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  1. AU="Moustafa, Ahmed M"
  2. AU="da Cruz, Luciana D"
  3. AU="Ratnayake, Jithendra"
  4. AU="Halesh, L H"
  5. AU=Babajanyan S G
  6. AU="Haruhara, Kotaro"
  7. AU="Wang, Che-Wei"
  8. AU="Eisenberg, Marcia"
  9. AU="Ufnalska, Sylwia"
  10. AU="Leroux, Dominique"
  11. AU="Gallagher, Timothy J"
  12. AU=Baggish Aaron
  13. AU="Bush, Ashley I"
  14. AU="Carr, Kenneth D."
  15. AU="Spiro, Stephen"
  16. AU="Roberts, William Clifford"
  17. AU="Park, Hyungjong"
  18. AU="Das, Debasish"
  19. AU="Sanz-Magro, Adrián"
  20. AU="Fan, Shanhui"
  21. AU="Ellonen, Pekka"
  22. AU="Lambert, T"
  23. AU="Vivekanandan, Rajesh"

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  1. Artikel ; Online: Jumping a Moving Train: SARS-CoV-2 Evolution in Real Time.

    Moustafa, Ahmed M / Planet, Paul J

    Journal of the Pediatric Infectious Diseases Society

    2021  Band 10, Heft Supplement_4, Seite(n) S96–S105

    Abstract: The field of molecular epidemiology responded to the SARS-CoV-2 pandemic with an unrivaled amount of whole viral genome sequencing. By the time this sentence is published we will have well surpassed 1.5 million whole genomes, more than 4 times the number ...

    Abstract The field of molecular epidemiology responded to the SARS-CoV-2 pandemic with an unrivaled amount of whole viral genome sequencing. By the time this sentence is published we will have well surpassed 1.5 million whole genomes, more than 4 times the number of all microbial whole genomes deposited in GenBank and 35 times the total number of viral genomes. This extraordinary dataset that accrued in near real time has also given us an opportunity to chart the global and local evolution of a virus as it moves through the world population. The data itself presents challenges that have never been dealt with in molecular epidemiology, and tracking a virus that is changing so rapidly means that we are often running to catch up. Here we review what is known about the evolution of the virus, and the critical impact that whole genomes have had on our ability to trace back and track forward the spread of lineages of SARS-CoV-2. We then review what whole genomes have told us about basic biological properties of the virus such as transmissibility, virulence, and immune escape with a special emphasis on pediatric disease. We couch this discussion within the framework of systematic biology and phylogenetics, disciplines that have proven their worth again and again for identifying and deciphering the spread of epidemics, though they were largely developed in areas far removed from infectious disease and medicine.
    Mesh-Begriff(e) COVID-19 ; Child ; Genome, Viral ; Humans ; Pandemics ; Phylogeny ; SARS-CoV-2
    Sprache Englisch
    Erscheinungsdatum 2021-06-24
    Erscheinungsland England
    Dokumenttyp Journal Article ; Review
    ZDB-ID 2668791-4
    ISSN 2048-7207 ; 2048-7193
    ISSN (online) 2048-7207
    ISSN 2048-7193
    DOI 10.1093/jpids/piab051
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  2. Artikel ; Online: Emerging SARS-CoV-2 Diversity Revealed by Rapid Whole-Genome Sequence Typing.

    Moustafa, Ahmed M / Planet, Paul J

    Genome biology and evolution

    2021  Band 13, Heft 9

    Abstract: Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events. We developed a tool (GNU-based Virus IDentification [GNUVID]) that integrates whole-genome ... ...

    Abstract Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events. We developed a tool (GNU-based Virus IDentification [GNUVID]) that integrates whole-genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to all high-quality genomes available from GISAID. STs were clustered into clonal complexes (CCs) and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events and to estimate effective viral diversity across locations and over time in 16 US states. GNUVID is a highly scalable tool for viral genotype classification (https://github.com/ahmedmagds/GNUVID) that can quickly classify hundreds of thousands of genomes in a way that is consistent with phylogeny. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states, an average of 20.6 putative introductions and 7.5 exportations for each state over the time period analyzed. We introduce the use of effective diversity metrics (Hill numbers) that can be used to estimate the impact of interventions (e.g., travel restrictions, vaccine uptake, mask mandates) on the variation in circulating viruses. Our classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. GNUVID classification lends itself to measures of ecological diversity, and, with systematic genomic sampling, it could be used to track circulating viral diversity and identify emerging clones and hotspots.
    Mesh-Begriff(e) COVID-19/virology ; Genome, Viral/genetics ; Genomics/methods ; Genotype ; Humans ; Machine Learning ; SARS-CoV-2/genetics ; Whole Genome Sequencing/methods
    Sprache Englisch
    Erscheinungsdatum 2021-08-25
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2495328-3
    ISSN 1759-6653 ; 1759-6653
    ISSN (online) 1759-6653
    ISSN 1759-6653
    DOI 10.1093/gbe/evab197
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel: Emerging SARS-CoV-2 diversity revealed by rapid whole genome sequence typing.

    Moustafa, Ahmed M / Planet, Paul J

    bioRxiv : the preprint server for biology

    2020  

    Abstract: Background: Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events.: Methods: We developed a tool (GNUVID) that integrates whole genome multilocus ... ...

    Abstract Background: Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events.
    Methods: We developed a tool (GNUVID) that integrates whole genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to each of 69,686 SARS-CoV-2 complete, high-quality genomes available from GISAID as of October 20
    Results: GNUVID is a scalable tool for viral genotype classification (available at https://github.com/ahmedmagds/GNUVID ) that can be used to quickly process tens of thousands of genomes. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states. We detected an average of 20.6 putative introductions and 7.5 exportations for each state. Effective viral diversity dropped in all states as shelter-in-place travel-restrictions went into effect and increased as restrictions were lifted. Interestingly, our analysis showed correlation between effective diversity and the date that state-wide mask mandates were imposed.
    Conclusions: Our classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. Combined with future genomic sampling the GNUVID system could be used to track circulating viral diversity and identify emerging clones and hotspots.
    Sprache Englisch
    Erscheinungsdatum 2020-12-28
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2020.12.28.424582
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel ; Online: WhatsGNU: a tool for identifying proteomic novelty.

    Moustafa, Ahmed M / Planet, Paul J

    Genome biology

    2020  Band 21, Heft 1, Seite(n) 58

    Abstract: To understand diversity in enormous collections of genome sequences, we need computationally scalable tools that can quickly contextualize individual genomes based on their similarities and identify features of each genome that make them unique. We ... ...

    Abstract To understand diversity in enormous collections of genome sequences, we need computationally scalable tools that can quickly contextualize individual genomes based on their similarities and identify features of each genome that make them unique. We present WhatsGNU, a tool based on exact match proteomic compression that, in seconds, classifies any new genome and provides a detailed report of protein alleles that may have novel functional differences. We use this technique to characterize the total allelic diversity (panallelome) of Salmonella enterica, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Staphylococcus aureus. It could be extended to others. WhatsGNU is available from https://github.com/ahmedmagds/WhatsGNU.
    Mesh-Begriff(e) Bacterial Proteins/genetics ; Gene Frequency ; Genome, Bacterial ; Mycobacterium tuberculosis/genetics ; Proteomics/methods ; Pseudomonas aeruginosa/genetics ; Salmonella enterica/genetics ; Software ; Staphylococcus aureus/genetics
    Chemische Substanzen Bacterial Proteins
    Sprache Englisch
    Erscheinungsdatum 2020-03-05
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1474-760X
    ISSN (online) 1474-760X
    ISSN 1474-760X
    DOI 10.1186/s13059-020-01965-w
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel ; Online: Rapid whole genome sequence typing reveals multiple waves of SARS-CoV-2 spread

    Moustafa, Ahmed M. / Planet, Paul J.

    bioRxiv

    Abstract: As the pandemic SARS-CoV-2 virus has spread globally its genome has diversified to an extent that distinct clones can now be recognized, tracked, and traced. Identifying clonal groups allows for assessment of geographic spread, transmission events, and ... ...

    Abstract As the pandemic SARS-CoV-2 virus has spread globally its genome has diversified to an extent that distinct clones can now be recognized, tracked, and traced. Identifying clonal groups allows for assessment of geographic spread, transmission events, and identification of new or emerging strains that may be more virulent or more transmissible. Here we present a rapid, whole genome, allele-based method (GNUVID) for assigning sequence types to sequenced isolates of SARS-CoV-2 sequences. This sequence typing scheme can be updated with new genomic information extremely rapidly, making our technique continually adaptable as databases grow. We show that our method is consistent with phylogeny and recovers waves of expansion and replacement of sequence types/clonal complexes in different geographical locations. GNUVID is available as a command line application (https://github.com/ahmedmagds/GNUVID).
    Schlagwörter covid19
    Verlag BioRxiv; WHO
    Dokumenttyp Artikel ; Online
    DOI 10.1101/2020.06.08.139055
    Datenquelle COVID19

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  6. Artikel ; Online: Rapid whole genome sequence typing reveals multiple waves of SARS-CoV-2 spread

    Moustafa, Ahmed M / Planet, Paul

    bioRxiv

    Abstract: As the pandemic SARS-CoV-2 virus has spread globally its genome has diversified to an extent that distinct clones can now be recognized, tracked, and traced. Identifying clonal groups allows for assessment of geographic spread, transmission events, and ... ...

    Abstract As the pandemic SARS-CoV-2 virus has spread globally its genome has diversified to an extent that distinct clones can now be recognized, tracked, and traced. Identifying clonal groups allows for assessment of geographic spread, transmission events, and identification of new or emerging strains that may be more virulent or more transmissible. Here we present a rapid, whole genome, allele-based method (GNUVID) for assigning sequence types to sequenced isolates of SARS-CoV-2 sequences. This sequence typing scheme can be updated with new genomic information extremely rapidly, making our technique continually adaptable as databases grow. We show that our method is consistent with phylogeny and recovers waves of expansion and replacement of sequence types/clonal complexes in different geographical locations. GNUVID is available as a command line application (https://github.com/ahmedmagds/GNUVID).
    Schlagwörter covid19
    Sprache Englisch
    Erscheinungsdatum 2020-06-09
    Verlag Cold Spring Harbor Laboratory
    Dokumenttyp Artikel ; Online
    DOI 10.1101/2020.06.08.139055
    Datenquelle COVID19

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  7. Artikel ; Online: Emerging SARS-CoV-2 diversity revealed by rapid whole genome sequence typing.

    Moustafa, Ahmed M. / Planet, Paul

    bioRxiv

    Abstract: Background: Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events. Methods: We developed a tool (GNUVID) that integrates whole genome multilocus sequence ...

    Abstract Background: Discrete classification of SARS-CoV-2 viral genotypes can identify emerging strains and detect geographic spread, viral diversity, and transmission events. Methods: We developed a tool (GNUVID) that integrates whole genome multilocus sequence typing and a supervised machine learning random forest-based classifier. We used GNUVID to assign sequence type (ST) profiles to each of 69,686 SARS-CoV-2 complete, high-quality genomes available from GISAID as of October 20th 2020. STs were then clustered into clonal complexes (CCs), and then used to train a machine learning classifier. We used this tool to detect potential introduction and exportation events, and to estimate effective viral diversity across locations and over time in 16 US states. Results: GNUVID is a scalable tool for viral genotype classification (available at https://github.com/ahmedmagds/GNUVID) that can be used to quickly process tens of thousands of genomes. Our genotyping ST/CC analysis uncovered dynamic local changes in ST/CC prevalence and diversity with multiple replacement events in different states. We detected an average of 20.6 putative introductions and 7.5 exportations for each state. Effective viral diversity dropped in all states as shelter-in-place travel-restrictions went into effect and increased as restrictions were lifted. Interestingly, our analysis showed correlation between effective diversity and the date that state-wide mask mandates were imposed. Conclusions: Our classification tool uncovered multiple introduction and exportation events, as well as waves of expansion and replacement of SARS-CoV-2 genotypes in different states. Combined with future genomic sampling the GNUVID system could be used to track circulating viral diversity and identify emerging clones and hotspots.
    Schlagwörter covid19
    Sprache Englisch
    Erscheinungsdatum 2020-12-28
    Verlag Cold Spring Harbor Laboratory
    Dokumenttyp Artikel ; Online
    DOI 10.1101/2020.12.28.424582
    Datenquelle COVID19

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  8. Artikel ; Online: Rapid Whole Genome Sequence Typing Reveals Multiple Waves of SARS-CoV-2 Spread

    Moustafa, Ahmed M. / Planet, Paul

    SSRN Electronic Journal ; ISSN 1556-5068

    2020  

    Schlagwörter covid19
    Sprache Englisch
    Verlag Elsevier BV
    Erscheinungsland us
    Dokumenttyp Artikel ; Online
    DOI 10.2139/ssrn.3629472
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  9. Artikel: WhatsGNU: a tool for identifying proteomic novelty

    Moustafa, Ahmed M / Planet, Paul J

    Genome biology. 2020 Dec., v. 21, no. 1

    2020  

    Abstract: To understand diversity in enormous collections of genome sequences, we need computationally scalable tools that can quickly contextualize individual genomes based on their similarities and identify features of each genome that make them unique. We ... ...

    Abstract To understand diversity in enormous collections of genome sequences, we need computationally scalable tools that can quickly contextualize individual genomes based on their similarities and identify features of each genome that make them unique. We present WhatsGNU, a tool based on exact match proteomic compression that, in seconds, classifies any new genome and provides a detailed report of protein alleles that may have novel functional differences. We use this technique to characterize the total allelic diversity (panallelome) of Salmonella enterica, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Staphylococcus aureus. It could be extended to others. WhatsGNU is available from https://github.com/ahmedmagds/WhatsGNU.
    Schlagwörter Mycobacterium tuberculosis ; Pseudomonas aeruginosa ; Salmonella enterica ; Staphylococcus aureus ; alleles ; allelic variation ; nucleotide sequences ; proteomics
    Sprache Englisch
    Erscheinungsverlauf 2020-12
    Umfang p. 58.
    Erscheinungsort BioMed Central
    Dokumenttyp Artikel
    ZDB-ID 2040529-7
    ISSN 1474-760X ; 1465-6906
    ISSN (online) 1474-760X
    ISSN 1465-6906
    DOI 10.1186/s13059-020-01965-w
    Datenquelle NAL Katalog (AGRICOLA)

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  10. Artikel: Comparative genomics in infectious disease

    Moustafa, Ahmed M / Lal, Arnav / Planet, Paul J

    Current opinion in microbiology. 2020 Feb., v. 53

    2020  

    Abstract: With more than one million bacterial genome sequences uploaded to public databases in the last 25 years, genomics has become a powerful tool for studying bacterial biology. Here, we review recent approaches that leverage large numbers of whole genome ... ...

    Abstract With more than one million bacterial genome sequences uploaded to public databases in the last 25 years, genomics has become a powerful tool for studying bacterial biology. Here, we review recent approaches that leverage large numbers of whole genome sequences to decipher the spread and pathogenesis of bacterial infectious diseases.
    Schlagwörter databases ; genomics ; infectious diseases ; nucleotide sequences ; pathogenesis
    Sprache Englisch
    Erscheinungsverlauf 2020-02
    Umfang p. 61-70.
    Erscheinungsort Elsevier Ltd
    Dokumenttyp Artikel
    ZDB-ID 1418474-6
    ISSN 1879-0364 ; 1369-5274
    ISSN (online) 1879-0364
    ISSN 1369-5274
    DOI 10.1016/j.mib.2020.02.009
    Datenquelle NAL Katalog (AGRICOLA)

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