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Alignment Visualization | Griffith Lab

## RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

# Alignment Visualization

Before we can view our alignments in the IGV browser we need to index our BAM files. We will use samtools index for this purpose. For convenience later, index all bam files.

echo $RNA_ALIGN_DIR cd$RNA_ALIGN_DIR
find *.bam -exec echo samtools index {} \; | sh


### Visualize alignments

Start IGV on your laptop. Load the UHR.bam & HBR.bam files in IGV. You can load the necessary files in IGV directly from your web accessible amazon workspace (see below) using ‘File’ -> ‘Load from URL’. You may wish to customize the track names as you load them in to keep them straight. Do this by right-clicking on the alignment track and choosing ‘Rename Track’.

UHR hisat2 alignment:

http://YOUR_DNS_NAME/workspace/rnaseq/alignments/hisat2/UHR.bam

HBR hisat2 alignment:

http://YOUR_DNS_NAME/workspace/rnaseq/alignments/hisat2/HBR.bam

Go to an example gene locus on chr22:

• e.g. EIF3L, NDUFA6, and RBX1 have nice coverage
• e.g. SULT4A1 and GTSE1 are differentially expressed. Are they up-regulated or down-regulated in the brain (HBR) compared to cancer cell lines (UHR)?
• Mouse over some reads and use the read group (RG) flag to determine which replicate the reads come from. What other details can you learn about each read and its alignment to the reference genome.

#### Exercise

Try to find a variant position in the RNAseq data:

• HINT: DDX17 is a highly expressed gene with several variants in its 3 prime UTR.
• Other highly expressed genes you might explore are: NUP50, CYB5R3, and EIF3L (all have at least one transcribed variant).
• Are these variants previously known (e.g., present in dbSNP)?
• How should we interpret the allele frequency of each variant? Remember that we have rather unusual samples here in that they are actually pooled RNAs corresponding to multiple individuals (genotypes).
• Take note of the genomic position of your variant. We will need this later.

Using one of the variant positions identified above, count the number of supporting reference and variant reads. First, use samtools mpileup to visualize a region of alignment with a variant.

cd $RNA_HOME mkdir bam_readcount cd bam_readcount  Create faidx indexed reference sequence file for use with mpileup echo$RNA_REF_FASTA
samtools faidx $RNA_REF_FASTA  Run samtools mpileup on a region of interest samtools mpileup -f$RNA_REF_FASTA -r 22:18918457-18918467 $RNA_ALIGN_DIR/UHR.bam$RNA_ALIGN_DIR/HBR.bam


Each line consists of chromosome, 1-based coordinate, reference base, the number of reads covering the site, read bases and base qualities. At the read base column, a dot stands for a match to the reference base on the forward strand, a comma for a match on the reverse strand, ACGTN for a mismatch on the forward strand and acgtn for a mismatch on the reverse strand. A pattern \+[0-9]+[ACGTNacgtn]+ indicates there is an insertion between this reference position and the next reference position. The length of the insertion is given by the integer in the pattern, followed by the inserted sequence. See samtools pileup/mpileup documentation for more explanation of the output:

Now, use bam-readcount to count reference and variant bases at a specific position. First, create a bed file with some positions of interest (we will create a file called snvs.bed using the echo command).

It will contain a single line specifying a variant position on chr22 e.g.:

22 38483683 38483683

Create the bed file

echo "22 38483683 38483683"
echo "22 38483683 38483683" > snvs.bed


Run bam-readcount on this list for the tumor and normal merged bam files

bam-readcount -l snvs.bed -f $RNA_REF_FASTA$RNA_ALIGN_DIR/UHR.bam 2>/dev/null
bam-readcount -l snvs.bed -f $RNA_REF_FASTA$RNA_ALIGN_DIR/HBR.bam 2>/dev/null


Now, run it again, but ignore stderr and redirect stdout to a file:

    bam-readcount -l snvs.bed -f $RNA_REF_FASTA$RNA_ALIGN_DIR/UHR.bam 2>/dev/null 1>UHR_bam-readcounts.txt
bam-readcount -l snvs.bed -f $RNA_REF_FASTA$RNA_ALIGN_DIR/HBR.bam 2>/dev/null 1>HBR_bam-readcounts.txt


From this output you could parse the read counts for each base

cat UHR_bam-readcounts.txt | perl -ne '@data=split("\t", $_); @Adata=split(":",$data[5]); @Cdata=split(":", $data[6]); @Gdata=split(":",$data[7]); @Tdata=split(":", $data[8]); print "UHR Counts\t$data[0]\t$data[1]\tA:$Adata[1]\tC: $Cdata[1]\tT:$Tdata[1]\tG: $Gdata[1]\n";' cat HBR_bam-readcounts.txt | perl -ne '@data=split("\t",$_); @Adata=split(":", $data[5]); @Cdata=split(":",$data[6]); @Gdata=split(":", $data[7]); @Tdata=split(":",$data[8]); print "HBR Counts\t$data[0]\t$data[1]\tA: $Adata[1]\tC:$Cdata[1]\tT: $Tdata[1]\tG:$Gdata[1]\n";'


### PRACTICAL EXERCISE 7

Assignment: Index your bam files from Practical Exercise 6 and visualize in IGV.

• Hint: As before, it may be simplest to just index and visualize the combined/merged bam files HCC1395_normal.bam and HCC1395_tumor.bam.
• If this works, you should have two BAM files that can be loaded into IGV from the following location on your cloud instance:
• http://YOUR_DNS_NAME/workspace/rnaseq/practice/alignments/hisat2/

Questions

• Load your merged normal and tumor BAM files into IGV. Navigate to this location on chromosome 22: ‘chr22:38,466,394-38,508,115’. What do you see here? How would you describe the direction of transcription for the two genes? Does the reported strand for the reads aligned to each of these genes appear to make sense? How do you modify IGV settings to see the strand clearly?
• How can we modify IGV to color reads by Read Group? How many read groups are there for each sample (tumor & normal)? What are your read group names for the tumor sample?
• What are the options for visualizing splicing or alternative splicing patterns in IGV? Navigate to this location on chromosome 22: ‘chr22:40,363,200-40,367,500’. What splicing event do you see?

Solution: When you are ready you can check your approach against the Solutions.

IGV | Griffith Lab

## RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

# IGV

### Introduction

Description of the lab Welcome to the lab for Genome Visualization! This lab will introduce you to the Integrative Genomics Viewer, one of the most popular visualization tools for High Throughput Sequencing (HTS) data.

Lecture files that accompany this tutorial:

After this lab, you will be able to:

• Visualize a variety of genomic data
• Quickly navigate around the genome
• Validate SNP/SNV calls and structural re-arrangements by eye

Things to know before you start:

• The lab may take between 1-2 hours, depending on your familiarity with genome browsing. Do not worry if you do not complete the lab. It will remain available to review later.

• There are a few thought-provoking Questions or Notes pertaining to sections of the lab. These are optional, and may take more time, but are meant to help you better understand the visualizations you are seeing. These questions will be denoted by boxes, as follows: Question(s):

Thought-provoking question goes here.

#### Requirements

• Integrative Genomics Viewer

• Ability to run Java

• Note that while most tutorials in this course are performed on the cloud, IGV will always be run on your local machine

#### Compatibility

This tutorial was intended for IGV v2.3, which is available on the IGV Download page. It is strongly recommended that you use this version.

#### Data Set for IGV

We will be using publicly available Illumina sequence data from the HCC1143 cell line. The HCC1143 cell line was generated from a 52 year old caucasian woman with breast cancer. Additional information on this cell line can be found here: HCC1143 (tumor, TNM stage IIA, grade 3, primary ductal carcinoma) and HCC1143/BL (matched normal EBV transformed lymphoblast cell line).

### Visualization Part 1: Getting familiar with IGV

We will be visualizing read alignments using IGV, a popular visualization tool for HTS data.

First, lets familiarize ourselves with it.

#### Get familiar with the interface

##### Load a Genome and some Data Tracks

By default, IGV loads Human hg19. If you work with another version of the human genome, or another organism altogether, you can change the genome by clicking the drop down menu in the upper-left. For this lab, we will be using Human hg19.

We will also load additional tracks from Server using (File -> Load from Server...):

• Ensembl genes (or your favourite source of gene annotations)
• GC Percentage
• dbSNP 1.3.1 or 1.3.7

You should see listing of chromosomes in this reference genome. Choose 1, for chromosome 1.

Chromosome chooser

Navigate to chr1:10,000-11,000 by entering this into the location field (in the top-left corner of the interface) and clicking Go. This shows a window of chromosome 1 that is 1,000 base pairs wide and beginning at position 10,000.

Navigition using Location text field. Sequence displayed as thin coloured rectangles.

IGV displays the sequence of letters in a genome as a sequence of colours (e.g. A = green, C = blue, etc.). This makes repetitive sequences, like the ones found at the start of this region, easy to identify. Zoom in a bit more using the + button to see the individual bases of the reference genome sequence.

You can navigate to a gene of interest by typing it in the same box the genomic coordinates are in and pressing Enter/Return. Try it for your favourite gene, or BRCA1 if you can not decide.

Gene model

Genes are represented as lines and boxes. Lines represent intronic regions, and boxes represent exonic regions. The arrows indicate the direction/strand of transcription for the gene. When an exon box become narrower in height, this indicates a UTR.

When loaded, tracks are stacked on top of each other. You can identify which track is which by consulting the label to the left of each track.

#### Region Lists

Sometimes, it is really useful to save where you are, or to load regions of interest. For this purpose, there is a Region Navigator in IGV. To access it, click Regions > Region Navigator. While you browse around the genome, you can save some bookmarks by pressing the Add button at any time.

Bookmarks in IGV

We will be using the breast cancer cell line HCC1143 to visualize alignments. For speed, only a small portion of chr21 will be loaded (19M:20M).

HCC1143 Alignments to hg19:

Copy the files to your local drive, and in IGV choose File > Load from File..., select the bam file, and click OK. Note that the bam and index files must be in the same directory for IGV to load these properly.

Navigate to a narrow window on chromosome 21: chr21:19,480,041-19,480,386.

To start our exploration, right click on the track-name, and select the following options:

• Sort alignments by start location
• Group alignments by pair orientation

Experiment with the various settings by right clicking the read alignment track and toggling the options. Think about which would be best for specific tasks (e.g. quality control, SNP calling, CNV finding).

Changing how read alignments are sorted, grouped, and colored

You will see reads represented by grey or white bars stacked on top of each other, where they were aligned to the reference genome. The reads are pointed to indicate their orientation (i.e. the strand on which they are mapped). Mouse over any read and notice that a lot of information is available. To toggle read display from hover to click, select the yellow box and change the setting.

Changing how read information is shown (i.e. on hover, click, never)

Once you select a read, you will learn what many of these metrics mean, and how to use them to assess the quality of your datasets. At each base that the read sequence mismatches the reference, the colour of the base represents the letter that exists in the read (using the same colour legend used for displaying the reference).

### Visualization Part 2: Inspecting SNPs, SNVs, and SVs

In this section we will be looking in detail at 8 positions in the genome, and determining whether they represent real events or artifacts.

#### Two neighbouring SNPs

• Navigate to region chr21:19,479,237-19,479,814

• Note two heterozygous variants, one corresponds to a known dbSNP (G/T on the right) the other does not (C/T on the left)

• Zoom in and center on the C/T SNV on the left, sort by base (window chr21:19,479,321 is the SNV position)

• Sort alignments by base

• Color alignments by read strand

Example1. Good quality SNVs/SNPs

Notes:

• High base qualities in all reads except one (where the alt allele is the last base of the read)
• Good mapping quality of reads, no strand bias, allele frequency consistent with heterozygous mutation

Question(s):

* What does *Shade base by quality* do? How might this be helpful?
* How does Color by *read strand* help?


#### Homopolymer region with indel

Navigate to position chr21:19,518,412-19,518,497

Example 2a

• Group alignments by read strand
• Center on the A within the homopolymer run (chr21:19,518,470), and Sort alignments by -> base

Example 2b

• Center on the one base deletion (chr21:19,518,452), and Sort alignments by -> base

Notes:

• The alt allele is either a deletion or insertion of one or two Ts
• The remaining bases are mismatched, because the alignment is now out of sync
• The dpSNP entry at this location (rs74604068) is an A->T, and in all likelihood an artifact
• i.e. the common variants from dbSNP include some cases that are actually common misalignments caused by repeats

#### Coverage by GC

Navigate to position chr21:19,611,925-19,631,555. Note that the range contains areas where coverage drops to zero in a few places.

Example 3

• Use Collapsed view
• Use Color alignments by -> insert size and pair orientation
• See concordance of coverage with GC content

Question:

* Why are there blue and red reads throughout the alignments?


#### Heterozygous SNPs on different alleles

Navigate to region chr21:19,666,833-19,667,007

Example 4

• Sort by base (at position chr21:19,666,901)

Note:

• There is no linkage between alleles for these two SNPs because reads covering both only contain one or the other

#### Low mapping quality

Navigate to region chr21:19,800,320-19,818,162

• Load repeat track (File -> Load from server...)

Example 5

Notes:

• Mapping quality plunges in all reads (white instead of grey). Once we load repeat elements, we see that there are two LINE elements that cause this.

#### Homozygous deletion

Navigate to region chr21:19,324,469-19,331,468

Example 6

• Turn on View as Pairs and Expanded view
• Use Color alignments by -> insert size and pair orientation
• Sort reads by insert size
• Click on a red read pair to pull up information on alignments

Notes:

• Typical insert size of read pair in the vicinity: 350bp
• Insert size of red read pairs: 2,875bp
• This corresponds to a homozygous deletion of 2.5kb

#### Mis-alignment

Navigate to region chr21:19,102,154-19,103,108

Example 7

Notes:

• This is a position where AluY element causes mis-alignment.
• Misaligned reads have mismatches to the reference and well-aligned reads have partners on other chromosomes where additional ALuY elements are encoded.
• Zoom out until you can clearly see the contrast between the difficult alignment region (corresponding to an AluY) and regions with clean alignments on either side

#### Translocation

Navigate to region chr21:19,089,694-19,095,362

Example 8

• Expanded view
• Group alignments by -> pair orientation
• Color alignments by -> pair orientation

Notes:

• Many reads with mismatches to reference
• Region is flanked by reads with poor mapping quality (white instead of grey)
• Presence of reads with pairs on other chromosomes (coloured reads at the bottom when scrolling down)

### Visualization Part 3: Automating Tasks in IGV

We can use the Tools menu to invoke running a batch script. Batch scripts are described on the IGV website:

Now run the file from the Tools` menu:

Automation

Notes:

• This script will navigate automatically to each location in the lab
• A screenshot will be taken and saved to the screenshots directory specified

#### Contributors/acknowledgements

Malachi Griffith, Sorana Morrissy, Jim Robinson, Ben Ainscough, Jason Walker, Obi Griffith

• ## Alignment Visualization

Before we can view our alignments in the IGV browser we need...

• ## IGV

### Introduction

Description of the lab Welcome to the lab for Genome Visualization! This lab will introduce you...