31, 2021 are reported here. Prevalence of the B.1.1.7 (Alpha) variant increased rapidly and caused 63% to 90% of new cases in the latter half of May. Eleven B.1.1.7 genomes had an E484K replacement in spike protein, a change also identified in other SARS-CoV-2 lineages. Compared with non–B.1.1.7-infected patients, individuals with B.1.1.7 had a significantly lower cycle threshold (a proxy for higher virus load) and significantly higher hospitalization rate. Other variants [eg, B.1.429 and B.1.427 (Epsilon), P.1 (Gamma), P.2 (Zeta), and R.1] also increased rapidly, although the magnitude was less than that in B.1.1.7. Twenty-two patients infected with B.1.617.1 (Kappa) or B.1.617.2 (Delta) variants had a high rate of hospitalization. Breakthrough cases (n = 207) in fully vaccinated patients were caused by a heterogeneous array of virus genotypes, including many not currently designated variants of interest or concern. Finally, in the aggregate, this study delineates the trajectory of SARS-CoV-2 variants circulating in a major metropolitan area, documents B.1.1.7 as the major cause of new cases in Houston, TX, and heralds the arrival of B.1.617 variants in the metroplex.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Sequence Analysis of 20,453 Severe Acute Respiratory Syndrome Coronavirus 2 Genomes from the Houston Metropolitan Area Identifies the Emergence and Widespread Distribution of Multiple Isolates of All Major Variants of Concern

Since the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there has been international concern about the emergence of virus variants with mutations that increase transmissibility, enhance escape from the human immune response, or otherwise alter biologically important phenotypes. In late 2020, several variants of concern emerged globally, including the UK variant (B.1.1.7), the South Africa variant (B.1.351), Brazil variants (P.1 and P.2), and two related California variants of interest (B.1.429 and B.1.427). These variants are believed to have enhanced transmissibility. For the South Africa and Brazil variants, there is evidence that mutations in spike protein permit it to escape from some vaccines and therapeutic monoclonal antibodies. On the basis of our extensive genome sequencing program involving 20,453 coronavirus disease 2019 patient samples collected from March 2020 to February 2021, we report identification of all six of these SARS-CoV-2 variants among Houston Methodist Hospital (Houston, TX) patients residing in the greater metropolitan area. Although these variants are currently at relatively low frequency (aggregate of 1.1%) in the population, they are geographically widespread. Houston is the first city in the United States in which active circulation of all six current variants of concern has been documented by genome sequencing. Finally, as vaccine deployment accelerates, increased genomic surveillance of SARS-CoV-2 is essential to understanding the presence, frequency, and medical impact of consequential variants and their patterns and trajectory of dissemination.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A machine learning-based service for estimating quality of genomes using PATRIC

Recent advances in high-volume sequencing technology and mining of genomes from metagenomic samples call for rapid and reliable genome quality evaluation. The current release of the PATRIC database contains over 220,000 genomes, and current metagenomic technology supports assemblies of many draft-quality genomes from a single sample, most of which will be novel. We have added two quality assessment tools to the PATRIC annotation pipeline. EvalCon uses supervised machine learning to calculate an annotation consistency score. EvalG implements a variant of the CheckM algorithm to estimate contamination and completeness of an annotated genome.We report on the performance of these tools and the potential utility of the consistency score. Additionally, we provide contamination, completeness, and consistency measures for all genomes in PATRIC and in a recent set of metagenomic assemblies. EvalG and EvalCon facilitate the rapid quality control and exploration of PATRIC-annotated draft genomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella

Nontyphoidal Salmonella species are the leading bacterial cause of food-borne disease in the United States. Whole genome sequences and paired antimicrobial susceptibility data are available for Salmonella strains because of surveillance efforts from public health agencies. In this study, a collection of 5,278 nontyphoidal Salmonella genomes, collected over 15 years in the United States, were used to generate XGBoost-based machine learning models for predicting minimum inhibitory concentrations (MICs) for 15 antibiotics. The MIC prediction models have an overall average accuracy of 95% within ± 1 two-fold dilution step (confidence interval of 95-95%), an average very major error rate of 2.7% (confidence interval of 2.4-3.0%) and an average major error rate of 0.1% (confidence interval of 0.1-0.2%). The model predicts MICs with no a priori information about the underlying gene content or resistance phenotypes of the strains. By selecting diverse genomes for training sets, we show that highly accurate MIC prediction models can be generated with fewer than 500 genomes. We also show that our approach for predicting MICs is stable over time despite annual fluctuations in antimicrobial resistance gene content in the sampled genomes. Finally, using feature selection, we explore the important genomic regions identified by the models for predicting MICs. To date, this is one of the largest MIC modeling studies to be published. Furthermore, our strategy for developing whole genome sequence-based models for surveillance and clinical diagnostics can be readily applied to other important human pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Programmatic Comparison and Dissemination of an Audit of Single-fraction Radiation Therapy Prescribing Practices for Bone Metastases is Associated with a Meaningful and Lasting Change in Practice on a Population Level

There is ample evidence that single-fraction radiation therapy (SFRT) is as efficacious as more costly and morbid multifraction regimens. We previously demonstrated that an audit-based intervention increased the use of SFRT in all regional cancer centers the following year. However, other investigators have demonstrated that interventions were only associated with a transient 1-year change in prescribing practices. We sought to determine whether our intervention resulted in a more lasting impact.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

Here, antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ± 1 two-fold dilution factor, is 92%. Individual accuracies are >= 90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center

Here, the Pathosystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center. Recent changes to PATRIC include a redesign of the web interface and some new services that provide users with a platform that takes them from raw reads to an integrated analysis experience. The redesigned interface allows researchers direct access to tools and data, and the emphasis has changed to user-created genome-groups, with detailed summaries and views of the data that researchers have selected. Perhaps the biggest change has been the enhanced capability for researchers to analyze their private data and compare it to the available public data. Researchers can assemble their raw sequence reads and annotate the contigs using RASTtk. PATRIC also provides services for RNA-Seq, variation, model reconstruction and differential expression analysis, all delivered through an updated private workspace. Private data can be compared by `virtual integration' to any of PATRIC's public data. The number of genomes available for comparison in PATRIC has expanded to over 80 000, with a special emphasis on genomes with antimicrobial resistance data. PATRIC uses this data to improve both subsystem annotation and k-mer classification, and tags new genomes as having signatures that indicate susceptibility or resistance to specific antibiotics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Antimicrobial resistance prediction in PATRIC and RAST

The emergence and spread of antimicrobial resistance (AMR) mechanisms in bacterial pathogens, coupled with the dwindling number of effective antibiotics, has created a global health crisis. Being able to identify the genetic mechanisms of AMR and predict the resistance phenotypes of bacterial pathogens prior to culturing could inform clinical decision-making and improve reaction time. At PATRIC (http://patricbrc.org/), we have been collecting bacterial genomes with AMR metadata for several years. In order to advance phenotype prediction and the identification of genomic regions relating to AMR, we have updated the PATRIC FTP server to enable access to genomes that are binned by their AMR phenotypes, as well as metadata including minimum inhibitory concentrations. Using this infrastructure, we custom built AdaBoost (adaptive boosting) machine learning classifiers for identifying carbapenem resistance in Acinetobacter baumannii , methicillin resistance in Staphylococcus aureus , and beta-lactam and co-trimoxazole resistance in Streptococcus pneumoniae with accuracies ranging from 88–99%. We also did this for isoniazid, kanamycin, ofloxacin, rifampicin, and streptomycin resistance in Mycobacterium tuberculosis , achieving accuracies ranging from 71–88%. Lastly, this set of classifiers has been used to provide an initial framework for species-specific AMR phenotype and genomic feature prediction in the RAST and PATRIC annotation services.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

PATtyFams: Protein families for the microbial genomes in the PATRIC database

The ability to build accurate protein families is a fundamental operation in bioinformatics that influences comparative analyses, genome annotation, and metabolic modeling. For several years we have been maintaining protein families for all microbial genomes in the PATRIC database (Pathosystems Resource Integration Center, patricbrc.org) in order to drive many of the comparative analysis tools that are available through the PATRIC website. However, due to the burgeoning number of genomes, traditional approaches for generating protein families are becoming prohibitive. In this report, we describe a new approach for generating protein families, which we call PATtyFams. This method uses the k-mer-based function assignments available through RAST (Rapid Annotation using Subsystem Technology) to rapidly guide family formation, and then differentiates the function-based groups into families using a Markov Cluster algorithm (MCL). In conclusion, this new approach for generating protein families is rapid, scalable and has properties that are consistent with alignment-based methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Volumetric Radiosurgery for 1 to 10 Brain Metastases: A Multicenter, Single-Arm, Phase 2 Study

Purpose: Interest is growing in treating multiple brain metastases with radiosurgery. We report on the effectiveness and tolerability of volumetric radiosurgery (VRS). Methods and Materials: We enrolled patients with a ≥6-month estimated life expectancy and 1 to 10 brain metastases with a diameter of ≤3 cm at 5 cancer centers. Volumetric radiosurgery was delivered in 5 fractions with 98% target coverage, prescribed as 95% of 50 Gy (47.5 Gy in 5 fractions) to the metastases with no margin and 95% of 40 Gy (38 Gy in 5 fractions) to their 2-mm planning target volumes, concurrent with 20 Gy to the whole brain planning target volume. The treatment was delivered with daily image guidance using conventional linear accelerators and volumetric modulated arc therapy. A magnetic resonance imaging scan was obtained every 3 months. The primary endpoint was the 3-month objective response in the brain according to the Response Evaluation Criteria in Solid Tumors, version 1.1. The principal secondary endpoint was 1-year actuarial control of treated metastases. Toxicities were graded using the Common Terminology Criteria for Adverse Events, version 4.0. The present study is registered with (ClinicalTrials.gov) ( (clinicaltrials.gov) identifier (NCT01046123)). Results: From July 2010 to May 2013, 60 patients underwent VRS with 47.5 Gy in 5 fractions for 12 metastases in the thalamus and basal ganglia (deep metastases) and 207 non-deep metastases. The median follow-up period was 30.5 months, and the median survival was 10.1 months. For the 43 patients assessable at 3 months, the objective response in the brain was 56%. The treated metastases were controlled in 88% of patients at 1 year and 84% at 3 years. Overall survival did not differ for patients with 4 to 10 versus 1 to 3 metastases (hazard ratio 1.18, P=.6). The crude incidence of severe radionecrosis (grade 3-5) was 25% (3 of 12) per deep metastasis, 1.9% (4 of 219) per non-deep metastasis, and 10% (6 of 60) per patient. Conclusions: For non-deep brain metastases, 47.5 Gy in 5 fractions was tolerable. Volumetric radiosurgery was effective for long-term control of treated brain metastases.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Second Malignancies After Adjuvant Radiation Therapy for Early Stage Breast Cancer: Is There Increased Risk With Addition of Regional Radiation to Local Radiation?

Purpose: This study was undertaken to determine whether there was an increased risk of second malignancies (SM), particularly lung cancer, in early stage breast cancer patients treated with the addition of nodal fields to breast and/or chest wall radiation therapy (RT). Materials and Methods: Subjects were stage I/II female breast cancer patients 20 to 79 years of age, diagnosed between 1989 and 2005 and treated with adjuvant RT at our institution. Patients were included if they survived and did not have SM within 3 years of diagnosis. Standardized incidence ratios (SIR) with 95% confidence intervals (CI) were calculated to compare SM incidence to cancer incidence in the general sex- and age-matched populations. Secondary malignancy risks in patients treated with local RT (LRT) to the breast/chest wall were compared to those in patients treated with locoregional RT (LRRT) to the breast/chest wall and regional nodes, using multivariate regression analysis (MVA) to account for covariates. Results: The cohort included 12,836 patients with a median follow-up of 8.4 years. LRRT was used in 18% of patients. The SIR comparing patients treated with LRT to the general population was 1.29 (CI: 1.21-1.38). No statistically significant increased incidence of in-field malignancies (SIR, 1.04; CI: 0.87-1.23) and lung cancers (SIR, 1.06; CI: 0.88-1.26) was detected. The SIR comparing patients treated with LRRT to the general population was 1.39 (CI: 1.17-1.64). No statistically significant increased incidence of in-field malignancies (SIR, 1.26; CI: 0.77-1.94) and lung cancers (SIR, 1.27; CI: 0.76-1.98) was detected. On MVA comparing LRRT to LRT, the adjusted hazard ratio was 1.20 for in-field malignancies (CI: 0.68-2.16) and 1.26 for lung cancer (CI: 0.67-2.36). The excess attributable risk (EAR) to regional RT was 3.1 per 10,000 person years (CI: −8.7 to 9.9). Conclusions: No statistically significant increased risk of second malignancy was detected after LRRT relative to that for LRT. The upper limit of the EAR was approximately 1% at 10 years.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

RASTtk: A modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes

The RAST (Rapid Annotation using Subsystem Technology) annotation engine was built in 2008 to annotate bacterial and archaeal genomes. It works by offering a standard software pipeline for identifying genomic features (i.e., protein-encoding genes and RNA) and annotating their functions. Recently, in order to make RAST a more useful research tool and to keep pace with advancements in bioinformatics, it has become desirable to build a version of RAST that is both customizable and extensible. In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation pipelines. RASTtk offers a choice of software for identifying and annotating genomic features as well as the ability to add custom features to an annotation job. RASTtk also accommodates the batch submission of genomes and the ability to customize annotation protocols for batch submissions. This is the first major software restructuring of RAST since its inception.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Should Women Younger Than 40 Years of Age With Invasive Breast Cancer Have a Mastectomy?: 15-Year Outcomes in a Population-Based Cohort

Purpose: Optimal local management for young women with early-stage breast cancer remains controversial. This study examined 15-year outcomes among women younger than 40 years treated with breast-conserving surgery plus whole-breast radiation therapy (BCT) compared with those treated with modified radical mastectomy (MRM). Methods and Materials: Women aged 20 to 39 years with early-stage breast cancer diagnosed between 1989 and 2003 were identified in a population-based database. Primary outcomes of breast cancer–specific survival (BCSS), overall survival (OS) and secondary outcomes of local relapse–free survival (LRFS), locoregional relapse–free survival (LRRFS), and distant relapse–free survival (DRFS) were calculated using Kaplan-Meier methods and compared between BCT and MRM cohorts using log-rank tests. A planned subgroup analysis was performed on patients considered “ideal” for BCT (ie, T1N0, negative margins and no extensive ductal carcinoma in situ) and in whom local therapy may have the largest impact on survival because of low systemic risk. Results: 965 patients were identified; 616 had BCT and 349 had MRM. The median follow-up time was 14.4 years (range, 8.4-23.3 years). Overall, 15-year rates of BCSS (76.0% vs 74.1%, P=.62), OS (74.2% vs 73.0%, P=.75), LRFS (85.4% vs 86.5%, P=.95), LRRFS (82.2% vs 81.6%, P=.61), and DRFS (74.4% vs 71.6%, P=.40) were similar between the BCT and MRM cohorts. In the “ideal” for BCT subgroup, there were 219 BCT and 67 MRM patients with a median follow-up time of 15.5 years. The 15-year BCSS (86.1% vs 82.9%, P=.57), OS (82.6% vs 82.9%, P=.89), LRFS (86.2% vs 84.2%, P=.50), LRRFS (83.1% vs 78.3%, P=.24), and DRFS (84.8% vs 79.1%, P=.17) were similar in the BCT and MRM cohorts. Conclusions: This population-based analysis with long-term follow-up confirmed that women younger than 40 years treated with BCT had similar 15-year outcomes compared with MRM. Young age alone is not a contraindication to BCT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST)

In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast. nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources.

59 BASIC BIOLOGICAL SCIENCES↗