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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
  • Visualize read alignments
  • 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

Load hg19 genome and additional data tracks

Load hg19 genome and additional data tracks

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

Chromosome chooser

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.

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

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

Bookmarks in IGV

Loading Read Alignments

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.

Load BAM track from File

Load BAM track from File

Visualizing read alignments

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

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)

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).

Viewing read information for a single aligned read

Viewing read information for a single aligned read


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

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 2a

Example 2b

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

Example 2b

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
  • Load GC track
  • See concordance of coverage with GC content

Example 3

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)

Example 4

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...)

Load repeats

Load repeats

Example 5

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

Example 6

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

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

Example 8

Notes:

  • Many reads with mismatches to reference
  • Read pairs in RL pattern (instead of LR pattern)
  • 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:

Download the batch script and the attribute file for our dataset:

Now run the file from the Tools menu:

Automation

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 QC | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Alignment QC


RNA-seq_Flowchart3


Use samtools and FastQC to evaluate the alignments

Use samtools view to see the format of a SAM/BAM alignment file

cd $RNA_ALIGN_DIR
samtools view -H UHR.bam
samtools view UHR.bam | head

Try filtering the BAM file to require or exclude certain flags. This can be done with samtools view -f -F options

-f INT required flag -F INT filtering flag

“Samtools flags explained”

Try requiring that alignments are ‘paired’ and ‘mapped in a proper pair’ (=3). Also filter out alignments that are ‘unmapped’, the ‘mate is unmapped’, and ‘not primary alignment’ (=268)

samtools view -f 3 -F 268 UHR.bam | head

Now require that the alignments be only for ‘PCR or optical duplicate’. How many reads meet this criteria? Why?

samtools view -f 1024 UHR.bam | head

Use samtools flagstat to get a basic summary of an alignment. What percent of reads are mapped? Is this realistic? Why?

cd $RNA_ALIGN_DIR
mkdir flagstat

find *Rep*.bam -exec echo samtools flagstat {} \> flagstat/{}.flagstat \; | sh

# View an example
cat flagstat/UHR_Rep1.bam.flagstat 

Details of the SAM/BAM format can be found here: http://samtools.sourceforge.net/SAM1.pdf

Using FastQC

You can use FastQC to perform basic QC of your BAM file (See Pre-alignment QC). This will give you output very similar to when you ran FastQC on your fastq files.

cd $RNA_ALIGN_DIR
fastqc UHR_Rep1.bam UHR_Rep2.bam UHR_Rep3.bam HBR_Rep1.bam HBR_Rep2.bam HBR_Rep3.bam
mkdir fastqc
mv *fastqc.html fastqc/
mv *fastqc.zip fastqc/

Using Picard

You can use Picard to generate RNA-seq specific quality metrics and figures


# Generating the necessary input files for picard CollectRnaSeqMetrics
cd $RNA_HOME/refs

# Create a .dict file for our reference
java -jar $RNA_HOME/tools/picard.jar CreateSequenceDictionary R=chr22_with_ERCC92.fa O=chr22_with_ERCC92.dict

# Create a bed file of the location of ribosomal sequences in our reference (first extract from the gtf then convert to bed)
grep --color=none -i rrna chr22_with_ERCC92.gtf > ref_ribosome.gtf
gff2bed < ref_ribosome.gtf > ref_ribosome.bed

# Create interval list file for the location of ribosomal sequences in our reference
java -jar $RNA_HOME/tools/picard.jar BedToIntervalList I=ref_ribosome.bed O=ref_ribosome.interval_list SD=chr22_with_ERCC92.dict

# Create a genePred file for our reference transcriptome
gtfToGenePred -genePredExt chr22_with_ERCC92.gtf chr22_with_ERCC92.ref_flat.txt

# reformat this genePred file
cat chr22_with_ERCC92.ref_flat.txt | awk '{print $12"\t"$0}' | cut -d$'\t' -f1-11 > tmp.txt
mv tmp.txt chr22_with_ERCC92.ref_flat.txt

cd $RNA_HOME/alignments/hisat2/
mkdir picard
find *Rep*.bam -exec echo java -jar $RNA_HOME/tools/picard.jar CollectRnaSeqMetrics I={} O=picard/{}.RNA_Metrics REF_FLAT=$RNA_HOME/refs/chr22_with_ERCC92.ref_flat.txt STRAND=SECOND_READ_TRANSCRIPTION_STRAND RIBOSOMAL_INTERVALS=$RNA_HOME/refs/ref_ribosome.interval_list \; | sh

RSeQC [optional]

Background: RSeQC is a tool that can be used to generate QC reports for RNA-seq. For more information, please check: RSeQC Tool Homepage

Files needed:

cd $RNA_HOME/refs/

# Convert Gtf to genePred
gtfToGenePred chr22_with_ERCC92.gtf chr22_with_ERCC92.genePred

# Convert genPred to bed12
genePredToBed chr22_with_ERCC92.genePred chr22_with_ERCC92.bed12

cd $RNA_ALIGN_DIR
mkdir rseqc
geneBody_coverage.py -i UHR_Rep1.bam,UHR_Rep2.bam,UHR_Rep3.bam -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 -o rseqc/UHR
geneBody_coverage.py -i HBR_Rep1.bam,HBR_Rep2.bam,HBR_Rep3.bam -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 -o rseqc/HBR

find *Rep*.bam -exec echo inner_distance.py -i {} -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 -o rseqc/{} \; | sh

find *Rep*.bam -exec echo junction_annotation.py -i {} -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 -o rseqc/{} \; | sh

find *Rep*.bam -exec echo junction_saturation.py -i {} -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 -o rseqc/{} \; | sh

find *Rep*.bam -exec echo read_distribution.py  -i {} -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 \> rseqc/{}.read_dist.txt \; | sh

find *Rep*.bam -exec echo RNA_fragment_size.py -i {} -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 \> rseqc/{}.frag_size.txt \; | sh

find *Rep*.bam -exec echo bam_stat.py -i {} \> {}.bam_stat.txt \; | sh

rm -f log.txt

MultiQC

We will now use multiQC to compile a QC report from all the QC tools above

cd $RNA_ALIGN_DIR
multiqc ./