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  1. AU="Pfeifer, Susanne P"
  2. AU=McGrath Eric J
  3. AU="Wang, Pengyu"
  4. AU="Becker, Joscha Nico"
  5. AU="Chillrud, Steven N"
  6. AU=Werner L P
  7. AU="Tianmin Xu"
  8. AU="Matsagkas, Miltos"
  9. AU="Cosentino, Claudia"
  10. AU="Radbruch, Lukas"
  11. AU=Edwards Justin P.
  12. AU="Morse, Dan J"
  13. AU="Priya, Vansh"
  14. AU="Chen, Eric R"
  15. AU=Behrens Kevin G.
  16. AU=Radtke Heather B
  17. AU="Erdmann, Kati"
  18. AU="Anh, Nguyen P Q"
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  1. Article ; Online: Studying mutation rate evolution in primates-the effects of computational pipelines and parameter choices.

    Pfeifer, Susanne P

    GigaScience

    2021  Volume 10, Issue 10

    Abstract: This commentary investigates the important role of computational pipeline and parameter choices in performing mutation rate estimation, using the recent article published in this journal by Bergeron et al. entitled "The germline mutational process in ... ...

    Abstract This commentary investigates the important role of computational pipeline and parameter choices in performing mutation rate estimation, using the recent article published in this journal by Bergeron et al. entitled "The germline mutational process in rhesus macaque and its implications for phylogenetic dating" as an illustrative example.
    MeSH term(s) Animals ; Germ-Line Mutation ; Macaca mulatta/genetics ; Mutation ; Mutation Rate ; Phylogeny
    Language English
    Publishing date 2021-10-21
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2708999-X
    ISSN 2047-217X ; 2047-217X
    ISSN (online) 2047-217X
    ISSN 2047-217X
    DOI 10.1093/gigascience/giab069
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: The Effects of Mutation and Recombination Rate Heterogeneity on the Inference of Demography and the Distribution of Fitness Effects.

    Soni, Vivak / Pfeifer, Susanne P / Jensen, Jeffrey D

    Genome biology and evolution

    2024  Volume 16, Issue 2

    Abstract: Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavor; however, despite clear evidence that both mutation and ... ...

    Abstract Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavor; however, despite clear evidence that both mutation and recombination rates vary across genomes, it is common practice to model both rates as fixed. In this study, we quantify how this unaccounted for rate heterogeneity may impact inference using common approaches for inferring selection (DFE-alpha, Grapes, and polyDFE) and/or demography (fastsimcoal2 and δaδi). We demonstrate that, if not properly modeled, this heterogeneity can increase uncertainty in the estimation of demographic and selective parameters and in some scenarios may result in mis-leading inference. These results highlight the importance of quantifying the fundamental evolutionary parameters of mutation and recombination before utilizing population genomic data to quantify the effects of genetic drift (i.e. as modulated by demographic history) and selection; or, at the least, that the effects of uncertainty in these parameters can and should be directly modeled in downstream inference.
    MeSH term(s) Selection, Genetic ; Demography ; Mutation ; Genetic Drift ; Recombination, Genetic ; Models, Genetic
    Language English
    Publishing date 2024-01-10
    Publishing country England
    Document type Journal Article
    ZDB-ID 2495328-3
    ISSN 1759-6653 ; 1759-6653
    ISSN (online) 1759-6653
    ISSN 1759-6653
    DOI 10.1093/gbe/evae004
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A Fine-Scale Genetic Map for Vervet Monkeys.

    Pfeifer, Susanne P

    Molecular biology and evolution

    2020  Volume 37, Issue 7, Page(s) 1855–1865

    Abstract: Despite its important biological role, the evolution of recombination rates remains relatively poorly characterized. This owes, in part, to the lack of high-quality genomic resources to address this question across diverse species. Humans and our closest ...

    Abstract Despite its important biological role, the evolution of recombination rates remains relatively poorly characterized. This owes, in part, to the lack of high-quality genomic resources to address this question across diverse species. Humans and our closest evolutionary relatives, anthropoid apes, have remained a major focus of large-scale sequencing efforts, and thus recombination rate variation has been comparatively well studied in this group-with earlier work revealing a conservation at the broad- but not the fine-scale. However, in order to better understand the nature of this variation, and the time scales on which substantial modifications occur, it is necessary to take a broader phylogenetic perspective. I here present the first fine-scale genetic map for vervet monkeys based on whole-genome population genetic data from ten individuals and perform a series of comparative analyses with the great apes. The results reveal a number of striking features. First, owing to strong positive correlations with diversity and weak negative correlations with divergence, analyses suggest a dominant role for purifying and background selection in shaping patterns of variation in this species. Second, results support a generally reduced broad-scale recombination rate compared with the great apes, as well as a narrower fraction of the genome in which the majority of recombination events are observed to occur. Taken together, this data set highlights the great necessity of future research to identify genomic features and quantify evolutionary processes that are driving these rate changes across primates.
    MeSH term(s) Animals ; Chlorocebus aethiops/genetics ; Female ; Genome ; Humans ; Male ; Polymorphism, Single Nucleotide ; Recombination, Genetic
    Language English
    Publishing date 2020-03-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 998579-7
    ISSN 1537-1719 ; 0737-4038
    ISSN (online) 1537-1719
    ISSN 0737-4038
    DOI 10.1093/molbev/msaa079
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Computational Prediction of Bacteriophage Host Ranges.

    Versoza, Cyril J / Pfeifer, Susanne P

    Microorganisms

    2022  Volume 10, Issue 1

    Abstract: Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a ... ...

    Abstract Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a bacteriophage, i.e., the bacterial genera, species, and strains a bacteriophage is able to infect. Although experimental explorations of host ranges remain the gold standard, such investigations are inherently limited to a small number of viruses and bacteria amendable to cultivation. Here, we review recently developed bioinformatic tools that offer a promising and high-throughput alternative by computationally predicting the putative host ranges of bacteriophages, including those challenging to grow in laboratory environments.
    Language English
    Publishing date 2022-01-12
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2720891-6
    ISSN 2076-2607
    ISSN 2076-2607
    DOI 10.3390/microorganisms10010149
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Performance evaluation of six popular short-read simulators.

    Milhaven, Mark / Pfeifer, Susanne P

    Heredity

    2022  Volume 130, Issue 2, Page(s) 55–63

    Abstract: High-throughput sequencing data enables the comprehensive study of genomes and the variation therein. Essential for the interpretation of this genomic data is a thorough understanding of the computational methods used for processing and analysis. Whereas ...

    Abstract High-throughput sequencing data enables the comprehensive study of genomes and the variation therein. Essential for the interpretation of this genomic data is a thorough understanding of the computational methods used for processing and analysis. Whereas "gold-standard" empirical datasets exist for this purpose in humans, synthetic (i.e., simulated) sequencing data can offer important insights into the capabilities and limitations of computational pipelines for any arbitrary species and/or study design-yet, the ability of read simulator software to emulate genomic characteristics of empirical datasets remains poorly understood. We here compare the performance of six popular short-read simulators-ART, DWGSIM, InSilicoSeq, Mason, NEAT, and wgsim-and discuss important considerations for selecting suitable models for benchmarking.
    MeSH term(s) Humans ; Genomics/methods ; Software ; Genome ; High-Throughput Nucleotide Sequencing/methods ; Benchmarking
    Language English
    Publishing date 2022-12-10
    Publishing country England
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2423-5
    ISSN 1365-2540 ; 0018-067X
    ISSN (online) 1365-2540
    ISSN 0018-067X
    DOI 10.1038/s41437-022-00577-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Ub I Zs.154: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
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  6. Article: Developing an evolutionary baseline model for humans: jointly inferring purifying selection with population history.

    Johri, Parul / Pfeifer, Susanne P / Jensen, Jeffrey D

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics - including quantifying the relative contributions of adaptive vs. non-adaptive processes - but ... ...

    Abstract Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics - including quantifying the relative contributions of adaptive vs. non-adaptive processes - but it is also essential for identifying candidate loci experiencing relatively rare and episodic forms of selection (
    Language English
    Publishing date 2023-04-11
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.04.11.536488
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Computational host range prediction-The good, the bad, and the ugly.

    Howell, Abigail A / Versoza, Cyril J / Pfeifer, Susanne P

    Virus evolution

    2023  Volume 10, Issue 1, Page(s) vead083

    Abstract: The rapid emergence and spread of antimicrobial resistance across the globe have prompted the usage of bacteriophages (i.e. viruses that infect bacteria) in a variety of applications ranging from agriculture to biotechnology and medicine. In order to ... ...

    Abstract The rapid emergence and spread of antimicrobial resistance across the globe have prompted the usage of bacteriophages (i.e. viruses that infect bacteria) in a variety of applications ranging from agriculture to biotechnology and medicine. In order to effectively guide the application of bacteriophages in these multifaceted areas, information about their host ranges-that is the bacterial strains or species that a bacteriophage can successfully infect and kill-is essential. Utilizing sixteen broad-spectrum (polyvalent) bacteriophages with experimentally validated host ranges, we here benchmark the performance of eleven recently developed computational host range prediction tools that provide a promising and highly scalable supplement to traditional, but laborious, experimental procedures. We show that machine- and deep-learning approaches offer the highest levels of accuracy and precision-however, their predominant predictions at the species- or genus-level render them ill-suited for applications outside of an ecosystems metagenomics framework. In contrast, only moderate sensitivity (<80 per cent) could be reached at the strain-level, albeit at low levels of precision (<40 per cent). Taken together, these limitations demonstrate that there remains room for improvement in the active scientific field of
    Language English
    Publishing date 2023-12-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 2818949-8
    ISSN 2057-1577
    ISSN 2057-1577
    DOI 10.1093/ve/vead083
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article: The effects of mutation and recombination rate heterogeneity on the inference of demography and the distribution of fitness effects.

    Soni, Vivak / Pfeifer, Susanne P / Jensen, Jeffrey D

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavour; however, despite clear evidence that both mutation and ... ...

    Abstract Disentangling the effects of demography and selection has remained a focal point of population genetic analysis. Knowledge about mutation and recombination is essential in this endeavour; however, despite clear evidence that both mutation and recombination rates vary across genomes, it is common practice to model both rates as fixed. In this study, we quantify how this unaccounted for rate heterogeneity may impact inference using common approaches for inferring selection (DFE-alpha, Grapes, and polyDFE) and/or demography (fastsimcoal2 and
    Language English
    Publishing date 2023-11-13
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.11.11.566703
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Developing an Evolutionary Baseline Model for Humans: Jointly Inferring Purifying Selection with Population History.

    Johri, Parul / Pfeifer, Susanne P / Jensen, Jeffrey D

    Molecular biology and evolution

    2023  Volume 40, Issue 5

    Abstract: Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics-including quantifying the relative contributions of adaptive versus nonadaptive processes-but ... ...

    Abstract Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics-including quantifying the relative contributions of adaptive versus nonadaptive processes-but also essential for identifying candidate loci experiencing relatively rare and episodic forms of selection (e.g., positive or balancing selection). Here, a baseline model was developed for a human population of West African ancestry, the Yoruba, comprising processes constantly operating on the genome (i.e., purifying and background selection, population size changes, recombination rate heterogeneity, and gene conversion). Specifically, to perform joint inference of selective effects with demography, an approximate Bayesian approach was employed that utilizes the decay of background selection effects around functional elements, taking into account genomic architecture. This approach inferred a recent 6-fold population growth together with a distribution of fitness effects that is skewed towards effectively neutral mutations. Importantly, these results further suggest that, although strong and/or frequent recurrent positive selection is inconsistent with observed data, weak to moderate positive selection is consistent but unidentifiable if rare.
    MeSH term(s) Humans ; Bayes Theorem ; Selection, Genetic ; Evolution, Molecular ; Genetics, Population ; Genomics ; Models, Genetic
    Language English
    Publishing date 2023-05-02
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
    ZDB-ID 998579-7
    ISSN 1537-1719 ; 0737-4038
    ISSN (online) 1537-1719
    ISSN 0737-4038
    DOI 10.1093/molbev/msad100
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2137: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  10. Article: Computational Prediction of Bacteriophage Host Ranges

    Versoza, Cyril J. / Pfeifer, Susanne P.

    Microorganisms. 2022 Jan. 12, v. 10, no. 1

    2022  

    Abstract: Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a ... ...

    Abstract Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a bacteriophage, i.e., the bacterial genera, species, and strains a bacteriophage is able to infect. Although experimental explorations of host ranges remain the gold standard, such investigations are inherently limited to a small number of viruses and bacteria amendable to cultivation. Here, we review recently developed bioinformatic tools that offer a promising and high-throughput alternative by computationally predicting the putative host ranges of bacteriophages, including those challenging to grow in laboratory environments.
    Keywords antibiotic resistance ; bacteriophages ; bioinformatics ; biotechnology ; host range ; medicine ; prediction
    Language English
    Dates of publication 2022-0112
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    ZDB-ID 2720891-6
    ISSN 2076-2607
    ISSN 2076-2607
    DOI 10.3390/microorganisms10010149
    Database NAL-Catalogue (AGRICOLA)

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