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Artikel ; Online: SnakeWRAP: a Snakemake workflow to facilitate automated processing of metagenomic data through the metaWRAP pipeline.

Krapohl, John / Pickett, Brett E

2022 Band 11, Seite(n) 265

Abstract: Generating high-quality genome assemblies of complex microbial populations from shotgun metagenomics data is often a manually intensive task involving many computational steps. SnakeWRAP is a novel tool, implemented in the Snakemake workflow language, to ...

Abstract	Generating high-quality genome assemblies of complex microbial populations from shotgun metagenomics data is often a manually intensive task involving many computational steps. SnakeWRAP is a novel tool, implemented in the Snakemake workflow language, to automate multiple metaWRAP modules. Specifically, it wraps the shell scripts provided within the original metaWRAP software, within Snakemake. This approach enables high-throughput simultaneous assembly and analysis of multiple shotgun metagenomic datasets using the robust modular metaWRAP software. We expect this advancement to be of import in institutions where high-performance computing infrastructure is available, especially in the context of big data. This software tool is publicly available at https://github.com/jkrapohl/SnakeWRAP.
Mesh-Begriff(e)	Metagenomics ; Workflow ; Software ; Metagenome ; Computing Methodologies
Sprache	Englisch
Erscheinungsdatum	2022-04-28
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2699932-8
ISSN	2046-1402 ; 2046-1402
ISSN (online)	2046-1402
ISSN	2046-1402
DOI	10.12688/f1000research.108835.2
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes

Clancy, Jeffrey / Hoffmann, Curtis S. / Pickett, Brett E.

Computational and Structural Biotechnology Journal. 20232023 Feb. 09, v. 21 p.1403-1413

2023

Abstract: SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest ... ...

Abstract	SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.
Schlagwörter	COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; biomarkers ; biotechnology ; cameras ; diagnostic techniques ; disease severity ; etiological agents ; forestry equipment ; gene expression ; gene expression regulation ; gene ontology ; human diseases ; human health ; humans ; immune response ; inflammation ; leukocytes ; metadata ; mortality ; phenotype ; sequence analysis ; transcription (genetics) ; transcriptome ; transcriptomics ; SARS-CoV-2 ; COVID-19 ; GEO ; ROC ; AUC ; DEG ; GO ; RNA-sequencing ; Data mining ; RNA ; Virus ; Bioinformatics
Sprache	Englisch
Erscheinungsverlauf	2023-0209
Umfang	p. 1403-1413.
Erscheinungsort	Elsevier B.V.
Dokumenttyp	Artikel ; Online
Anmerkung	Use and reproduction
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2023.02.003
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel: Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes.

Clancy, Jeffrey / Hoffmann, Curtis S / Pickett, Brett E

Computational and structural biotechnology journal

2023 Band 21, Seite(n) 1403–1413

Abstract	SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.
Sprache	Englisch
Erscheinungsdatum	2023-02-09
Erscheinungsland	Netherlands
Dokumenttyp	Journal Article
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2023.02.003
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Genomic Analyses of Major SARS-CoV-2 Variants Predicting Multiple Regions of Pathogenic and Transmissive Importance.

Brugger, Steven W / Grose, Julianne H / Decker, Craig H / Pickett, Brett E / Davis, Mary F

Viruses

2024 Band 16, Heft 2

Abstract: The rapid evolution of SARS-CoV-2 has fueled its global proliferation since its discovery in 2019, with several notable variants having been responsible for increases in cases of coronavirus disease 2019 (COVID-19). Analyses of codon bias and usage in ... ...

Abstract	The rapid evolution of SARS-CoV-2 has fueled its global proliferation since its discovery in 2019, with several notable variants having been responsible for increases in cases of coronavirus disease 2019 (COVID-19). Analyses of codon bias and usage in these variants between phylogenetic clades or lineages may grant insights into the evolution of SARS-CoV-2 and identify target codons indicative of evolutionary or mutative trends that may prove useful in tracking or defending oneself against emerging strains. We processed a cohort of 120 SARS-CoV-2 genome sequences through a statistical and bioinformatic pipeline to identify codons presenting evidence of selective pressure as well as codon coevolution. We report the identification of two codon sites in the
Mesh-Begriff(e)	Humans ; SARS-CoV-2/genetics ; COVID-19/genetics ; Phylogeny ; Genome, Viral ; Genomics ; Codon
Chemische Substanzen	Codon
Sprache	Englisch
Erscheinungsdatum	2024-02-10
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2516098-9
ISSN	1999-4915 ; 1999-4915
ISSN (online)	1999-4915
ISSN	1999-4915
DOI	10.3390/v16020276
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Joint Secondary Transcriptomic Analysis of Non-Hodgkin's B-Cell Lymphomas Predicts Reliance on Pathways Associated with the Extracellular Matrix and Robust Diagnostic Biomarkers.

Rapier-Sharman, Naomi / Clancy, Jeffrey / Pickett, Brett E

Journal of bioinformatics and systems biology : Open access

2022 Band 5, Heft 4, Seite(n) 119–135

Abstract: Approximately 450,000 cases of Non-Hodgkin's lymphoma are annually diagnosed worldwide, resulting in ~240,000 deaths. An augmented understanding of the common mechanisms of pathology among larger numbers of B-cell Non-Hodgkin's Lymphoma (BCNHL) patients ... ...

Abstract	Approximately 450,000 cases of Non-Hodgkin's lymphoma are annually diagnosed worldwide, resulting in ~240,000 deaths. An augmented understanding of the common mechanisms of pathology among larger numbers of B-cell Non-Hodgkin's Lymphoma (BCNHL) patients is sorely needed. We consequently performed a large joint secondary transcriptomic analysis of the available BCNHL RNA-sequencing projects from GEO, consisting of 322 relevant samples across ten distinct public studies, to find common underlying mechanisms and biomarkers across multiple BCNHL subtypes and patient subpopulations; limitations may include lack of diversity in certain ethnicities and age groups and limited clinical subtype diversity due to sample availability. We found ~10,400 significant differentially expressed genes (FDR-adjusted p-value < 0.05) and 33 significantly modulated pathways (Bonferroni-adjusted p-value < 0.05) when comparing BCNHL samples to non-diseased B-cell samples. Our findings included a significant class of proteoglycans not previously associated with lymphomas as well as significant modulation of genes that code for extracellular matrix-associated proteins. Our drug repurposing analysis predicted new candidates for repurposed drugs including ocriplasmin and collagenase. We also used a machine learning approach to identify robust BCNHL biomarkers that include YES1, FERMT2, and FAM98B, which have not previously been associated with BCNHL in the literature, but together provide ~99.9% combined specificity and sensitivity for differentiating lymphoma cells from healthy B-cells based on measurement of transcript expression levels in B-cells. This analysis supports past findings and validates existing knowledge while providing novel insights into the inner workings and mechanisms of transformed B-cell lymphomas that could give rise to improved diagnostics and/or therapeutics.
Sprache	Englisch
Erscheinungsdatum	2022-09-27
Erscheinungsland	United States
Dokumenttyp	Journal Article
ISSN	2688-5107
ISSN (online)	2688-5107
DOI	10.26502/jbsb.5107040
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Mutation in Hemagglutinin Antigenic Sites in Influenza A pH1N1 Viruses from 2015-2019 in the United States Mountain West, Europe, and the Northern Hemisphere.

Decker, Craig H / Rapier-Sharman, Naomi / Pickett, Brett E

Genes

2022 Band 13, Heft 5

Abstract: H1N1 influenza A virus is a respiratory pathogen that undergoes antigenic shift and antigenic drift to improve viral fitness. Tracking the evolutionary trends of H1N1 aids with the current detection and the future response to new viral strains as they ... ...

Abstract	H1N1 influenza A virus is a respiratory pathogen that undergoes antigenic shift and antigenic drift to improve viral fitness. Tracking the evolutionary trends of H1N1 aids with the current detection and the future response to new viral strains as they emerge. Here, we characterize antigenic drift events observed in the hemagglutinin (HA) sequence of the pandemic H1N1 lineage from 2015-2019. We observed the substitutions S200P, K147N, and P154S, together with other mutations in structural, functional, and/or epitope regions in 2015-2019 HA protein sequences from the Mountain West region of the United States, the larger United States, Europe, and other Northern Hemisphere countries. We reconstructed multiple phylogenetic trees to track the relationships and spread of these mutations and tested for evidence of selection pressure on HA. We found that the prevalence of amino acid substitutions at positions 147, 154, 159, 200, and 233 significantly changed throughout the studied geographical regions between 2015 and 2019. We also found evidence of coevolution among a subset of these amino acid substitutions. The results from this study could be relevant for future epidemiological tracking and vaccine prediction efforts. Similar analyses in the future could identify additional sequence changes that could affect the pathogenicity and/or infectivity of this virus in its human host.
Mesh-Begriff(e)	Antigens ; Europe/epidemiology ; Hemagglutinin Glycoproteins, Influenza Virus/chemistry ; Hemagglutinin Glycoproteins, Influenza Virus/genetics ; Hemagglutinins ; Humans ; Influenza A Virus, H1N1 Subtype/genetics ; Influenza A virus ; Influenza, Human/epidemiology ; Influenza, Human/genetics ; Mutation ; Phylogeny ; United States/epidemiology
Chemische Substanzen	Antigens ; Hemagglutinin Glycoproteins, Influenza Virus ; Hemagglutinins
Sprache	Englisch
Erscheinungsdatum	2022-05-19
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2527218-4
ISSN	2073-4425 ; 2073-4425
ISSN (online)	2073-4425
ISSN	2073-4425
DOI	10.3390/genes13050909
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Comparative meta-analysis of host transcriptional response during Streptococcus pneumoniae carriage or infection.

Gifford, Kennedy T L / Pickett, Brett E

Microbial pathogenesis

2022 Band 173, Heft Pt A, Seite(n) 105816

Abstract: Infection with Streptococcus pneumoniae causes over 150,000 cases of pneumonia annually in the United States alone. We performed a meta-analysis of publicly available RNA-sequencing data to compare the host-specific intracellular transcriptional ... ...

Abstract	Infection with Streptococcus pneumoniae causes over 150,000 cases of pneumonia annually in the United States alone. We performed a meta-analysis of publicly available RNA-sequencing data to compare the host-specific intracellular transcriptional responses that differ between infection and carriage with S. pneumoniae in humans and mice. We applied an automated preprocessing workflow to collections of publicly available fastq files that were categorized as either of two phenotypes-infection or carriage in humans and mice. We identified the differentially expressed genes and intracellular signaling pathways that changed in host cells during infection or carriage and whether these human phenotypes could be appropriately modeled at the molecular level in mice. Although we observed multiple differentially expressed genes shared among multiple comparisons, we found no overlapping significant signaling pathways between the mouse and human studies in either phenotype. Based on the samples included in this secondary analysis, we identified a minimal number of generalized transcriptional relationships between host infection and carriage phenotypes of S. pneumoniae that were consistently shared between the mouse and human hosts. Our findings suggest that additional controlled datasets in mouse and human carriage or infection models are needed to better understand the underlying mechanism(s) of invasion and pathogenesis. This knowledge could then contribute to the development of improved prophylactics and/or therapeutics against this pathogen.
Mesh-Begriff(e)	Humans ; Mice ; Animals ; Streptococcus pneumoniae/genetics ; Pneumococcal Infections/prevention & control ; Carrier State ; Nasopharynx
Sprache	Englisch
Erscheinungsdatum	2022-10-06
Erscheinungsland	England
Dokumenttyp	Meta-Analysis ; Journal Article
ZDB-ID	632772-2
ISSN	1096-1208 ; 0882-4010
ISSN (online)	1096-1208
ISSN	0882-4010
DOI	10.1016/j.micpath.2022.105816
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Comparative sequence analysis elucidates the evolutionary patterns of

Warren, Mary E / Pickett, Brett E / Adams, Byron J / Villalva, Crystal / Applegate, Alyssa / Robison, Richard A

PeerJ

2023 Band 11, Seite(n) e16007

Abstract: Background: Yersinia pestis: Methods: To better understand evolutionary patterns in : Results: We identified four genes, ... ...

Abstract	Background: Yersinia pestis Methods: To better understand evolutionary patterns in Results: We identified four genes, including
Mesh-Begriff(e)	Humans ; Yersinia pestis/genetics ; Phylogeny ; New Mexico/epidemiology ; Plague/epidemiology ; Sequence Analysis
Sprache	Englisch
Erscheinungsdatum	2023-09-26
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2703241-3
ISSN	2167-8359 ; 2167-8359
ISSN (online)	2167-8359
ISSN	2167-8359
DOI	10.7717/peerj.16007
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: A signaling pathway-driven bioinformatics pipeline for predicting therapeutics against emerging infectious diseases.

Scott, Tiana M / Jensen, Sam / Pickett, Brett E

F1000Research

2021 Band 10, Seite(n) 330

Abstract: Background: ...

Abstract	Background:
Mesh-Begriff(e)	COVID-19 ; Communicable Diseases, Emerging ; Computational Biology ; Humans ; SARS-CoV-2 ; Signal Transduction
Sprache	Englisch
Erscheinungsdatum	2021-04-29
Erscheinungsland	England
Dokumenttyp	Journal Article ; Meta-Analysis ; Research Support, Non-U.S. Gov't
ZDB-ID	2699932-8
ISSN	2046-1402 ; 2046-1402
ISSN (online)	2046-1402
ISSN	2046-1402
DOI	10.12688/f1000research.52412.2
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Transcriptomics Meta-Analysis Predicts Two Robust Human Biomarkers for Severe Infection with SARS-CoV-2

Clancy, Jeffrey / Hoffmann, Curtis S / Pickett, Brett E

medRxiv

Abstract: Defining the human factors associated with severe vs mild SARS-CoV-2 infection has become of increasing interest. Mining large numbers of public gene expression datasets is an effective way to identify genes that contribute to a given phenotype. ... ...

Abstract	Defining the human factors associated with severe vs mild SARS-CoV-2 infection has become of increasing interest. Mining large numbers of public gene expression datasets is an effective way to identify genes that contribute to a given phenotype. Combining RNA-sequencing data with the associated clinical metadata describing disease severity can enable earlier identification of patients who are at higher risk of developing severe COVID-19 disease. We consequently identified 356 public RNA-seq human transcriptome samples from the Gene Expression Omnibus database that had disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to quantify gene expression in each patient. This process involved using Salmon to map the reads to the reference transcriptomes, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then applied a machine learning algorithm to the read counts data to identify features that best differentiated samples based on COVID-19 severity phenotype. Ultimately, we produced a ranked list of genes based on their Gini importance values that includes GIMAP7 and S1PR2, which are associated with immunity and inflammation (respectively). Our results show that these two genes can potentially predict people with severe COVID-19 at up to ~90% accuracy. We expect that our findings can help contribute to the development of improved prognostics for severe COVID-19.
Schlagwörter	covid19
Sprache	Englisch
Erscheinungsdatum	2022-06-06
Verlag	Cold Spring Harbor Laboratory Press
Dokumenttyp	Artikel ; Online
DOI	10.1101/2022.06.06.22276040
Datenquelle	COVID19

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