1 Just to remind

Course etiquette
Please read the course etiquette, if you haven’t read that yet.

Shared document
We are using shared GoogleDocs documents for each of the main topics covered during the summer school. The document for this section can be found here.

Prerequisites
If you want to follow this tutorial using your own machine, you need to install the following command line tools:

You can install the tools one by one, but a very convenient way to manage installed tools/packages and their dependencies is Conda. If you are new to Conda, please follow this tutorial.

Sample dataset
A dataset for this tutorial is avaliable here. Please keep in mind that, the directories and folder structure in your machine may differ from the one we used during the course.

2 Introduction

After successful quality check raw seqencing reads are ready to be aligned against the reference genome. The choice of aligner is usually a personal preference that might be directed by avaliability of computational resources and running time. Therefore, we will not align any raw .fastq files today. Instead we will carefully go through a standard alignment workflow together. For a practical expercise, we will focus on already aligned sequences.

3 Preparing a Reference Genome

Reference genomes can be downloaded from UCSC, Ensembl or NCBI. We downloaded the most recent one version of the human genome (hg38 a.k.a GRCh38) togther with matching GTF file with annotations from Gencode.

3.1 Reference genome index

Mapping of millions of short reads to a very large reference sequence is a challenging task. In order to accelerate short reads mapping, most of the modern alignment tools use a strategy of ‘indexing’ (think about it as indexing of a book). Indexing is specific to an aligner and reference sequence / annotations used. All the detailed informations about required genome indexes can be found in a software documentation. For this tutorial we’ll use STAR a splice-aware aligner that is very commonly used to map RNA-Seq reads.

Let’s explore the most imporant parameters of STAR command to generate indexed reference genome. Full docummentation can be accessed here.

--runThreadN : number of threads (cores)
--runMode genomeGenerate : will generate indexed reference genome
--genomeDir : specify the output directory
--genomeFastaFiles : path to the reference genome FASTA file
--sjdbGTFfile: path to the GTF file with annotations
--sjdbOverhang: max(readlength) - 1

So a basic command for GRCh38 genome with gencode.v29 annotations for a sequencing experiment with reads 100 bp long can be:

** DO NOT RUN **
STAR --runThreadN 6 \
--runMode genomeGenerate \
--genomeDir STAR_GRCh38_gencode29 \
--genomeFastaFiles GRCh38.primary_assembly.genome.fa \
--sjdbGTFfile gencode.v29.annotation.gtf \
--sjdbOverhang 99

3.2 Alignment to the reference genome

Once we generated a reference genome we can move to the alignment stage.

** DO NOT RUN **
STAR --runThreadN 6 \
--genomeDir STAR_GRCh38_gencode29 \
--readFilesIn data/tp53_rnaseq_rep1_trimmed.fastq.gz \
--outFileNamePrefix aligned/tp53_rnaseq_rep1_trimmed \
--outSAMtype BAM SortedByCoordinate \
--outSAMunmapped Within \
--outSAMattributes Standard

4 Quality check of aligned reads

Option 1: run multiqc on aligner log files

cd /home/ubuntu/Course_Materials/Introduction/Preprocessing
multiqc -o data/multiqc data/sample1_STAR

Option 2: Use Samstat

cd /home/ubuntu/Course_Materials/Introduction/Preprocessing
samstat data/sample1_STAR/Aligned.sortedByCoord.out.bam

5 Manipulating aligned sequences

Samtools is an open source toolkit for next generation sequence data manipulation. It is patriculairly useful to modify and reformat sequence alignment files (SAM/BAM) for downstream processing. We’ll demonstrate only a few examples of samtools utilities, full documentation can be accessed here.

5.1 BAM to SAM

A BAM file (.bam) is the binary version of a SAM file. BAM occupies less disk space than SAM and is a default input required by some bioinformatic tools.

# Go to the directory with STAR output
cd data/sample1_STAR

samtools view Aligned.sortedByCoord.out.bam > Aligned.sortedByCoord.out.sam

# SAM file is just a text file, so in order to view first few reads we can do:
head -n 10 Aligned.sortedByCoord.out.sam  

# BAM file is a binary file
samtools view Aligned.sortedByCoord.out.bam | head -n 10

5.2 Sorting a BAM file

Many tools require sorted and indexed BAM/SAM files. In order to sort a BAM file we will use samtools sort command:

samtools sort Aligned.sortedByCoord.out.bam -o Aligned.sortedByCoord.out.sorted.bam

For indexing samtools index command:

samtools index Aligned.sortedByCoord.out.sorted.bam

5.3 Filter a BAM file to contain only uniquely mapped reads

samtools view -bh -q 255 Aligned.sortedByCoord.out.sorted.bam > Aligned.sortedByCoord.out.sorted.unique.bam

5.4 Filter a BAM files to contain reads mapping to a specific region

Let’s extract reads mapping to PIK3CA, a gene that is essential for B-cell development and contributes to lymphomagenesis. We used Ensembl resources to know genomic coordinates of PIK3CA.

samtools view -bh Aligned.sortedByCoord.out.sorted.unique.bam "chr3:179148114-179240093" > PIK3CA.bam

5.5 Exercise 1

  1. Go to data/sample2_STAR/ directory.
  2. Convert Aligned.sortedByCoord.out.bam to Aligned.sortedByCoord.out.sam
  3. Compare the size of BAM and SAM file.
  4. How many reads Aligned.sortedByCoord.out.bam out of first 10 reads was mapped uniquely? Hint: mapping quality = 255 for uniquely mapped reads.
  5. Sort Aligned.sortedByCoord.out.bam using samtools sort command, save the output as Aligned.sortedByCoord.out.sorted.bam
  6. Index Aligned.sortedByCoord.out.sorted.bam using samtools index command
  7. Extract only uniquely mapped reads from Aligned.sortedByCoord.out.sorted.bam and save them as Aligned.sortedByCoord.out.sorted.unique.bam
  8. [ADVANCED] How many reads were mapped uniquely?
  9. [ADVANCED] How many reads mapped uniquely to PIK3CA?

7 Suplemmentary Materials: Alignment of ChIP-Seq readswith BWA

We’ll use BWA to align a fastq ChIP-seq sample to the GRCh38 reference genome. First, we need to create a BWA hg38chr3 index. You can access BWA docummentation here or just simply type in the terminal window:

bwa index

This command will display all the parameters that you can use when creating the reference genome (in today’s example - chromosome) index. Now we will run:

bwa index -p hg38_chr3_BWA_idx -a bwtsw reference/hg38_chr3.fa

The command should take couple of minutes to finish, so let’s explore parameters used:

-p: gives the name of a directory where indexed files will be placed (you can call this whatever you want, we named it hg38_chr3_BWA_idx) -a: chooses one of the indexing algorithm within bwa

Normally you would use a complete genome build fasta (eg. hg38.fa) file to build a bwa index. In this case we’re using only chromosome 3: hg38_chr3.fa.

Once we created the reference index we can run main mapping command:

bwa mem -M -t 8 hg38_chr3_BWA_idx data/tp53_r2.fastq_trimmed.fastq.gz > BWA/tp53_r2.sam

Parameters we used:

-M: leaves the best (longest) alignment for a read as primary alignment and additional alignments for the read as secondary
-t: indicates number of processor cores used to do the alignment

8 Acknowledgements

Dora Bihary
VIB Center for Cancer Biology, University of Leuven, BE
MRC Cancer Unit, University of Cambridge, UK

Harvard Chan Bioinformatics Core