Previously known as GrapeTree Extended.

• Description
• Use SPREAD
• Instruction to run it locally
  • Prerequisites
  • Install and use live-server
  • Run SPREAD locally
  • Load geoJSON or cooridates data
  • Load files
  • Save or load a compatible JSON file
  • Zooms for clusters
• Docker
• Videos
• Documentation
• Credits
• License
• Citation

The project is based on GrapeTree, a fully interactive, tree visualization program within EnteroBase, which supports facile manipulations of both tree layout and metadata. Please see more info here.

The idea was to extend the original project in order to conducts spatio-temporal analyses with an integrated geographic information system (GIS) as well as a data visualization system across time. The web application allows users to upload geographic coordinates and temporal data related to each sample and allows to display them reflecting the selection in the tree on the map and vice versa, and reproducing a timelapse visualization both in the map and in the tree.

You can download the project and easly running it locally, but we provide also a ready to use version freely available following link below:

• https://cohesive.izs.it/grapetree/spread/

Please note, you can load sensitive data, your dataset is visualised client-side in the browser. No data is transmitted, and no tracking cookies are used. The only data downloaded from the internet are the visualisation (JavaScript) code, fonts and map tiles.

To run the project locally you need to set a web server, node packages http-server or live-server works pretty fine.

• Node js. Download the latest stable release of NodeJS from https://nodejs.org and install it using all the default options.

Now you will be able to install live-server globally on your machine using the node package manager npm command line tool, this will allow you to run a web server from anywhere on your computer.

npm install live-server -g

To start a web server, in terminal open the directory containing your static web files and start the server with the following command:

live-server

Download the .zip of the code from this repository, then unzip on a directory DIR_SPREAD (for example: /tmp/spread).

Source files in place

Produce your newick (tree.nwk) and metadata (meta.tsv) files and copy them inside DIR_SPREAD, then on terminal:

live-server DIR_SPREAD

Now you will be able to load data by url in your web browser, for example:

http:https://localhost:8080?tree=tree.nwk&metadata=metadata.tsv

In the project you will find a datasets folder with some example data to load, you can use url to see them as well:

http:https://localhost:8080?tree=/datasets/test/tree.nwk&metadata=/datasets/test/metadata.tsv&geo=/datasets/test/points.geojson

The dashboard is able to recognize .geojson file passed as geo parameter by query string:

&geo=points.geojson

Alternatively you can integrate in metadata.tsv longitute and latitude values and pass them as query string parameters in the place of geoJSON. For example, if in the .tsv you define x for longitude and y for latitude, just add &longitude=x&latitude=y in the url in this way:

&metadata=metadata.tsv&longitude=x&latitude=y

Using our data example:

http:https://localhost:8080?tree=/datasets/test/tree.nwk&metadata=/datasets/test/metadata.tsv&longitude=x&latitude=y

Important

If you use longitude and latitude to name coordinates in the .tsv file, there is no need to pass them as query string parameters, so simply use:

&metadata=metadata.tsv

You can also directly upload files by dragging them over the tree, over the initial droppable area or using load buttons provided in the UI. In this case you don't need to user parameters in the url. You can drag or load a .nwk file followed by a .tsv file containing metadata and optionally a .geojson file containing geo-saptial information.

Important

.nwk file should be loaded before loading metadata or geoJson files.

Dashboard allows you to download a complete JSON file including metadata and configurations. Generated JSON file can be loaded with the same drag or load functionalities seen previously, this is very useful if you want to save your work or share it.

SPREAD can be used to visualize results generated with ReporTree. Very briefly, ReporTree allows the generation of a series of zooms on clusters by specifying samples of interest and parameters such as thresholds and distances.

In this context, it's possible to indicate to SPREAD the presence of available zooms using the methods described below.

Please note. Here, we describe how to set up and place the needed newick and metadata files for zooms. However, if you produce these files directly with ReporTree, the structure, composition, and positions are already correct.

Here the minimum metadata mandatory schema:

Insert the folders containing the zooms (a newick and optionally a related .tsv metadata file) at the same level as the general tree. Following the output generated by ReporTree, the names of folders and files should adhere to the following conventions:

• prefix_threshold-definition_cluster-name: for example, Reportree_MST-7x1.0_cluster_334
• prefix_sample-id_closest-name: for example, Reportree_sample-01_closest5

For a concrete example, the structure should follow this convention:

Reportree.nwk
Reportree_metadata_w_partitions.tsv
Reportree_MST-7x1.0_cluster_335
  |__ MST-7x1.0_cluster_335.nwk
  |__ MST-7x1.0_cluster_335_metadata_w_partitions.tsv
Reportree_MST-7x1.0_cluster_887
  |__ MST-7x1.0_cluster_887.nwk
  |__ MST-7x1.0_cluster_887_metadata_w_partitions.tsv
...

Once the zooms folders are moved as shown, it is necessary to specify some parameters, and this can be done in two ways.

There are 2 parameters to specify: zooms, which is mandatory, and zooms_prefix, which is optional.

• zooms parameter can have one of the following syntax:
  • zooms=category,sample%20of%20interest@5,7: the code will find in the general metadata file the clusters for the samples marked as sample of interest for the indicated thresholds.
  • zooms=category,sample%20of%20interest@all: the code will find in the general metadata file the clusters for the samples marked as sample of interest for all thresholds.
  • zooms=@7,21: The code will use category and sample of interest by default to find clusters in the general metadata file for the indicated thresholds.
  • zooms=all: The code will use category and sample of interest by default to find clusters in the general metadata file for all thresholds.

Please note. As permitted by ReporTree, different thresholds can be separated by commas (e.g., @5,8,16), but you can also define ranges by specifying them with a hyphen to separate the minimum and maximum (e.g., @5,8,10-20).

• zooms_prefix should define a prefix used for files and folders. This is optional, by default application take it from the .nwk file name.
  • zooms_prefix=zoom indicates that folder names and file names start with zoom_.

At the same level as the zoom folders, alternatively, it's possible to insert a .txt file (for example zooms.txt) containing a simple list of prefix_threshold-name_cluster-name for each zoom folder in the full tree root. By using this index, it is possible to cover a second use case, that of closest zooms, and it is also possible to indicate elements in the list composed as follows: prefix_sample-name_closest-name. To give a more concrete example:

Reportree_MST-7x1.0_cluster_335
Reportree_MST-7x1.0_cluster_510
Reportree_MST-7x1.0_cluster_519
Reportree_MST-15x1.0_cluster_556
Reportree_sample-01_closest5
Reportree_sample-01_closest10
Reportree_sample-02_closest5
Reportree_sample-02_closest10

then add in the URL one of the following parameters:

• zooms_list=category,sample%20of%[email protected]: the code will find in the general metadata file the clusters for the samples marked as sample of interest for zooms indicated by zooms.txt file.
• [email protected] or zooms_list=zooms.txt: the code will use category and sample of interest by default to find clusters in the general metadata file for zooms indicated by zooms.txt file.

Please note. The prefix should match the name of the .nwk file.

docker pull ghcr.io/genpat-it/spread:latest
docker run -d -p 3000:80 --name spread ghcr.io/genpat-it/spread:latest

You can surf the spread instance by visiting http:https://<IP_ADDRESS_OR_HOSTNAME>:3000 in your web browser.

To build the Docker image from the source code, follow these steps:

1. Clone the repository:

git clone https://github.com/genpat-it/spread

2. Navigate to the cloned directory:

cd spread

3. Build the Docker image using the provided Dockerfile. You can also specify the port using the PORT build argument:

docker build . -t spread --build-arg PORT=3000

4. Once the image is built, you can run the Docker container:

docker run -d spread

This will expose the application running inside the container on port 3000 of your host machine.

• Video Settings: Watch Video - This video demonstrates the settings configuration in SPREAD.

• Video Upload: Watch Video - This video shows how to upload data in SPREAD.

• https://cohesive.izs.it/wiki/user/Dashboards/GrapeTree/GrapeTree_extended.html
• https://enterobase.readthedocs.io/en/latest/grapetree/grapetree-about.html

• https://github.com/achtman-lab/GrapeTree

• https://www.openstreetmap.org/
• https://leafletjs.com/

• https://github.com/ag-grid/ag-grid

• https://github.com/bgrins/spectrum

• https://iconic.app/

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http:https://www.gnu.org/licenses/.

If you run SPREAD, please cite publications:

de Ruvo, A., De Luca, A., Bucciacchio, A., Castelli, P., Di Lorenzo, A., Radomski, N., Di Pasquale, A. SPREAD: Spatiotemporal Pathogen Relationships and Epidemiological Analysis Dashboard. Veterinaria Italiana, Vol. 60 No. 4 (2024): Special Issue GeoVet23. https://doi.org/10.12834/VetIt.3476.23846.1

De Luca, A., de Ruvo, A., Di Lorenzo, A., Bucciacchio, A., Di Pasquale, A. GrapeTree integration with spatio-temporal data visualization: a holistic understanding of diseases and the transmission pathways. GeoVet23 (2023). Poster

Also, SPREAD relies on the work of other developers. So, there are other tools that you must cite:

• ReporTree: https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-023-01196-1
• GrapeTree: http:https://www.genome.org/cgi/doi/10.1101/gr.232397.117