Genomic Data Visualisation with JBrowse
OverviewQuestions:Objectives:
How can I visualise features or blast data?
How can I visualise sequencing data in a workflow
Requirements:
Build several visualisations in JBrowse
Have basic familiarity with moving around JBrowse, and loading several data tracks
Time estimation: 1 hourSupporting Materials:Last modification: Oct 18, 2022
Introduction
JBrowse (Buels et al. 2016) is a fast, embeddable genome browser built completely with JavaScript and HTML5, with optional run-once data formatting tools written in Perl.
from http://jbrowse.org/
The Galaxy tool accepts data in many formats:
- Intervals/Annotation/Feature Tracks (GFF/GFF3, BED, GenBank)
- BAM Pileups
- Blast XML results
- Wig/BigWig
- VCF files
and executes the “run-once data formatting tools” mentioned in its description. The JBrowse tool has an incredibly extensive number of options, more than anyone needs most of the time. We’ll go through them in detail but feel free to skip the sections that don’t apply to the data types you use. Not everyone has Blast results to visualise. This tutorial also is very hands off, we will give some guidance but it is up to you to explore JBrowse and consider ways in which it can apply to your own data. Unlike other tutorials there is no biological story here, just some ways you can visualise common datatypes.
This tutorial covers version 1.16.5+ of the JBrowse tool, earlier versions will have different behaviour and tool layout.
AgendaIn this tutorial, we will deal with:
Data Upload
Hands-on: Getting the data
Create and name a new history for this tutorial.
Click the new-history icon at the top of the history panel.
If the new-history is missing:
- Click on the galaxy-gear icon (History options) on the top of the history panel
- Select the option Create New from the menu
Import the datasets we will visualize:
https://zenodo.org/record/3591856/files/blastp%20genes.gff3 https://zenodo.org/record/3591856/files/blastp%20vs%20swissprot.xml https://zenodo.org/record/3591856/files/dna%20sequencing.bam https://zenodo.org/record/3591856/files/dna%20sequencing%20coverage.bw https://zenodo.org/record/3591856/files/genes%20(de%20novo).gff3 https://zenodo.org/record/3591856/files/genes%20(NCBI).gff3 https://zenodo.org/record/3591856/files/genome.fa https://zenodo.org/record/3591856/files/RNA-Seq%20coverage%201.bw https://zenodo.org/record/3591856/files/RNA-Seq%20coverage%202.bw https://zenodo.org/record/3591856/files/variants.vcf
- Copy the link location
Open the Galaxy Upload Manager (galaxy-upload on the top-right of the tool panel)
- Select Paste/Fetch Data
Paste the link into the text field
Press Start
- Close the window
The data for today is a subset of real datasets from E. coli MG1655 strain K-12
Simple Gene Tracks
We will start by adding a couple of gene call tracks. In our case these are genes and gene predictions, but they don’t have to be. In the general case this can be any interesting region from an analysis, where a tool has pointed out some region for further inspection for some reason, then this data can be visualised with the “gene” track type.
Hands-on: Build the JBrowse
- JBrowse Tool: toolshed.g2.bx.psu.edu/repos/iuc/jbrowse/jbrowse/1.16.9+galaxy0 with the following parameters:
- “Reference genome to display”:
Use a genome from history
- param-file “Select the reference genome”:
genome.fa
- “Genetic Code”:
11. The Bacterial, Archaeal and Plant Plastid Code
- In “Track Group”:
- param-repeat “Insert Track Group”
- “Track Category”:
Genes
- In “Annotation Track”:
- param-repeat “Insert Annotation Track”
- “Track Type”:
GFF/GFF3/BED Features
- param-file “GFF/GFF3/BED Track Data”:
genes (de novo).gff3
Execute
View the contents of the file
If you see this error message, click OK, this is a known bug.
If you are not familiar with the operation of JBrowse there are some important points:
- “Tracks” are shown on the left. Clicking the checkboxes will make the tracks visible or invisible
- You can use your mouse scrollwheel to move around the genome view area, or you can click and drag to move.
- Double clicking will zoom in on the genome, or you can use the magnifying glass icons to zoom in our out
- The JBrowse tool takes track names directly from file names
- If you want to rename tracks: Click on the pencil icon, edit the Name and click Save.
- You can now re-run the JBrowse tool and it will produce a new JBrowse instance with corrected names.
Complex Gene Tracks
All of the track types in the JBrowse tool support a wide array of features. We’ve only looked at a simple track with default options, however there are more tools available to us to help create user-friendly JBrowse instances that can embed rich data.
Hands-on: Build a JBrowse for viewing Genes
- JBrowse Tool: toolshed.g2.bx.psu.edu/repos/iuc/jbrowse/jbrowse/1.16.9+galaxy0 with the following parameters:
- “Reference genome to display”:
Use a genome from history
- param-file “Select the reference genome”:
genome.fa
- “Genetic Code”:
11. The Bacterial, Archaeal and Plant Plastid Code
- In “Track Group”:
- param-repeat “Insert Track Group”
- “Track Category”:
Genes
- In “Annotation Track”:
- param-repeat “Insert Annotation Track”
- “Track Type”:
GFF/GFF3/BED Features
- param-file “GFF/GFF3/BED Track Data”:
genes (de novo).gff3
- “JBrowse Track Type [Advanced]”:
Canvas Features
- In “JBrowse Feature Score Scaling & Coloring Options [Advanced]”:
- “Color Score Algorithm”:
Based on score
- “How should minimum and maximum values be determined for the scores of the features”:
Manually specify minimum and maximum expected scores for the feature track
- “Minimum expected score”:
0
- “Maximum expected score”:
1
- param-repeat “Insert Annotation Track”
- “Track Type”:
GFF/GFF3/BED Features
- param-file “GFF/GFF3/BED Track Data”:
genes (NCBI).gff3
- “JBrowse Track Type [Advanced]”:
Canvas Features
In “JBrowse Contextual Menu options [Advanced]”:
We’re going to add a “Contextual Menu” which deserves a little explanation first. In JBrowse terminology the right click menu of some features is called the contextual menus. You can customize this menu to add new links and options that will be useful to the user. These links can be templated with variables based on metadata of the feature that the user clicked upon.
There are more valid values for templating than mentioned in the help. When you click on a feature in the JBrowse instance, it will present all of the properties of the feature. Any of the top level properties can be used directly in your templating
- In “Track Menu”:
- param-repeat “Insert Track Menu”
- “Menu label”:
See protein at NCBI
- “Menu url”:
https://www.ncbi.nlm.nih.gov/gene?term={locus_tag}%5BGene%20Name%5D
- “Menu icon”:
Database
View the output
Turn on both tracks of data.
Navigate to
21,200
, either manually, or by copying and pasting the location block:NC_000913.3:18351..24780
Right click on the
yaaY
gene, and click the “See protein at NCBI” menu option.This menu option is dynamic, try it with a few other features from the
genes (NCBI).gff3
track. These features have alocus_tag
and the menu button we added will open a URL to an NCBI search page for the value of thislocus_tag
attribute.
Contextual menus can be used to link to more than just NCBI.
- The links can go anywhere such as web search services (e.g. Google) or genomics web services (e.g. EBI)
- Some sites use the IFrame action to link genes to local services where users are expected to submit annotation notes or data.
Sequencing, Coverage, and Variation
This is the next major category of data that people wish to visualize: sequencing, coverage, and variation. The sequencing data can be of any type, and does not necessarily need to be sequencing data, as long as the results are formatted as BAM files. Here we will add several coverage tracks (essentially a line plot along the genome, associating a position with a value) with various visualisation options like scaling and display type. Each of these visualisation options can be useful in different situations, but it largely is a matter of preference or what you are used to seeing.
Next we will add a sequencing dataset, a BAM file which maps some sequencing reads against various locations along the genome. JBrowse helpfully highlights which reads have mapping issues, and any changes in bases between the reads and the genome. We “Autogenerate a SNP track”, which produces an extra track we can enable in JBrowse. This track reads the same BAM file used for visualising reads, and then produces a SNP and coverage visualisation. NB: This only works for small BAM files, if your files are large (>200 Mb), then you should consider generating these coverage and SNP tracks by other means (e.g. bamCoverage
and FreeBayes
or similar tools) as it will be significantly faster. You can learn more about generating these files in the Mapping tutorial.
Hands-on: Hands-on
- JBrowse Tool: toolshed.g2.bx.psu.edu/repos/iuc/jbrowse/jbrowse/1.16.9+galaxy0 with the following parameters:
- “Reference genome to display”:
Use a genome from history
- param-file “Select the reference genome”:
genome.fa
- “Genetic Code”:
11. The Bacterial, Archaeal and Plant Plastid Code
- In “Track Group”:
- param-repeat “Insert Track Group”
- “Track Category”:
Coverage
- In “Annotation Track”:
- param-repeat “Insert Annotation Track”
- “Track Type”:
BigWig XY
- param-file “BigWig Track Data”:
dna sequencing coverage.bw
- param-repeat “Insert Annotation Track”
- “Track Type”:
BigWig XY
- param-file “BigWig Track Data”:
rna-seq coverage/1.bw
- “Use XYPlot”:
Yes
- param-repeat “Insert Annotation Track”
- “Track Type”:
BigWig XY
- param-file “BigWig Track Data”:
rna-seq coverage/2.bw
- “Use XYPlot”:
Yes
- “Show variance band”:
Yes
- “Track Scaling”:
Autoscale (global)
- param-repeat “Insert Track Group”
- “Track Category”:
Sequencing & Variation
- In “Annotation Track”:
- param-repeat “Insert Annotation Track”
- “Track Type”:
BAM Pileups
- param-file “BAM Track Data”:
sequencing.bam
- “Autogenerate SNP Track”:
Yes
- param-repeat “Insert Annotation Track”
- “Track Type”:
VCF SNPs
- param-file “SNP Track Data”:
variants.vcf
Execute and then explore the resulting data.
Try:
- Clicking on individual variations in the dedicated
variants.vcf
track - Hovering over the autogenerated SNPs/Coverage track
- Clicking on individual reads of the
sequencing.bam
track - Changing the visualisation options of the BAM track
Blast Results
The Blast visualisation module requires that you have a gff3 formatted set of features which you then exported as DNA or protein, and blasted. The reason is easy to understand: when you extract DNA/protein sequences for Blasting, this process looses information about where these sequences were along the genome. The results from Blast retains the identifiers from the DNA/protein sequences, so we need to “map” these identifiers, to proper features with locations.
The best way to accomplish this is through the gffread
tool which can cleanup a gff3 file, and export various features, optionally translating them. With these outputs, the cleaned features and fasta
formatted sequences, you can Blast the sequences, and then supply the resulting Blast XML outputs in addition to the cleaned features, allowing a script to re-associate these Blast results to their original locations along the genome.
Hands-on: Building a JBrowse for Blast results
- JBrowse Tool: toolshed.g2.bx.psu.edu/repos/iuc/jbrowse/jbrowse/1.16.9+galaxy0 with the following parameters:
- “Reference genome to display”:
Use a genome from history
- param-file “Select the reference genome”:
genome.fa
- “Genetic Code”:
11. The Bacterial, Archaeal and Plant Plastid Code
- In “Track Group”:
- param-repeat “Insert Track Group”
- In “Annotation Track”:
- param-repeat “Insert Annotation Track”
- “Track Type”:
Blast XML
- param-file “BlastXML Track Data”:
blastp vs swissprot.xml
- param-file “Features used in Blast Search”:
blastp genes.gff3
- “Minimum Gap Size”:
5
- “Is this a protein blast search?”:
Yes
- In “JBrowse Feature Score Scaling & Coloring Options [Advanced]”:
- “Color Score Algorithm”:
Based on score
- “JBrowse style.color function’s score scaling”:
Blast scaling
Execute and then explore the resulting data.
Conclusion
This does not exhaustively cover JBrowse, and the tool is more extensible than can be easily documented, but hopefully these examples are illustrative and can give you some ideas about your next steps.
Key points
This tutorial can not exhaustively cover every data type, but maybe it provides inspiration for your own analyses
JBrowse is a great, workflow-compatible alternative to other genome browsers
You can build visualisations that summarise dozens of analyses in one visualisation
Frequently Asked Questions
Have questions about this tutorial? Check out the tutorial FAQ page or the FAQ page for the Visualisation topic to see if your question is listed there. If not, please ask your question on the GTN Gitter Channel or the Galaxy Help ForumReferences
- Buels, R., E. Yao, C. M. Diesh, R. D. Hayes, M. Munoz-Torres et al., 2016 JBrowse: a dynamic web platform for genome visualization and analysis. Genome Biology 17: 10.1186/s13059-016-0924-1
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Citing this Tutorial
- Helena Rasche, Saskia Hiltemann, Erasmus+ Programme, 2022 Genomic Data Visualisation with JBrowse (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/visualisation/tutorials/jbrowse/tutorial.html Online; accessed TODAY
- Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012
Congratulations on successfully completing this tutorial!@misc{visualisation-jbrowse, author = "Helena Rasche and Saskia Hiltemann and Erasmus+ Programme", title = "Genomic Data Visualisation with JBrowse (Galaxy Training Materials)", year = "2022", month = "10", day = "18" url = "\url{https://training.galaxyproject.org/training-material/topics/visualisation/tutorials/jbrowse/tutorial.html}", note = "[Online; accessed TODAY]" } @article{Batut_2018, doi = {10.1016/j.cels.2018.05.012}, url = {https://doi.org/10.1016%2Fj.cels.2018.05.012}, year = 2018, month = {jun}, publisher = {Elsevier {BV}}, volume = {6}, number = {6}, pages = {752--758.e1}, author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{\i}}rez and Devon Ryan and Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Björn Grüning}, title = {Community-Driven Data Analysis Training for Biology}, journal = {Cell Systems} }