Árbol del tiempo   NWK   🔰
Diagrama de las perlas   NWK     SVG   🔰

CoVizu

en es fr zh

Visulización en tiempo real de la variación genómica de SARS-CoV-2 (hCoV-19)

Habilitado por datos de 

Pasa sobre las accesiones (EPI_ISL_#) para mostrar información sobre laboratorios y autores

CoVizu es un proyecto de código abierto creado con el objetivo de visualizar la diversidad global de los genomas de SARS-CoV2, obtenidos a partir de la iniciativa GISAID.

Este sitio está compuesto de dos visualizaciones interactivas. En la izquierda, se muestra un árbol filogenético que resume las relaciones evolutivas entre diferentes linajes de SARS-CoV-2 (agrupar virus con genomas similares es útil para detectar brotes relaciones en diferentes lugares; Rambaut et al. 2020). Puedes navegar entre diferentes linajes haciendo click en sus respectivas ramas.

Al seleccionar un linaje, se muestra una visulización de "beadplot" en el centro de la página. Cada línea horizontal representa una o más muestras de SARS-CoV-2 con la misma secuencia genómica. Líneas verticales a lo largo de dicha línea representa las fechas en las cuales estas variantes fueron colectadas.

Para más ayuda has click en los 🔰íconos.

Un árbol filogenético es un modelo que permite establecer relaciones entre diferentes poblaciones que comparten un ancestro común. La filogenia mostrada aquí (generada con TreeTime v0.8.0) resume el ancestro común de los diferentes linajes de SARS-CoV-2, que han sido definidos agrupando virus de acuerdo a la similitud de sus genomas.

A time scale is drawn above the tree marked with dates. The earliest ancestor (root) is drawn on the left, and the most recent observed descendants are on the right. We estimate the dates of common ancestors by comparing the sampled genomes and assuming a constant rate of evolution.

For each lineage, we draw a rectangle to summarize the range of sample collection dates, and colour it according to the geographic region it was sampled most often. To explore the samples within a lineage, click on the label (e.g., "B.4") or the rectangle to retrieve the associated beadplot.

We use beadplots to visualize the different variants of SARS-CoV-2 within a lineage, where and when they have been sampled, and how they are related to each other. Every object in the beadplot has additional info in a tooltip (which you view by hovering over that object with your mouse pointer).

Each horizontal line segment represents a variant – viruses with identical genomes. We draw beads along a line to indicate when that variant was sampled. If there are no beads on the line and it is grey, then it is an unsampled variant: two or more sampled variants descend from an ancestral variant that has not been directly observed.

The area of the bead is scaled in proportion to the number of times the variant was sampled that day. This is important for rapid or intensively-sampled epidemics, e.g., lineage D.2 in Australia. Beads are coloured with respect to the most common geographic region of the samples.

We draw vertical line segments to connects variants to their common ancestors. These relationships are estimated by the neighbor-joining method using RapidNJ. Tooltips for each edge report the number of genetic differences (mutations) between ancestor and descendant as the "genomic distance". Since it's difficult to reconstruct exactly when these mutations occurred, we simply map each line to when the first sample was collected.

We would like to thank the GISAID Initiative and are grateful to all of the data contributors, i.e. the Authors, the Originating laboratories responsible for obtaining the specimens, and the Submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based.

Elbe, S., and Buckland-Merrett, G. (2017) Data, disease and diplomacy: GISAID’s innovative contribution to global health. Global Challenges, 1:33-46.
DOI: 10.1002/gch2.1018   PMCID: 31565258

Note: When using results from these analyses in your manuscript, ensure that you also acknowledge the Contributors of data, i.e. “We gratefully acknowledge all the Authors, the Originating laboratories responsible for obtaining the specimens, and the Submitting laboratories for generating the genetic sequence and metadata and sharing via the GISAID Initiative, on which this research is based.”

Also, cite the following reference: Shu, Y., McCauley, J. (2017) GISAID: From vision to reality. EuroSurveillance, 22(13)
DOI: 10.2807/1560-7917.ES.2017.22.13.30494   PMCID: PMC5388101