Content from Introduction to genome browsers

Last updated on 2025-12-22 | Edit this page

Overview

Questions

  • What are genome browsers and why are they useful?
  • How was the human reference genome constructed?

Objectives

  • Understand the utility of genome browsers
  • Describe the human reference genome

Introduction to genome browsers

Genome browsers are invaluable for viewing and interpreting the many different types of data that can be anchored to genomic positions. These include variation, transcription, the many types regulatory data such as methylation and transcription factor binding, and disease associations. The larger genome browsers serve as data archives for valuable public datasets facilitating visualisation and analysis of different data types. It is also possible to load your own data into some of the public genome browsers.

By enabling viewing of one type of data in the context of another, the use of Genome browsers can reveal important information about gene regulation in both normal development and disease, and assist hypothesis development relating to genotype phenotype relationships.

Callout

COMMONLY USED GENOME BROWSERS

All researchers are encouraged to become familiar with the use of some of the main browsers, such as:

These browsers are designed for use by researchers without programming experience and the developers often provide extensive tutorials and cases studies demonstrating the many ways in which data can be loaded and interpreted to assist in develop and supporting your research hypothesis.

Many large genomic projects also incorporate genome browsers into their web portals to enable users to easily search and view the data. These include:

The human reference genome

Genome browsers rely on a common reference genome for each species in order to map data from different sources to the correct location. A consortium has agreed on a common numbering for each position on the genome for each species. However, this position will vary based on the version of the genome, as error correction and updates can change the numbering. Therefore it is very important to know which version of the genome your data of interest is aligned to.

The sequence for the human reference genome was accumulated up over many years from sequence data from many different sources and does not represent the sequence of one single person. Instead it is a composite of fragments of the genome from many different people. Also, unlike the human genome which is diploid, the human reference genome is haploid. This means there is only one copy of each chromosome. Therefore, the human reference genome does not reflect the variation of the population, or even the most common variants in the human genome. Exploring variation within human genome is very important and facilitated by genome browsers but not covered in this workshop.

If you would like to learn more about genome build version number and updates, please go to:


Key Points
  • Genome browsers allow us to interpret and view many different types of data associated with genomic positions. They are designed to be used by researchers without programming experience.
  • Genome browsers rely on a common reference genome for each species to map data to the correct location.

Content from Data used in this workshop

Last updated on 2025-12-22 | Edit this page

Overview

Questions

  • What data are we using in this workshop?

Objectives

  • Describe the biological pathway that will be referenced throughout this workshop

Introduction to the data used in this workshop

This tutorial uses a well known and important signalling pathway in the central nervous system (CNS) to illustrate some genome browser tools and utility.


BDNF and TrkB signalling

Brain Derived Neurotrophic factor (BDNF) protein is an important neurotrophin responsible for regulating many aspects of growth and development in different cells within the CNS. The NTRK2 gene encodes the TrkB protein, which is an important receptor that binds extracellular BDNF and propagates the intracellular signalling response via a tyrosine kinase.

The NTRK2 gene expresses a number of different transcript variants in different cell types. The most well studied of these is the full length TrkB receptor referred to as TrkB, which is mainly expressed in neuronal cell types. The other transcript variants all express the same exons encoding the extracellular domain of the receptor (shown in green in the figure below) but have truncated intracellular domains, which do not include the tyrosine kinase domain and thus activate different signalling pathways upon binding to BDNF. None of these truncated protein products have been well studied, but the most highly expressed receptor variant is known as TrkB-T1, and is known to be highly expressed in astrocytes.

Graphical representation of the BDNF and TrkB signalling pathway
Graphical representation of the BDNF and TrkB signalling pathway

Since the transcript variants are differently expressed in different cell types within the CNS, the NTRK2 gene is a very useful example for exploring cell type specific transcript expression in available public data.


Major CNS cell types

  • Neuron (yellow)
  • Astrocyte
  • Oligodendrocyte
  • Microglia
  • Ependymal



Key Points
  • The NTRK2 gene is a useful example for exploring cell-type specific transcript expression.

Content from UCSC Genome Browser: Setup

Last updated on 2025-12-22 | Edit this page

Overview

Questions

  • How do we navigate the web interface of the UCSC Genome Browser?

Objectives

  • Become familiar with basic navigation and configuration of the UCSC Genome Browser

Many of the tools that we will explore in this section can be selected via multiple different routes within the browser interface:

  • Many tools can be accessed via the top toolbar on a pull down list
  • Other tools can be accessed from within the browser window

In the following instructions, we will mostly navigate through successive lower levels from the pull-down menu from the top toolbar. Code-style text indicates buttons to click or options to select from a pull-down menu, with successive instructions indicated by > . For example: the below indicates that you should select Genome Browser from the top tool bar and then click on Reset all user settings.

Toolbar > Genome Browser > Reset all user settings

The UCSC Genome Browser is supported by a rich training resource which has new material added regularly to the YouTube channel.

To access training and develop your skills further, go to:

Toolbar > Help > Training


Caution

WARNING

Weekly maintenance of the UCSC browser occurs at 2-3pm Sundays Pacific time (7-8am Mondays AEST / 8-9am Mondays AEDT). During this time the browser may be down for a few minutes.

To ensure uninterrupted browser services for your research during UCSC server maintenance and power outages, bookmark one of the mirror sites that replicates the UCSC Genome Browser.

More information and contacts for the UCSC Genome Browser can be found here.

Setting up your browser

1. Open the Browser interface

Navigate to the UCSC Genome Browser and sign in if you have an account.


2. Reset the browser

To ensure we all see the same screen, select:

Toolbar > Genome Browser > Reset all user settings


3. Select and open the human Genome Hg38 at the default position


You should see a view of the browser similar to the image below, opening at a position on chromosome 7 of Human genome version GRCh38 showing the gene model for the SHH gene. Some of the default tracks may have been updated since this screenshot was made.


4. Familiarise yourself with the main areas of the interface

Checklist

Can you locate:

  • The main toolbar
  • Blue bar track collections (data of similar types are collected together under the same ‘Blue bar’ heading). Scroll down to see additional data collections and which ones are turned on as default.
  • Genome species and version number
  • Position box
  • Navigation tool buttons
  • Chromosome ideogram
  • Genome view window
  • Pre loaded tracks, track titles:
    • The grey bars on the left of the genome view can be used for selecting and configuring the tracks.
    • You can change the order of the tracks by dragging these grey bars up and down.
  • Turn tracks on and off:
    • You can hide tracks by right clicking on the grey bar or by turning them off in the Blue bar collections.
    • You have to click on a ‘refresh’ button for changes to be reflected in the genome view window.
  • View the configuration page for one of the tracks. The configuration page gives you a lot of information about the data track and its colouring. You can open the configuration page for a track by:
    • clicking on the grey bar for the track or,
    • clicking on the track title in the Blue bar collection. More information and options are usually available by selecting the configuration page for a track via the track title in the Blue Bar collections.
  • Select the resize button under the genome view to fit the genome view window to your screen


5. Customise your view

Use the ‘Configure’ tool to change the font size to 12


6. Practise navigating around the genome view

Checklist

Can you practise:

  • Moving left and right, using both the navigation buttons and your mouse
  • Zooming in and out, using navigation buttons
  • Zooming in to a region of interest, using drag-and-select:
    • Using your mouse, select a region of interest by clicking the ruler (position track) at the very top of the genome view window. Hold down the left click and drag to select a larger region.
    • This is also how to access the highlight tool which you will use in a later exercise to highlight a region of interest.
      • Click on the down arrow next to the highlight colour to select a different colour.
      • Don’t forget to click Add Highlight to save your highlighted region on the genome


Key Points
  • The UCSC genome browser can be easily configured according to your visual preferences and data needs

Content from UCSC Genome Browser: Understanding gene models

Last updated on 2025-12-22 | Edit this page

Overview

Questions

  • How do we interpret gene models represented in the UCSC genome browser?

Objectives

  • Understand how genomic data is represented in the UCSC genome browser
  • Identify differences between transcript variants


Now we will explore some tools and public datasets available in the UCSC genome browser.

Gene model representation

The typical structure of a gene as represented in the UCSC genome browser
The typical structure of a gene as represented in the UCSC genome browser

NTRK2

First we are going to familiarise ourselves with the gene model representation of the different transcripts of NTRK2.


1. Navigate to the NTRK2 gene position in GRCh38 and view the gene models.

You can navigate to a different region of the genome by typing in the position box.


2. Hide all tracks

Select the Hide all button below the genome view.


3. Turn on the Gencode track

From the blue bar group labelled Genes and Gene predictions, set GENCODE V48 to full and select Refresh to turn on the gene modelling track. Note: the version number 48 may have changed since this workshop was last updated.


4. Turn on the Conservation track

From the blue bar group labelled Comparative Genomics, set UCSC 100 Vertebrates to full and select Refresh to turn on the conservation track.


5. Zoom out and scroll across the whole gene

Zoom out until you can view all of the 5’ UTRs and 3’ UTRs for all transcript variants for this gene. Then drag the view left and right to centre the transcripts or use drag-and-select to Zoom In.

You should now see something like this
You should now see something like this
Challenge

CHALLENGE 1

Which strand is the gene encoded on / transcribed from? (+ or - strand)

The positive (+) strand. We know this because we can see the right-facing arrow ticks in the intronic regions of the gene model.

Challenge

CHALLENGE 2

Identify the exons, introns and UTRs.

Do regions of conservation only occur were there are coding regions?

Compare the NTRK2 transcript tracks to the gene model representation at the top of this lesson and discuss with your neighbour to identify exons, introns and UTRs.

Peaks in the conservation track indicate regions of high conservation across species. Use drag-and-select to zoom in to some non-exonic regions of high conservation (you may need to drag and select multiple times to zoom in far enough).

Challenge

CHALLENGE 3

How many different transcripts variants are there for this gene?

How do they differ?

Count the number of items in the GENCODE track. Hover over each transcript to view its ID. You may also wish to turn on the NCBI RefSeq track to view the NCBI RefSeq transcript IDs.

Notice how the number and position of exons, introns, and gene length differ between transcripts.

Challenge

CHALLENGE 4

Select a coding region/exon towards the 3’UTR of the gene. Zoom in to the region until you can see the letters of the amino acid sequence. Then do the same for a coding region/exon towards the end of the 5’UTR of the gene.

Why are some amino acid boxes red or green?

Green boxes indicate potential start codons, red boxes indicate translation termination codons.

Challenge

CHALLENGE 5

Zoom in further until you can see each amino acid number.

Why do different transcripts have different amino acid numbers?

Different transcripts may have different exons, or exons of different lengths, so as you move further towards the end of the gene, the amino acid numbers between different transcripts may no longer match.

Callout

NOTE

Note that one of the transcript names is highlighted in a different colour: this is the transcript you selected from the autocompleted list or the search results.


6. Change the view settings for the track

Right click on the track grey bar in the left of the genome window to access view settings.

Switch between dense , squish , pack , and full to see how it changes the representation of the models.


7. Reveal the Ensembl ID for each transcript

  1. Right click on the GENCODE track grey bar and select Configure GENCODE v48 to go to the configuration page.

  2. Check the box to also reveal the Ensembl ID in the label.

  3. Select Apply to save your changes.

The transcript names are now too long to fit on the screen. You can use the configuration page (like you did to change the font size earlier) to change the number of characters in the Label area width so that you can see the entire transcript label.


Check your understanding

Challenge

Question 1

Which transcript encodes the shortest amino acid sequence?

ENST00000352327.5

This transcript does not include one of the large coding regions and the coding region in the terminal exon is also slightly shorter.

Challenge

Question 2

Which transcript has the longest 3’UTR?

ENST00000396976.6

Although the last two exons of ENST00000396972.2 are UTR rather than coding sequence, it is still not as long as the 3’UTR of ENST00000396976.6

Challenge

Question 3

Which transcripts appear to encode the same protein product?

ENST00000420986.6, ENST00000280352.13 and ENST00000393047.8

Notice the height of the boxes for the first three exons: ENST00000532163.5 appears to encode a different CDS from the other transcripts.

Challenge

Question 4

Which transcript has the longest 5’UTR?

ENST00000532681.5

This transcript is encoded on the reverse strand.

Challenge

Question 5

Which transcript has the longest CDS?

ENST00000253801.7

This transcript is encoded on the forward strand.

Challenge

Question 6

Which transcript has the longest 5’UTR?

ENST00000589334.5

This transcript is encoded on the reverse strand.

BDNF

Now we will look at the gene model for BDNF in the same genome. There are some differences that enable us to demonstrate some more tools.


  • Note that there are blue transcript models encoded on the - strand and green BDNS-AS transcript models on the + strand. BDNF-AS is the antisense gene.
  • Colouring information is specific for each track and can be obtained from the configuration page. Below is the colouring legend for the GENCODE V36 track.


2. Flip the orientation of the gene

Since the convention is to display genes in the 5’ to 3’ orientation, it can be useful for our own interpretation, and also for presentation purposes, to flip the orientation of a gene when viewing it in a Genome Browser.

To flip the orientation of the gene, use the reverse button under the genome view window.


3. Apply the Multi-Region view from the main tool bar view options

When a gene has many large introns taking up a lot of white space in an image, it can be difficult to see if exons in different transcript models or other data tracks align. The Multi-Region view tool can be used to fold the intronic regions out of the view like a concertina. The Broswer selects which region to fold out based on the gene model track(s) that you have turned on at the time.

Toolbar > View > Multi-Region > Show exons using GENCODE V48 > Submit

It is now a lot easier to view a number of interesting features in the BDNF transcript models
It is now a lot easier to view a number of interesting features in the BDNF transcript models
Callout

Did you notice?

  • The transcript variants for the BDNF gene vary mostly in the genomic position of the 5’UTR.
  • The noncoding BDNF-AS gene transcript includes a region that would be antisense to the coding BDNF transcript.

You may find that using the multi-region tool facilitates visualisation and interpretation of gene expression data later in the workshop.


Key Points
  • The UCSC genome browser graphically represents key elements of gene transcripts, including exons, introns, and untranslated regions
  • Different settings and tools can be used to configure the browser to more easily investigate specific features of a gene.

Content from UCSC Genome Browser: BLAT tool

Last updated on 2025-12-22 | Edit this page

Overview

Questions

  • What is the BLAT tool and how do we use it?

Objectives

  • Use the BLAT tool to locate genomic regions with similarity to a sequence of interest

The BLAT tool

The BLAT tool is a sequence similarity tool similar to BLAST. It can quickly identify region(s) of homology between a genome and a sequence of interest. Due to the presence of orthologs and paralogs, a target sequence may have similarity to more than one region in the genome.

Exercise

In this exercise, we will employ the BLAT tool to map the sequences of two different expression probes to their target regions and determine which NTRK2 gene transcripts the probes are likely to detect.

Callout

A note on microarray expression data

Microarray expression data is not commonly used now, but some of the data generated from large, well-orchestrated studies still provide valuable information to researchers. Microarray probes, like in situ hybridisation probes, target a small region of the RNA and do not measure the whole RNA transcript. If you are measuring gene expression, it is important to know exactly which region of the gene you are detecting.

The study was the Human Brain Gene Expression Atlas generated by the Allen Institute. Below are sequences of two hybridisation probes that were use in a microarray used to detect expression of the NTRK2 gene. These two probes result in very different hybridisation and expression patterns across different regions of the brain. As we observed in the previous exercise, NTRK2 has a number of different transcript variants.


Our question: are these probes detecting different NTRK2 transcripts, and if yes, which ones?


The probes

The images below are of one of the six donors included in the atlas, and typical of the expression pattern for NTRK2. These images are taken from the NTRK2 gene page of Human Brain Atlas.

Most obvious in the images is the high level of expression signal using Probe A_23_P216779 and low level for A_24_P343559 in the corpus callosum (CC), which is a region of white matter in the brain with relatively few neurons and relatively high proportion of myelinating oligodendrocytes. This expression profile is reversed in the the cortical regions, eg. frontal lobe (FL) and parietal lobe (PL), which have a relatively high density of neuronal cells.

NTRK2 Probe A_23_P216779

Sequence: TTCTATACTCTAATCAGCACTGAATTCAGAGGGTTTGACTTTTTCATCTATAACACAGTG

Z score of expression level in Human brain (blue = low expression, red = high expression)
Z score of expression level in Human brain (blue = low expression, red = high expression)

NTRK2 Probe A_24_P343559

Sequence: AAGCTGCTCTCCTTCACTCTGACAGTATTAACATCAAAGACTCCGAGAAGCTCTCGAGGG

Z score of expression level in Human brain (blue = low expression, red = high expression)
Z score of expression level in Human brain (blue = low expression, red = high expression)


Your task

1. Use the BLAT tool to find region of homology

  1. Select Toolbar > tools > blat

  2. Copy the sequence of the first probe above and paste into the search box

  3. Under Assembly, select the human GRCh38 and click Submit

Probe A_23_P216779 returns 2 hits for different chromosomes. One of these has 100% homology over the whole 60 base sequence, the other has 87% homology over a 24 base region.
Probe A_23_P216779 returns 2 hits for different chromosomes. One of these has 100% homology over the whole 60 base sequence, the other has 87% homology over a 24 base region.

  1. Select Rename Custom Track. Copy and paste the probe name to use as the label for the Custom track name and Custom track description. Click Submit. It is not necessary to build a custom track, but this enables you to give your sequence a unique name which is important if you are doing multiple BLAT searches and want be able to tell which one is which.

  2. Select browser for the hit with the highest homology to view the result. Observe which region of the NTRK2 gene this probe will target. Zoom out for context.

  3. Note that a new blue bar heading has appeared for your custom tracks.

  4. Repeat for the other probe sequence.


2. Use the highlight tool to keep track of region of interest in the genome view.

It is easy to lose track of a region you are investigating when navigating around the genome in a browser. We are going to highlight each region of probe homology within the NTRK2 gene, using a different colour for each probe. Highlight is also useful if you have lots of different tracks loaded and you want to check that a feature on one track lines up with another.

  1. Use drag-and-select to select only the region of homology for each probe within the NTRK2 gene. Use a different highlight for each region.

  2. Zoom out to view the whole gene again.

Discussion

DISCUSS

Do the probes detect coding regions of the NTRK2 gene?

Discussion

Discuss

Are the probes likely to detect different transcripts?


3. Use ‘Multiregion view’ to make it easier to compare coding regions of different transcripts

  1. Toolbar > View > Multi-Region

  2. Select Show exons using GENCODE V48

This view makes it easier to check what region of each transcript is likely to be detected by the probes
This view makes it easier to check what region of each transcript is likely to be detected by the probes


Key Points
  • We can use the UCSC BLAT tool to identify region(s) of similarity between a sequence of interest and other parts of the genome.

Content from UCSC Genome Browser: Gene expression data

Last updated on 2025-12-22 | Edit this page

Overview

Questions

  • How can we explore gene expression data in the UCSC genome browser?

Objectives

Use the UCSC gene browser to:

  • Compare a region of one genome to genomes of other species
  • Determine the tissue/cell type expression profiles of a gene of interest in mouse and human expression data

Viewing GTEx data in the UCSC Genome Browser

Human tissue specific expression data from the GTEX project is available in the UCSC genome browser.

  1. Gene level expression data from GTEx Gene V8 donors can be turned on from the blue bar under Expression. These are displayed as coloured bar plots.

  2. Transcript level expression data is available by turning on GTEx Transcript under the same blue bar. This is also displayed as bar plots.

  3. Transcript level expression data for GTEx V6 is also available as coverage plots.

    1. Toolbar > My data > Track Hubs
    2. Scroll down to GTEx RNA-seq Signal Hub. Select hg38

Default settings

By default, all the available data from one individual only is loaded. Data from other subjects in the study can be loaded as desired. For example, you could load all available samples for one tissue region only.

The data is autoscale to data view with a track height or 12 pixels for each samples. You can change the height of the track or add a data transformation.

As usual, you can right click on the grey bar on the left hand side to configure the track set.


Your task

  1. Using the selection matrix for female donors aged 20-49 years, deselect the default samples and select only Brain cortex and Pancreas samples.

  2. Navigate to the location for the gene MYRF. You can increase the height of the datatrack to improve visualisation.

Discussion

CHALLENGE 1

Can you locate an exon in the MYRF gene that is present in transcripts expressed in the brain but not in the pancreas?

Discussion

CHALLENGE 2

Does this alternative splicing event result in a frame shift of the coding sequence?

Discussion

CHALLENGE 3

How many amino acids are there in the protein products for each MYRF transcript?

FACS data

The FACS derived data from the Tabula Muris cell type data can be visualised as a coverage plot.


Your task

  1. Navigate back to the NTRK2 gene in the human genome. Navigate to the Ntrk2 gene in the mouse genome via Toolbar > View > In Other Genomes (Convert)

  2. Under New Genome select Mouse, under New Assembly select GRC38/mm10, then click Submit

  3. Select the region with the greatest homology

  4. Click on the bar chart icon for the Tabula muris data in the default view to see a summary bar chart of the cell type data.

  1. To see a coverage plot of the expression data, we have to configure the Tabula Muris track.
    1. Right click on the grey bar to Configure Tabula Muris track set
    2. Hide Cell expression
    3. Select Genome coverage to full
    4. Select submit

This can look a bit overwhelming as there are many tracks and the default track height is set very high, but it is easy to simplify it by focusing on a few cell types of interest.

  1. Right click on the grey bar to configure Genome Coverage track set
    1. Change Track height to 30 pixels
    2. for Data view scaling select group auto-scale
    3. Hide All subtracks and then manually select only these few cell types of interest:
      • astrocyte Cv
      • Bergmann glial Cv
      • microglia Cv
      • neuron Cv
      • oligodendrocyte Cv
      • OPC Cv
    4. Submit
Discussion

CHALLENGE 4

Which cell type has the highest level expression of Ntrk2 in this dataset?

  1. Go back to the configuration page to change Data view scaling to auto-scale to data view and Submit

  2. Export a PDF image of the genome view: Toolbar > View > PDF select Download the current browser graphic in PDF

Discussion

CHALLENGE 5

Which cell type(s) express the long and short transcripts of Ntrk2?

Linnarsson lab data

Mouse CNS cell type expression data can also be validated using an independent single cell dataset of mouse cortex from the Linnarsson lab.

The data that is publicly available for viewing in the UCCS genome browser is not housed in the UCSC genome browser. You must first access it from the the Linnarsson lab data page.

This RNAseq data is stranded, meaning you can see if the transcript data is from the + or - strand.

Your task

  1. Click here for the Linnarsson lab public data page for this dataset where you can search for cell expression profiles for individual genes.

  2. Click on the Browse the genome blue text near the bottom of the page.

    This loads 18 different tracks, one for each cell type investigated. The default setting for expression range is quite high and most gene expression is not observed with these settings. Each track must be configured individually rather than as a group, which takes a lot of time.

    We previously created a version of this data where each track is autoscaled which can make it quicker to determine which expression range would be ideal for visualising the expression of an individual gene. The session is illustrated in the screen shot below. Note that the data is also viewed using ‘Multi-Region’ which hides the introns in the gene models.

Linnarsson lab mouse cortex single cell data as autoscaled datatracks
Linnarsson lab mouse cortex single cell data as autoscaled datatracks
Discussion

CHALLENGE 6

Select two or three cell types in your session and adjust the scale to best reflect differences in gene expression of Ntrk2 between these cells.

Save this session and share it.


Key Points
  • The UCSC genome browser allows you to compare gene expression data from multiple different sources and species

Content from IGV

Last updated on 2025-12-22 | Edit this page

Overview

Questions

  • How do you use IGV?

Objectives

  • Learn how to open basic file types and navigate IGV
  • Understand the type of data that can be visualised in IGV

IGV

In this section we will download a BAM file of gene expression data from SRA, and view it in the Integrated Genome Viewer (IGV). BAM files must first be sorted and indexed before they can be loaded into genome viewers. IGV has tools to do this without having to use the command line.

Exercise

The data

The expression data we are using for this exercise is from the mouse Celltax single cell expression atlas published by the Allen Brain Institute. The cell tax vignette has an expression browser that displays gene level expression as a heat map for any gene of interest. The readsets (fastq files) and aligned data (BAM files) for 1809 runs on single cells are also available for download from SRA.

The SRA study ID for this study is SRP061902 and individual runs from this study are easily selected by viewing the samples in the ‘RunSelector’.

For this exercise, we will download a few samples in order to illustrate navigating in IGV by looking at the expression of NTRK2 in the same cell types we have discussed in earlier exercises. For each cell type, we will down load a .BAM file containing only the reads from the chromosome of interest.


Your task

1. Download BAM files from SRA

For each SRA run in the table below:

  1. Click on the SRA run to open the link.

  2. Click on the ‘Alignment’ tab. Note that the data is aligned to the mouse GRCm38 genome (mm10).

  3. Select the chromosome of interest. For Ntrk2 in mouse it is chr13

  4. For Output this run in: select BAM and click on format to: File

  5. Rename the downloaded file to include the cell type, to avoid confusion, e.g. SRR2138661_astrocyte_chr13.bam

Cell type SRA run Vignette Cell ID
astrocyte SRR2138962 D1319_V
astrocyte SRR2139935 A1643_VL
neuron SRR2139989 S467_V4
neuron SRR2140047 S1282_V


2. Use IGV tools to SORT and INDEX the BAM files

  1. Open IGV and select Tools > Run igvtools from the pull down menus

  2. Select Sort from the Command options and use the browse options to select the BAM file you just downloaded. Click Run

  3. Without closing the igvtools window, now select the command Index and browse to find the BAM file you just sorted. It will have the same file name with ‘sorted’ added to the end, e.g. SRR2138661_astrocyte_chr13.sorted.bam

The resulting index file will have the file name SRR2138661_astrocyte_chr13.sorted.bam.bai

Callout

IMPORTANT

It is essential that the index file for a BAM file has the same name and is located in the same folder as its BAM file. If not, the IGV software will not be able to open the BAM file.


3. View the BAM files in IGV

  1. Select the Mouse (mm10) genome from the genome box in the top right hand corner.

  2. Select File > Load from File and select all four _chr13.sorted.bam files only.

  3. Select open - but don’t expect to see any data yet. The genome view window opens on a whole chromosome view as default but it wont show any data until the view region is small enough to show all data in the current view.

  4. Type the gene name NTRK2 into the search window.

  5. Expand the Refseq gene model track by right clicking it to see all the splice variants.

  6. The gene and thus the genome view is 328kb and the default setting for viewing data is only 100kb. Unless you have already changed your settings, alignment data will not get be showing. Zoom into the region of a coding exon by selecting in the numbered location track at the top of the genome view.

    To see the whole gene in the genome window at the same time you may need to change the preferences.

  7. Go to View > Preferences and select the Alignments tab. Change the visibility range threshold to 400kb.

You may need to change this back to a smaller range in the future if you are working with large datasets and/or small amounts of memory on your computer
You may need to change this back to a smaller range in the future if you are working with large datasets and/or small amounts of memory on your computer


4. Export images

  1. The Genome view above can be exported by selecting File > Save image from the tool bar

  2. To export the Sashimi plot below:

    1. Right click on one of the junction tracks and select Sashimi Plot from the pull down menu.
    2. Select the tracks you want in your final image.
    3. There are some data filtering and style adjustments you can make to the Sashimi plot. Right click on each track to access the menu options. Some changes apply to each track individually and some to all tracks.


5. Download and install the Gencode gene model annotation track

The refseq gene model track is not as comprehensive as GenCode gene models. For both Human and Mouse the Gencode gene model gtf annotation files can be downloaded from Gencode. If you wish to do this be aware that it takes a little time and is not done as part of a workshop.

  1. Create a folder called ‘annotations/Mouse’ in the main ‘igv’ folder that was installed on your computer when you downloaded IGV.

  2. Download the GTF file from the link above and save it in this folder.

  3. Unpack and then sort and index the .gtf file using igvtools.

  4. In IGV, before you load you data files, load this annotation file and it will replace the refseq one.

If you would like to learn more about how to use IGV, please go to:

https://rockefelleruniversity.github.io/IGV_course/presentations/singlepage/IGV.html


Key Points
  • IGV can sort and index BAM files without use of the command line
  • Sorted and indexed BAM files can then be opened to view genomic sequencing and gene expression data in IGV