The practical aims at familiarzing you with the Panoply Galaxy interactive tool. Panoply is among the most popular tool to visualize geo-referenced data stored in Network Common Data Form (netCDF). It provides a graphical interface for inspecting (show metadata) and visualizing netCDF data. It supports many features to customize your plots and we will introduce some of them in this lesson.
In this tutorial, we will be focusing on the usage of Biodiversity data in Network Common data Form (netCDF) because it is the data format used to store data on the EBV data portal.
We will be using a freely available dataset representing Essential Biodiversity Variables from GEO BON data portal. We will learn to use panoply to visualize the Local bird diversity for last century.
NetCDF format
NetCDF data format is a binary format and to be able to read or visualize it, we would need to use dedicated software or libraries that can handle this “special” format. It is self-describing and machine-independent data format that supports the creation, access, and sharing of array-oriented scientific data. NetCDF files usually have the extension .nc or .netcdf.
For climate and forecast data stored in NetCDF format there are (non-mandatory) conventions on metadata (CF Convention).
Click galaxy-uploadUpload Data at the top of the tool panel
Select galaxy-wf-editPaste/Fetch Data
Paste the link(s) into the text field
Press Start
Close the window
As an alternative to uploading the data from a URL or your computer, the files may also have been made available from a shared data library:
Go into Data (top panel) then Data libraries
Navigate to the correct folder as indicated by your instructor.
On most Galaxies tutorial data will be provided in a folder named GTN - Material –> Topic Name -> Tutorial Name.
Select the desired files
Click on Add to Historygalaxy-dropdown near the top and select as Datasets from the dropdown menu
In the pop-up window, choose
“Select history”: the history you want to import the data to (or create a new one)
Click on Import
Check that the datatype is netcdf
Files you uploaded are in netcdf format. In Galaxy, Datatypes are, by default, automatically guessed. Here, as netcdf is a derivative of the h5 format, Galaxy automatically affect the h5 datatype to netcdf files. To cope with that, one can change the datatype manually, once datasets uploaded (as shown below) OR you can directly specify datatype on the upload tool form so Galaxy will not try to automatically guess it.
Click on the galaxy-pencilpencil icon for the dataset to edit its attributes
In the central panel, click galaxy-chart-select-dataDatatypes tab on the top
In the galaxy-chart-select-dataAssign Datatype, select datatypes from “New type” dropdown
Tip: you can start typing the datatype into the field to filter the dropdown menu
Click the Save button
Rename Datasetsgalaxy-pencil
As the original name martins_comcom_id1_20220208_v1.nc can be not so good to use, don’t hesitate to modify it in Local bird diversity (cSAR/BES-SIM) martins dataset for example.
Click on the galaxy-pencilpencil icon for the dataset to edit its attributes
In the central panel, change the Name field
Click the Save button
Add a taggalaxy-tags to the dataset corresponding to #EBV
Datasets can be tagged. This simplifies the tracking of datasets across the Galaxy interface. Tags can contain any combination of letters or numbers but cannot contain spaces.
To tag a dataset:
Click on the dataset to expand it
Click on Add Tagsgalaxy-tags
Add tag text. Tags starting with # will be automatically propagated to the outputs of tools using this dataset (see below).
Press Enter
Check that the tag appears below the dataset name
Tags beginning with # are special!
They are called Name tags. The unique feature of these tags is that they propagate: if a dataset is labelled with a name tag, all derivatives (children) of this dataset will automatically inherit this tag (see below). The figure below explains why this is so useful. Consider the following analysis (numbers in parenthesis correspond to dataset numbers in the figure below):
a set of forward and reverse reads (datasets 1 and 2) is mapped against a reference using Bowtie2 generating dataset 3;
dataset 3 is used to calculate read coverage using BedTools Genome Coverageseparately for + and - strands. This generates two datasets (4 and 5 for plus and minus, respectively);
datasets 4 and 5 are used as inputs to Macs2 broadCall datasets generating datasets 6 and 8;
datasets 6 and 8 are intersected with coordinates of genes (dataset 9) using BedTools Intersect generating datasets 10 and 11.
Now consider that this analysis is done without name tags. This is shown on the left side of the figure. It is hard to trace which datasets contain “plus” data versus “minus” data. For example, does dataset 10 contain “plus” data or “minus” data? Probably “minus” but are you sure? In the case of a small history like the one shown here, it is possible to trace this manually but as the size of a history grows it will become very challenging.
The right side of the figure shows exactly the same analysis, but using name tags. When the analysis was conducted datasets 4 and 5 were tagged with #plus and #minus, respectively. When they were used as inputs to Macs2 resulting datasets 6 and 8 automatically inherited them and so on… As a result it is straightforward to trace both branches (plus and minus) of this analysis.
Click on martins_comcom_id1_20220208_v1.nc dataset and open.
Inspect metadata
Hands-on: Inspect dataset
Inspect dataset content
Here you can look at the dataset (martins_comcom_id1_20220208_v1.nc) and related variables (crs, entity, lat, lon, metric_1, ebv_cube, time)
Question
what is the unit of the ebv_cube variable of metric_1and its shape?
The unit of ebv_cube is “Percentage points”. ebv_cube is a 4D array (entity, time, latitude, longitude).
Take a look at the general properties of the dataset
Question
Can you find the title, summary, EBV class and EBV name informations?
Double click on “martins_comcom_id1_2022…” element of the “Name” column to display these general information
Title: Local bird diversity (cSAR/BES-SIM).
Summary: Changes in bird diversity at 1-degree resolution caused by land use, estimated by the cSAR model for 1900-2015 using LUH2.0 historical reconstruction of land-use.
EBV Class: Community composition.
EBV name: Taxonomic and phylogenetic diversity.
Question
Can you find the biodviersity metrics names?
metric_1: Relative change in the number of species (%)
metric_2: Absolute change in the number of species
Create Geo-referenced Longitude-Latitude plot
Hands-on: geographical map
Double click on the variable ebv_cube from metric_1 and click on CreateOpen image in new tab
Figure 2: Create map
Question
What does it shows?
What is the date of the generated plot?
Can you plot other dates?
The plot represent the relative change in the number of species (%).
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Figure 3: Plot map
The date of the default plot is 1st January 1900 at 00:00.
To plot another date, change either:
Initial time of forecast (give a value between 1 and 12, corresponding to years between 1900 and 2010.
Click on the date and scroll down to select the date of your choice.
Save your plot
Click on the tab File (from your plot window) to store your plot by selecting Save Image As
Double click on the folder outputs to enter this folder and save your plot.
You need to make sure to save all your plot in the outputs folder otherwise you can loose all your plots once to close panoply.
Change colormap
Always make sure you use color blind friendly palettes.
To change the default colormap, click on tab “Scale” (bottom of your plot window) and select another “Color Table” (you can scroll down to go through all the different available colormap).
Save your plot using Save Image As and make sure to choose another name to avoid overwritting your preceding plot.
From your previous plot window, click on File and select Export Animation. Save your plot using either MOV or AVI format.
It goes through each plot e.g. for each month and create an animation where you can see the evolution of sea-ice extent from January 1979 to December 1979.
You will be able to download the resulting movie from Galaxy once you quit Panoply.
Quit Panoply
Hands-on: Quit Panoply to keep your plots
To make sure all your plots stored in outputs folder get exported to Galaxy, you need to quit panoply:
File –> Quit Panoply.
Go back to your current Galaxy history and you should find Panoply outputs
We have now learnt how to visualize EBV cube data using Panoply. We only use one of the two datasets so we strongly encourage you to do the same exercises with others datasets from EBV data portal.
You've Finished the Tutorial
Please also consider filling out the Feedback Form as well!
Key points
Inspect and view EBV cube netCDF data with Panoply
Further information, including links to documentation and original publications, regarding the tools, analysis techniques and the interpretation of results described in this tutorial can be found here.
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Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012
@misc{ecology-panoply_ebv,
author = "pndb and Yvan Le Bras and Coline Royaux and Marie Josse and Anne Fouilloux",
title = "Visualize EBV cube data with Panoply netCDF viewer (Galaxy Training Materials)",
year = "",
month = "",
day = ""
url = "\url{https://training.galaxyproject.org/training-material/topics/ecology/tutorials/panoply_ebv/tutorial.html}",
note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
doi = {10.1371/journal.pcbi.1010752},
url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
year = 2023,
month = {jan},
publisher = {Public Library of Science ({PLoS})},
volume = {19},
number = {1},
pages = {e1010752},
author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
editor = {Francis Ouellette},
title = {Galaxy Training: A powerful framework for teaching!},
journal = {PLoS Comput Biol} Computational Biology}
}
Funding
These individuals or organisations provided funding support for the development of this resource
PNDB
Yvan Le Bras
Coline Royaux
Marie Josse
Anne Fouilloux
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