Mwaskom/Seaborn: V0.8.1 (September 2017)

Waskom, Michael; Botvinnik, Olga; O'Kane, Drew; Hobson, Paul; Lukauskas, Saulius; Gemperline, David C; Augspurger, Tom; Halchenko, Yaroslav; Cole, John B.; Warmenhoven, Jordi; Ruiter, Julian de; Pye, Cameron R.; Hoyer, Stephan; Vanderplas, Jake; Villalba, Santi; Kunter, Gero; Quintero, E. A.; Bachant, Pete; Martin, Matthew J.; Meyer, Kyle; Miles, Alistair; Ram, Yoav; Yarkoni, Tal; Williams, Mike Lee; Evans, Constantine; Fitzgerald, Clark; Brian,; Fonnesbeck, Chris; Lee, Antony; Qalieh, Adel

doi:10.5281/zenodo.883859

Cited by 233 publications

(91 citation statements)

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“…All model calculations were encoded in Python 3.7.3 and performed using NumPy [47] and SciPy [48] while visualization was executed with matplotlib [49] and seaborn [50]. The source codes for all the calculations and figures were implemented in supplementary notebooks using Jupyter Notebook (http://jupyter.org/) and can be found at: http://doi.org/10.5281/zenodo.4421327 [51].…”

Section: Methodsmentioning

confidence: 99%

Size matters: tissue size as a marker for a transition between reaction–diffusion regimes in spatio-temporal distribution of morphogens

2022

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The reaction–diffusion model constitutes one of the most influential mathematical models to study distribution of morphogens in tissues. Despite its widespread use, the effect of finite tissue size on model-predicted spatio-temporal morphogen distributions has not been completely elucidated. In this study, we analytically investigated the spatio-temporal distributions of morphogens predicted by a reaction–diffusion model in a finite one-dimensional domain, as a proxy for a biological tissue, and compared it with the solution of the infinite-domain model. We explored the reduced parameter, the tissue length in units of a characteristic reaction–diffusion length, and identified two reaction–diffusion regimes separated by a crossover tissue size estimated in approximately three characteristic reaction–diffusion lengths. While above this crossover the infinite-domain model constitutes a good approximation, it breaks below this crossover, whereas the finite-domain model faithfully describes the entire parameter space. We evaluated whether the infinite-domain model renders accurate estimations of diffusion coefficients when fitted to finite spatial profiles, a procedure typically followed in fluorescence recovery after photobleaching (FRAP) experiments. We found that the infinite-domain model overestimates diffusion coefficients when the domain is smaller than the crossover tissue size. Thus, the crossover tissue size may be instrumental in selecting the suitable reaction–diffusion model to study tissue morphogenesis.

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Section: Methodsmentioning

confidence: 99%

Size matters: tissue size as a marker for a transition between reaction–diffusion regimes in spatio-temporal distribution of morphogens

2022

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“…To identify homologous positions between Mexican and reference sequences, we used the multiple sequence alignment generated by our local Nextstrain installation. We transformed the output file to study the incidence for 315 mutations grouped in 11 clades, and we studied their covariances between one another using in-house scripts with the R packages tidyverse [ 28 ] and circlize [ 29 ], and Python modules NumPy [ 30 ], Pandas [ 31 ], matplotlib [ 32 ], seaborn [ 33 ] - R version 4.0.4 [ 34 ], R Studio 1.4.1106 [ 35 ], Python 3.8 [ 36 ] and JupyterNotebook 6.1.4 [ 37 ] ( https://github.com/plissonf/Phylogenomics_SARS-CoV-2_Mexico ).…”

Section: Methodsmentioning

confidence: 99%

Phylogenomics and population genomics of SARS-CoV-2 in Mexico during the pre-vaccination stage reveals variants of interest B.1.1.28.4 and B.1.1.222 or B.1.1.519 and the nucleocapsid mutation S194L associated with symptoms

et al. 2021

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Understanding the evolution of the SARS-CoV-2 virus in various regions of the world during the Covid-19 pandemic is essential to help mitigate the effects of this devastating disease. We describe the phylogenomic and population genetic patterns of the virus in Mexico during the pre-vaccination stage, including asymptomatic carriers. A real-time quantitative PCR screening and phylogenomic reconstructions directed at sequence/structure analysis of the spike glycoprotein revealed mutation of concern E484K in genomes from central Mexico, in addition to the nationwide prevalence of the imported variant 20C/S:452R (B.1.427/9). Overall, the detected variants in Mexico show spike protein mutations in the N-terminal domain (i.e. R190M), in the receptor-binding motif (i.e. T478K, E484K), within the S1–S2 subdomains (i.e. P681R/H, T732A), and at the basis of the protein, V1176F, raising concerns about the lack of phenotypic and clinical data available for the variants of interest we postulate: 20B/478K.V1 (B.1.1.222 or B.1.1.519) and 20B/P.4 (B.1.1.28.4). Moreover, the population patterns of single nucleotide variants from symptomatic and asymptomatic carriers obtained with a self-sampling scheme confirmed the presence of several fixed variants, and differences in allelic frequencies among localities. We identified the mutation N:S194L of the nucleocapsid protein associated with symptomatic patients. Phylogenetically, this mutation is frequent in Mexican sub-clades. Our results highlight the dual and complementary role of spike and nucleocapsid proteins in adaptive evolution of SARS-CoV-2 to their hosts and provide a baseline for specific follow-up of mutations of concern during the vaccination stage.

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“…All plots and visualizations were done using Matplotlib 70 (v3.2.0) and Seaborn 71 (v0.11.0). Genome alignment visualizations were made using EasyFig 72 (v2.2.2) and Geneious Prime 2019.0.3.…”

Section: Methodsmentioning

confidence: 99%

vRhyme enables binning of viral genomes from metagenomes

Kieft

Adams

Salamzade

et al. 2021

Preprint

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Genome binning has been essential for characterization of bacteria, archaea, and even eukaryotes from metagenomes. Yet, no approach exists for viruses. We developed vRhyme, a fast and precise software for construction of viral metagenome-assembled genomes (vMAGs). vRhyme utilizes single- or multi-sample coverage effect size comparisons between scaffolds and employs supervised machine learning to identity nucleotide feature similarities, which are compiled into iterations of weighted networks and refined bins. Using simulated viromes, we displayed superior performance of vRhyme compared to available binning tools in constructing more complete and uncontaminated vMAGs. When applied to 10,601 viral scaffolds from human skin, vRhyme advanced our understanding of resident viruses, highlighted by identification of a Herelleviridae vMAG comprised of 22 scaffolds, and another vMAG encoding a nitrate reductase metabolic gene, representing near-complete genomes post-binning. vRhyme will enable a convention of binning uncultivated viral genomes and has the potential to transform metagenome-based viral ecology.

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Mwaskom/Seaborn: V0.8.1 (September 2017)

Cited by 233 publications

References 0 publications

Size matters: tissue size as a marker for a transition between reaction–diffusion regimes in spatio-temporal distribution of morphogens

Size matters: tissue size as a marker for a transition between reaction–diffusion regimes in spatio-temporal distribution of morphogens

Phylogenomics and population genomics of SARS-CoV-2 in Mexico during the pre-vaccination stage reveals variants of interest B.1.1.28.4 and B.1.1.222 or B.1.1.519 and the nucleocapsid mutation S194L associated with symptoms

vRhyme enables binning of viral genomes from metagenomes

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