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  • Articles  (2)
  • Analytical models  (1)
  • Animal evolution  (1)
  • Oxford University Press  (2)
  • American Institute of Physics
  • American Physical Society
  • 2020-2023  (2)
  • 1970-1974
Collection
  • Articles  (2)
Publisher
  • Oxford University Press  (2)
  • American Institute of Physics
  • American Physical Society
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  • 2020-2023  (2)
  • 1970-1974
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  • 1
    Publication Date: 2022-10-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Tassia, M. G., David, K. T., Townsend, J. P., & Halanych, K. M. TIAMMAt: leveraging biodiversity to revise protein domain models, evidence from innate immunity. Molecular Biology and Evolution, 38(12), (2021): 5806–5818, https://doi.org/10.1093/molbev/msab258.
    Description: Sequence annotation is fundamental for studying the evolution of protein families, particularly when working with nonmodel species. Given the rapid, ever-increasing number of species receiving high-quality genome sequencing, accurate domain modeling that is representative of species diversity is crucial for understanding protein family sequence evolution and their inferred function(s). Here, we describe a bioinformatic tool called Taxon-Informed Adjustment of Markov Model Attributes (TIAMMAt) which revises domain profile hidden Markov models (HMMs) by incorporating homologous domain sequences from underrepresented and nonmodel species. Using innate immunity pathways as a case study, we show that revising profile HMM parameters to directly account for variation in homologs among underrepresented species provides valuable insight into the evolution of protein families. Following adjustment by TIAMMAt, domain profile HMMs exhibit changes in their per-site amino acid state emission probabilities and insertion/deletion probabilities while maintaining the overall structure of the consensus sequence. Our results show that domain revision can heavily impact evolutionary interpretations for some families (i.e., NLR’s NACHT domain), whereas impact on other domains (e.g., rel homology domain and interferon regulatory factor domains) is minimal due to high levels of sequence conservation across the sampled phylogenetic depth (i.e., Metazoa). Importantly, TIAMMAt revises target domain models to reflect homologous sequence variation using the taxonomic distribution under consideration by the user. TIAMMAt’s flexibility to revise any subset of the Pfam database using a user-defined taxonomic pool will make it a valuable tool for future protein evolution studies, particularly when incorporating (or focusing) on nonmodel species.
    Description: This work was supported by The National Science Foundation (Grant No. IOS—1755377 to K.M.H., Rita Graze, and Elizabeth Hiltbold Schwartz), and K.T.D. was supported by The National Science Foundation’s Graduate Research Fellowship Program.
    Keywords: Protein evolution ; Domain annotation ; Animal evolution ; Innate immunity
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-05-26
    Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Beckman, N. G., Asian, C. E., Rogers, H. S., Kogan, O., Bronstein, J. L., Bullock, J. M., Hartig, F., HilleRisLambers, J., Zhou, Y., Zurell, D., Brodie, J. F., Bruna, E. M., Cantrell, R. S., Decker, R. R., Efiom, E., Fricke, E. C., Gurski, K., Hastings, A., Johnson, J. S., Loiselle, B. A., Miriti, M. N., Neubert, M. G., Pejchar, L., Poulsen, J. R., Pufal, G., Razafindratsima, O. H., Sandor, M. E., Shea, K., Schreiber, S., Schupp, E. W., Snell, R. S., Strickland, C., & Zambrano, J. Advancing an interdisciplinary framework to study seed dispersal ecology. Aob Plants, 12(2), (2020): plz048, doi:10.1093/aobpla/plz048.
    Description: Although dispersal is generally viewed as a crucial determinant for the fitness of any organism, our understanding of its role in the persistence and spread of plant populations remains incomplete. Generalizing and predicting dispersal processes are challenging due to context dependence of seed dispersal, environmental heterogeneity and interdependent processes occurring over multiple spatial and temporal scales. Current population models often use simple phenomenological descriptions of dispersal processes, limiting their ability to examine the role of population persistence and spread, especially under global change. To move seed dispersal ecology forward, we need to evaluate the impact of any single seed dispersal event within the full spatial and temporal context of a plant’s life history and environmental variability that ultimately influences a population’s ability to persist and spread. In this perspective, we provide guidance on integrating empirical and theoretical approaches that account for the context dependency of seed dispersal to improve our ability to generalize and predict the consequences of dispersal, and its anthropogenic alteration, across systems. We synthesize suitable theoretical frameworks for this work and discuss concepts, approaches and available data from diverse subdisciplines to help operationalize concepts, highlight recent breakthroughs across research areas and discuss ongoing challenges and open questions. We address knowledge gaps in the movement ecology of seeds and the integration of dispersal and demography that could benefit from such a synthesis. With an interdisciplinary perspective, we will be able to better understand how global change will impact seed dispersal processes, and potential cascading effects on plant population persistence, spread and biodiversity.
    Description: Ideas for this manuscript initiated during the Seed Dispersal Workshop held in May 2016 at the Socio-Environmental Synthesis Center in Annapolis, MD and supported by the US National Science Foundation Grant DEB-1548194 to N.G.B. and the National Socio-Environmental Synthesis Center under the US National Science Foundation Grant DBI-1052875. D.Z. received funding from the Swiss National Science Foundation (SNF, grant: PZ00P3_168136/1) and from the German Science Foundation (DFG, grant: ZU 361/1-1).
    Keywords: Analytical models ; demography ; global change ; individual-based models ; long-distance seed dispersal ; population models ; seed dispersal
    Repository Name: Woods Hole Open Access Server
    Type: Article
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