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Article ; Online: EPIC-CoGe: managing and analyzing genomic data.

Nelson, Andrew D L / Haug-Baltzell, Asher K / Davey, Sean / Gregory, Brian D / Lyons, Eric

2018 Volume 34, Issue 15, Page(s) 2651–2653

Abstract: Summary: The EPIC-CoGe browser is a web-based genome visualization utility that integrates the GMOD JBrowse genome browser with the extensive CoGe genome database (currently containing over 30 000 genomes). In addition, the EPIC-CoGe browser boasts many ...

Abstract	Summary: The EPIC-CoGe browser is a web-based genome visualization utility that integrates the GMOD JBrowse genome browser with the extensive CoGe genome database (currently containing over 30 000 genomes). In addition, the EPIC-CoGe browser boasts many additional features over basic JBrowse, including enhanced search capability and on-the-fly analyses for comparisons and analyses between all types of functional and diversity genomics data. There is no installation required and data (genome, annotation, functional genomic and diversity data) can be loaded by following a simple point and click wizard, or using a REST API, making the browser widely accessible and easy to use by researchers of all computational skill levels. In addition, EPIC-CoGe and data tracks are easily embedded in other websites and JBrowse instances. Availability and implementation: EPIC-CoGe Browser is freely available for use online through CoGe (https://genomevolution.org). Source code (MIT open source) is available: https://github.com/LyonsLab/coge. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Data Visualization ; Genome ; Genomics/methods ; Molecular Sequence Annotation ; Sequence Analysis, DNA/methods ; Software
Language	English
Publishing date	2018-02-23
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bty106
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: EPIC-CoGe: managing and analyzing genomic data

Nelson, Andrew D L / Haug-Baltzell, Asher K / Davey, Sean / Gregory, Brian D / Lyons, Eric / Hancock, John

Bioinformatics. 2018 Aug. 01, v. 34, no. 15

2018

Abstract: The EPIC-CoGe browser is a web-based genome visualization utility that integrates the GMOD JBrowse genome browser with the extensive CoGe genome database (currently containing over 30 000 genomes). In addition, the EPIC-CoGe browser boasts many ... ...

Abstract	The EPIC-CoGe browser is a web-based genome visualization utility that integrates the GMOD JBrowse genome browser with the extensive CoGe genome database (currently containing over 30 000 genomes). In addition, the EPIC-CoGe browser boasts many additional features over basic JBrowse, including enhanced search capability and on-the-fly analyses for comparisons and analyses between all types of functional and diversity genomics data. There is no installation required and data (genome, annotation, functional genomic and diversity data) can be loaded by following a simple point and click wizard, or using a REST API, making the browser widely accessible and easy to use by researchers of all computational skill levels. In addition, EPIC-CoGe and data tracks are easily embedded in other websites and JBrowse instances. EPIC-CoGe Browser is freely available for use online through CoGe (https://genomevolution.org). Source code (MIT open source) is available: https://github.com/LyonsLab/coge. Supplementary data are available at Bioinformatics online.
Keywords	Internet ; bioinformatics ; databases ; genome ; genomics
Language	English
Dates of publication	2018-0801
Size	p. 2651-2653.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	1468345-3
ISSN	1460-2059 ; 1367-4811 ; 1367-4803
ISSN (online)	1460-2059 ; 1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bty106
Database	NAL-Catalogue (AGRICOLA)

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Article: Leveraging cyverse resources for De Novo comparative transcriptomics of underserved (non-model) organisms

Joyce, Blake L / Haug-Baltzell, Asher K / Hulvey, Jonathan P / McCarthy, Fiona / Devisetty, Upendra Kumar / Lyons, Eric

Journal of visualized experiments. 2017 May 09, , no. 123

2017

Abstract: This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. ...

Abstract	This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (https://wiki.cyverse.org/wiki/x/dgGtAQ). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.
Keywords	Internet ; cloud computing ; data collection ; gene expression regulation ; genes ; transcriptome ; transcriptomics
Language	English
Dates of publication	2017-0509
Size	p. e55009.
Publishing place	Journal of Visualized Experiments
Document type	Article
ZDB-ID	2259946-0
ISSN	1940-087X
ISSN	1940-087X
DOI	10.3791/55009
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: COVID-19 susceptibility and severity risks in a cross-sectional survey of over 500 000 US adults.

Knight, Spencer C / McCurdy, Shannon R / Rhead, Brooke / Coignet, Marie V / Park, Danny S / Roberts, Genevieve H L / Berkowitz, Nathan D / Zhang, Miao / Turissini, David / Delgado, Karen / Pavlovic, Milos / Haug Baltzell, Asher K / Guturu, Harendra / Rand, Kristin A / Girshick, Ahna R / Hong, Eurie L / Ball, Catherine A

BMJ open

2022 Volume 12, Issue 10, Page(s) e049657

Abstract: Objectives: The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the ...

Abstract	Objectives: The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data. Design: A cross-sectional study. Setting: AncestryDNA customers in the USA who consented to research. Participants: The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors and exposures for over 563 000 adult individuals in the USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test. Results: We replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex and genetic ancestry groups within the study. Conclusions: The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.
MeSH term(s)	Adult ; COVID-19/epidemiology ; Cross-Sectional Studies ; Humans ; Male ; Pandemics ; Risk Factors ; SARS-CoV-2
Language	English
Publishing date	2022-10-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2599832-8
ISSN	2044-6055 ; 2044-6055
ISSN (online)	2044-6055
ISSN	2044-6055
DOI	10.1136/bmjopen-2021-049657
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Expanded COVID-19 phenotype definitions reveal distinct patterns of genetic association and protective effects.

Nature genetics

2022 Volume 54, Issue 4, Page(s) 374–381

Abstract: Multiple COVID-19 genome-wide association studies (GWASs) have identified reproducible genetic associations indicating that there is a genetic component to susceptibility and severity risk. To complement these studies, we collected deep coronavirus ... ...

Abstract	Multiple COVID-19 genome-wide association studies (GWASs) have identified reproducible genetic associations indicating that there is a genetic component to susceptibility and severity risk. To complement these studies, we collected deep coronavirus disease 2019 (COVID-19) phenotype data from a survey of 736,723 AncestryDNA research participants. With these data, we defined eight phenotypes related to COVID-19 outcomes: four phenotypes that align with previously studied COVID-19 definitions and four 'expanded' phenotypes that focus on susceptibility given exposure, mild clinical manifestations and an aggregate score of symptom severity. We performed a replication analysis of 12 previously reported COVID-19 genetic associations with all eight phenotypes in a trans-ancestry meta-analysis of AncestryDNA research participants. In this analysis, we show distinct patterns of association at the 12 loci with the eight outcomes that we assessed. We also performed a genome-wide discovery analysis of all eight phenotypes, which did not yield new genome-wide significant loci but did suggest that three of the four 'expanded' COVID-19 phenotypes have enhanced power to capture protective genetic associations relative to the previously studied phenotypes. Thus, we conclude that continued large-scale ascertainment of deep COVID-19 phenotype data would likely represent a boon for COVID-19 therapeutic target identification.
MeSH term(s)	COVID-19/genetics ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Phenotype ; Polymorphism, Single Nucleotide/genetics
Language	English
Publishing date	2022-04-11
Publishing country	United States
Document type	Journal Article ; Meta-Analysis
ZDB-ID	1108734-1
ISSN	1546-1718 ; 1061-4036
ISSN (online)	1546-1718
ISSN	1061-4036
DOI	10.1038/s41588-022-01042-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Evolinc: A Tool for the Identification and Evolutionary Comparison of Long Intergenic Non-coding RNAs.

Nelson, Andrew D L / Devisetty, Upendra K / Palos, Kyle / Haug-Baltzell, Asher K / Lyons, Eric / Beilstein, Mark A

Frontiers in genetics

2017 Volume 8, Page(s) 52

Abstract: Long intergenic non-coding RNAs (lincRNAs) are an abundant and functionally diverse class of eukaryotic transcripts. Reported lincRNA repertoires in mammals vary, but are commonly in the thousands to tens of thousands of transcripts, covering ~90% of the ...

Abstract	Long intergenic non-coding RNAs (lincRNAs) are an abundant and functionally diverse class of eukaryotic transcripts. Reported lincRNA repertoires in mammals vary, but are commonly in the thousands to tens of thousands of transcripts, covering ~90% of the genome. In addition to elucidating function, there is particular interest in understanding the origin and evolution of lincRNAs. Aside from mammals, lincRNA populations have been sparsely sampled, precluding evolutionary analyses focused on their emergence and persistence. Here we present Evolinc, a two-module pipeline designed to facilitate lincRNA discovery and characterize aspects of lincRNA evolution. The first module (Evolinc-I) is a lincRNA identification workflow that also facilitates downstream differential expression analysis and genome browser visualization of identified lincRNAs. The second module (Evolinc-II) is a genomic and transcriptomic comparative analysis workflow that determines the phylogenetic depth to which a lincRNA locus is conserved within a user-defined group of related species. Here we validate lincRNA catalogs generated with Evolinc-I against previously annotated Arabidopsis and human lincRNA data. Evolinc-I recapitulated earlier findings and uncovered an additional 70 Arabidopsis and 43 human lincRNAs. We demonstrate the usefulness of Evolinc-II by examining the evolutionary histories of a public dataset of 5,361 Arabidopsis lincRNAs. We used Evolinc-II to winnow this dataset to 40 lincRNAs conserved across species in Brassicaceae. Finally, we show how Evolinc-II can be used to recover the evolutionary history of a known lincRNA, the human telomerase RNA (TERC). These latter analyses revealed unexpected duplication events as well as the loss and subsequent acquisition of a novel TERC locus in the lineage leading to mice and rats. The Evolinc pipeline is currently integrated in CyVerse's Discovery Environment and is free for use by researchers.
Language	English
Publishing date	2017-05-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2017.00052
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms.

Joyce, Blake L / Haug-Baltzell, Asher K / Hulvey, Jonathan P / McCarthy, Fiona / Devisetty, Upendra Kumar / Lyons, Eric

Journal of visualized experiments : JoVE

2017 , Issue 123

Abstract	This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (https://wiki.cyverse.org/wiki/x/dgGtAQ). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.
Language	English
Publishing date	2017-05-09
Publishing country	United States
Document type	Journal Article
ISSN	1940-087X
ISSN (online)	1940-087X
DOI	10.3791/55009
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: AncestryDNA COVID-19 Host Genetic Study Identifies Three Novel Loci

Roberts, Genevieve H.L. / Park, Danny S. / Coignet, Marie V. / McCurdy, Shannon R. / Knight, Spencer C. / Partha, Raghavendran / Rhead, Brooke / Zhang, Miao / Berkowitz, Nathan / Science Team, AncestryDNA / Haug Baltzell, Asher K. / Guturu, Harendra / Girshick, Ahna R. / Rand, Kristin A. / Hong, Eurie L. / Ball, Catherine A.

medRxiv

Abstract: Human infection with SARS-CoV-2, the causative agent of COVID-19, leads to a remarkably diverse spectrum of outcomes, ranging from asymptomatic to fatal. Recent reports suggest that both clinical and genetic risk factors may contribute to COVID-19 ... ...

Abstract	Human infection with SARS-CoV-2, the causative agent of COVID-19, leads to a remarkably diverse spectrum of outcomes, ranging from asymptomatic to fatal. Recent reports suggest that both clinical and genetic risk factors may contribute to COVID-19 susceptibility and severity. To investigate genetic risk factors, we collected over 500,000 COVID-19 survey responses between April and May 2020 with accompanying genetic data from the AncestryDNA database. We conducted sex-stratified and meta-analyzed genome-wide association studies (GWAS) for COVID-19 susceptibility (positive nasopharyngeal swab test, ncases=2,407) and severity (hospitalization, ncases=250). The severity GWAS replicated associations with severe COVID-19 near ABO and SLC6A20 (P<0.05). Furthermore, we identified three novel loci with P<5x10-8. The strongest association was near IVNS1ABP, a gene involved in influenza virus replication, and was associated only in males. The other two novel loci harbor genes with established roles in viral replication or immunity: SRRM1 and the immunoglobulin lambda locus. We thus present new evidence that host genetic variation likely contributes to COVID-19 outcomes and demonstrate the value of large-scale, self-reported data as a mechanism to rapidly address a health crisis.
Keywords	covid19
Language	English
Publishing date	2020-10-09
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.10.06.20205864
Database	COVID19

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Article ; Online: COVID-19 Susceptibility and Severity Risks in a Survey of Over 500,000 People

Knight, Spencer C / McCurdy, Shannon R / Rhead, Brooke / Coignet, Marie V / Park, Danny S / Roberts, Genevieve H L / Berkowitz, Nathan D / Zhang, Miao / Turissini, David / Delgado, Karen / Pavlovic, Milos / Science Team, AncestryDNA / Haug Baltzell, Asher K / Guturu, Harendra / Rand, Kristin A / Girshick, Ahna R / Hong, Eurie L / Ball, Catherine A

medRxiv

Abstract: The growing toll of the COVID-19 pandemic has heightened the urgency of identifying individuals most at risk of infection and severe outcomes, underscoring the need to assess susceptibility and severity patterns in large datasets. The AncestryDNA COVID- ... ...

Abstract	The growing toll of the COVID-19 pandemic has heightened the urgency of identifying individuals most at risk of infection and severe outcomes, underscoring the need to assess susceptibility and severity patterns in large datasets. The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors, and exposures for over 563,000 adult individuals in the U.S., including over 4,700 COVID-19 cases as measured by a self-reported positive nasal swab test. We observed significant associations between several risk factors and COVID-19 susceptibility and severity outcomes. Many of the susceptibility associations were accounted for by differences in known exposures; a notable exception was elevated susceptibility odds for males after adjusting for known exposures and age. We also leveraged the dataset to build risk models to robustly predict individualized COVID-19 susceptibility (area under the curve [AUC]=0.84) and severity outcomes including hospitalization and life-threatening critical illness amongst COVID-19 cases (AUC=0.87 and 0.90, respectively). The results highlight the value of self-reported epidemiological data at scale to provide public health insights into the evolving COVID-19 pandemic.
Keywords	covid19
Language	English
Publishing date	2020-10-12
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.10.08.20209593
Database	COVID19

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Article ; Online: Novel COVID-19 phenotype definitions reveal phenotypically distinct patterns of genetic association and protective effects

Roberts, Genevieve H.L. / Partha, Raghavendran / Rhead, Brooke / Knight, Spencer C. / Park, Danny S. / Coignet, Marie V. / Zhang, Miao / Berkowitz, Nathan / Turrisini, David A. / Gaddis, Michael / McCurdy, Shannon R. / Pavlovic, Milos / Ruiz, Luong / Banda, Yambazi / Bi, Ke / Burton, Robert / Champine, Marjan / Curtis, Ross / Delgado, Karen /

medRxiv

Abstract: Multiple large COVID-19 genome-wide association studies (GWAS) have identified reproducible genetic associations indicating that some infection susceptibility and severity risk is heritable. Most of these studies ascertained COVID-19 cases in medical ... ...

Abstract	Multiple large COVID-19 genome-wide association studies (GWAS) have identified reproducible genetic associations indicating that some infection susceptibility and severity risk is heritable. Most of these studies ascertained COVID-19 cases in medical clinics and hospitals, which can lead to an overrepresentation of cases with severe outcomes, such as hospitalization, intensive care unit admission, or ventilation. Here, we demonstrate the utility and validity of deep phenotyping with self-reported outcomes in a population with a large proportion of mild and subclinical cases. Using these data, we defined eight different phenotypes related to COVID-19 outcomes: four that align with previously studied COVID-19 definitions and four novel definitions that focus on susceptibility given exposure, mild clinical manifestations, and an aggregate score of symptom severity. We assessed replication of 13 previously identified COVID-19 genetic associations with all eight phenotypes and found distinct patterns of association, most notably related to the chr3/SLC6A20/LZTFL1 and chr9/ABO regions. We then performed a discovery GWAS, which suggested some novel phenotypes may better capture protective associations and also identified a novel association in chr11/GALNT18 that reproduced in two fully independent populations.
Keywords	covid19
Language	English
Publishing date	2021-01-26
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2021.01.24.21250324
Database	COVID19

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