Download Trackster+Circster

TRACKSTER &CIRCSTER DEMO
Slides: /g/funcgen/trainings/visualization/Demos/Trackster+Circster.ppt
Galaxy:http://gbcs.embl.de/galaxy/
Galaxy Dev: http://gbcs-dev.embl.de/galaxy-dev/
What is Trackster?
• Galaxy's visualization and visual analysis environment.
• Genome browser for high throughput sequencing data.
• It lets you visualize your SAM/BAM, BED, GFF/GTF, WIG,
bigWig, bigBed, bedGraph, and VCF datasets from within
Galaxy.
• Works with large NGS/HTS datasets.
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Visualization in Trackster
Visualization by two track classes:
- Line Tracks: display quantitative data along a y-axis
- wig, bigWig, bigBed
-Drawing modes: Histogram, Line, Filled, Intensity
- FeatureTracks: display feature interval data
- BED,BAM/SAM, gff, gtf, vcf
-Drawing modes: Automatic, Coverage, Dense, Squish, Pack
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I.Importing Data Libraries in Galaxy
1.Click on ‘Shared Data’ in the Galaxy Menu and select ‘Data
Libraries’
2.All the shared data libraries will be displayed. Select ‘Training’.
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Importing Data Libraries to current History
3.Two folders containing ChIP-Seq and RNA-Seq data will be visible. Select
both and ‘Import to current history’. All data for this demo is from Chr 4 in
D.melanogaster (dm3).
Or you can select them and right-click for importing them into other histories of
your choice.
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Importing Library Datasets into
selected histories
4.You can select the library datasets and choose to import them in one of your
existing histories.
Or you can import the selected datasets into a new history named, for
example, ‘Training-RNA-Seq,etc’.
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Importing Chip-Seq datasets into
new History
Follow the same procedure to import the ChIP-Seq datasets from the Library
You can either import them into an existing history or create a new one,
for eg:Training-ChIP-Seq Visualization
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II.Managing Histories
Now, select ‘Analyze Data’ in the Galaxy Menu and click on the gear icon in
the History Panel.A drop down menu with numerous options can be seen.
‘Saved Histories’ will display all your histories as a list. You can click on the
desired History to make it your current history.
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Managing Histories(2)
By right clicking on a History name, you can also view options to share,
rename or delete this history.
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III. Visualizing data from your Galaxy
History :Editing Dataset Attributes
Now, select the History containing the RNA-Seq and other datasets.
Incase the database is not shown as
dm3 for a dataset, click on the pencil
icon to edit attributes.
The correct Database/build can be
selected under the ‘Attributes’ Tab
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Datasets used in this tutorial
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VCF- Variant Call Format
BedGraph
Gff3
Gtf
Bam
Bed
Wig
BigWig
BigBed
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Visualizing data from your Galaxy
History in Trackster
Look for the ‘mapped4.vcf’ dataset in the RNA-Seq history.
Click on the ‘Visualize’ icon under the dataset name in the ‘History’ panel
and select ‘Trackster’.
History Panel in Galaxy
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Viewing data in a New or Saved
Visualization
Datasets can be viewed in either a new visualization or in a
previously saved one. Click on ‘View in new visualization’ to
start with a fresh visualization.
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Viewing data in a New Visualization
1.Enter a name in the ‘Browser name’ field. For example: ‘TrainingRNA-Seq,etc’.
2.Select the Reference genome build as ‘D.melanogaster (EMBL)
(dm3)’.
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IV. Visualizing the dataset in Trackster
Once the visualization is created, select chr4 from the drop down
menu, as the datasets used for this training only contain chr4 data for
practical purposes.
Selecting chr4 for mapped4.vcf should result in one of the following
visualizations. Click on the Floppy Disc icon in the upper right corner to
save your visualization.
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Adding Tracks to the saved visualization
1.Click on the first icon in the upper-right corner to ‘Add Tracks’.
2.Select the desired datasets from your history. For instance, select the
Genome Coverage bedgraph and click on ‘Add’ at the bottom of the pane.
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Adding Tracks to the saved visualization(2)
3.Now, you should be able to visualize both datasets in Trackster. Colour
of the tracks may vary. Save the visualization again.
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Adding datasets to the saved Visualization
from Galaxy
This is an alternate way of adding datasets to your Trackster
Visualization.
1.After saving your visualization, click on ‘Analyze Data’ to go back to
the main Galaxy window and select another dataset (for instance, the
‘refgenedm3.gff3’) to be added in this visualization.
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Adding datasets to the saved
visualization from Galaxy (2)
2.Click on ‘View in Saved Visualization’
3.Select your visualization (‘Training-RNA-Seq,etc’) and then click on
‘Add to visualization’.
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Viewing and Zooming in on datasets
Now, you should be able to view the three datasets in your
visualization. Remember to keep saving your work.
You can zoom in on the datasets by clicking on the magnifying glass icon
with a ‘+’ and zoom out by clicking on the magnifying glass icon with a ‘-’.
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V. Displaying the genome annotation
In order to visualize the genome annotation along with your tracks, go back to
the main Galaxy window by clicking on ‘Analyze data’.
From the ‘Tools’ pane, select ‘Get Data’ and then click on ‘UCSC Main’.
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Displaying the genome annotation (2)
In the Table Browser, select ‘dm3’ assembly for ‘D.melanogaster’ genome and
‘RefSeq genes’ track for the group ‘Genes and Gene prediction tracks’. Choose
the output format as GTF for instance, and select to send the output to Galaxy.
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Displaying the genome annotation (3)
The dataset should be now visible in your current history. Visualize it in Trackster
by adding it to the RNA-Seq visualization.
You should now be seeing the four tracks, as shown in Step 6.
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Setting Tracks as overview
If you click on the third icon next to the dataset name, you can choose
to set that track as overview. Set the genome annotation as overview,
as seen below.
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VI. Exploring Trackster Visualization
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Re-ordering Tracks
It is easy to change the order of the tracks on Trackster. If you want to
place the third track (‘refgenedm3.gff3’) at the first position, click on the
beginning of the dataset name and drag it to the desired position. The
selected dataset will be indicated in blue.
Try dragging ‘mapped4.vcf’ to the third position.
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Changing Visualization modes:
Line Tracks
Hover around the dataset name and click on the first icon next to it to
change the display mode. Current display mode for BedGraph
(zoomed image): ‘Histogram’
Display mode changed to ‘Line’
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Changing Visualization modes:
Line Tracks(2)
Visualization mode changed to ‘Intensity’
Visualization mode changed to ‘Filled’
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Edit settings
Hover over the dataset name to see five different icons beside it.
Click on the fourth icon (gear shaped) to edit the settings of this
track.
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Edit Settings (2)
The name of the track, colours, Histogram min and max values can be easily
changed as per your choice.
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Changing visualization modes: Feature
tracks
Carry out the same process of changing the display mode for the
‘refgenedm3.gff3’ track.
Current display mode for gff3: ‘Coverage’ (Zoomed in image)
Display mode changed to : ‘Squish’- exons shown but not strand/labels
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Changing visualization modes:
Feature tracks(2)
Display mode changed to : ‘Dense’- everything stacked on top of
each other
Display mode changed to : ‘Pack’ – exons, strands, labels shown
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Hiding/Showing Tracks
By clicking on the second icon next to the dataset name, you can choose
to hide that particular dataset temporarily.
The screenshot here shows mapped4.vcf hidden at the moment. You
can choose to show the track again by clicking on the ‘+’ sign as the
second icon seen below.
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Visualizing datasets from Chr4: dm3
Add all the datasets from the RNA-Seq Library into Trackster and save. You
should be able to visualize the datasets this way:
gff3
vcf
BedGraph
Bed
Bam
wig
Bigwig
gtf
Overflow beyond listed maximum/minimum value indicated by red region
(Histogram) or red dot (Line)
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Visualizing Chip-Seq data
Switch to the History containing the ChIP-Seq data and visualize all the
datasets in Trackster.
BAM tracks are obtained from Chip-Seq data.
BED obtained as peaks from MACS on BAM files.
Wiggle files produced during MACS and 1 of them converted to BigWig.
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Dynamic Filters in Trackster
Filters are used to interactively show and hide data based on
feature attribute values—
-scores for genomic features,
-feature attribute values (in GFF/GTF datasets)
-mapping quality scores for mapped reads.
Click on the fifth icon next to the dataset ‘PolVsInput MACS
peaks’ to view the available filter for this dataset.
Slide over the Score filter to see similar results as shown below.
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Dynamic Filters in Trackster(2)
Filters for GTF datasets obtained from Cufflinks
Filter for a BAM dataset
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Zooming in and out on datasets
Zooming in and out on ‘PolII_chr4.bam’
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Viewing Saved Visualizations
To view the saved visualizations, click on the ‘Visualization’ Tab on the Galaxy
menu and select ‘Saved Visualizations’. A list of all saved visualizations will
then be visible.
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Managing Saved Visualizations
Right click on a visualization name to view different options. You can copy,
delete or share it easily. You can also rename it by selecting ‘Edit Attributes’.
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Visualizing in Circster
1.
2.
For your saved visualization, if you choose to (1) ‘Open in Circster’ or (2) click on the
circular ‘Circster’ icon in the upper right corner of the Trackster pane, you can visualize
your datasets in Circster.
Visualizing data in Circster
On choosing to visualize the Training RNA-Seq data in Circster, you should
be able to visualize something like this.
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What is Circster?
• Circos-style viewer for rendering genome-wide data on the Web.
• Circos - software package for visualizing data and information in a
circular layout.
Non-genomic data visualized by Circos
• Panning and zooming around the view automatically populates it
with more detailed data for visible regions.
How is data visualized with Circster?
• Position-based data (e.g., binding affinity, gene expression) laid out
in concentric circles representing chromosome position.
• Chromosome interaction data (e.g., three-dimensional interactions,
gene fusions) denoted as arcs on the inside of the position data.
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Datasets can be added easily.
Ideal for exploring relationships between objects or positions.
Better visualization for whole genome data.
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Visualizing in Circster
Click on ‘Add Tracks’ on the upper-right corner and import the five datasets
from the ‘Additional’ folder in the ‘Training’ data library. The expected
visualization is shown below. The interactions given by the ‘chrint’ format
make up the arcs in the centre.
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Caution with Circster
• Though Circster is a good way to visualize data in a circular format,
we have noted some difficulties while using it.
• Changing track settings, deleting tracks not possible directly in
Circster.
• Unexpected visualization glitches seen.
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THANKYOU!
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