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Table of Contents

Overview and Objectives

Once raw sequence files are generated (in FASTQ format) and quality-checked, the next step in most NGS pipelines is mapping to a reference genome. For individual sequences of interest, it is common to use a tool like BLAST to identify genes or species of origin. However, a typical example will have millions of reads, and a reference space that is frequently billions of bases, which BLAST and similar tools are not really designed to handle.

...

# Request a 180 minute interactive node on the normal queue using our reservation
idev -m 120 -N 1 -A OTH21164 -r CoreNGS
idev -m 120 -N 1 -A TRA23004 -r CoreNGS

# Request a 120 minute idev node on the development queue 
idev -m 120 -N 1 -A OTH21164 -p development
idev -m 120 -N 1 -A TRA23004 -p development

# Using our reservation
sbatch --reseservation=CoreNGS <batch_file>.slurm

Overview

Image Added

After raw sequence files are generated (in FASTQ format), quality-checked, and pre-processed in some way, the next step in many NGS pipelines is mapping to a reference genome.

For individual sequences it is common to use a tool like BLAST to identify genes or species of origin. However a normal NGS dataset will have tens to hundreds of millions of sequences, which BLAST and similar tools are not designed to handle. Thus a large set of computational tools have been developed to quickly align each read to its best location , (if any, ) in a reference.

Even though many mapping tools exist, a few individual programs have a dominant "market share" of the NGS world. These programs vary widely in their design, inputs, outputs, and applications. In this section, In this section, we will primarily focus on two of the most versatile mappersgeneral-purpose ones: BWA and Bowtie2, (the latter being part of the Tuxedo suite (e.g. Tophat2).

Sample Datasets

You have already worked with a paired-end yeast ChIP-seq dataset, which we will continue to use here.  The paired end data should be located at:

$SCRATCH/YEAST_FASTQ_AFTER_DAY_2

We will also use two additional RNA-seq datasets, which are located at:

/corral-repl/utexas/BioITeam/core_ngs_tools/human_stuff

Set up a new directory in your scratch area called 'fastq_align', and populate it with copies the following files, derived from the locations given abovewhich includes the transcriptome-aware RNA-seq aligner Tophat2 as well as other downstream quantification tools).

Stage the alignment data

First connect to ls6.tacc.utexas.edu and start an idev session. This should be second nature by now

idev -m 180 -N 1 -A OTH21164 -r CoreNGS

Then stage the sample datasets and references we will use.

# Copy the FASTA files for building references
mkdir -p $SCRATCH/core_ngs/references/fasta
cp $CORENGS/references/fasta/*.fa   $SCRATCH/core_ngs/references/fasta/

# Copy the FASTQ files that will be used for alignment
mkdir -p $SCRATCH/core_ngs/alignment/fastq
cp $CORENGS/alignment/*fastq.gz $SCRATCH/core_ngs/alignment/fastq/
cd $SCRATCH/core_ngs/alignment/fastq

These are descriptions of the FASTQ files we copied:

...

cds
mkdir fastq_align
cd fastq_align
cp $SCRATCH/YEAST_FASTQ_AFTER_DAY_2 /corral-repl/utexas/BioITeam/core_ngs_tools/human_stuff/*rnaseq.fastq.gz .

Do a fast quality check on the two new data files like you did earlier on the yeast files, and move all files and directories that are produced from the fastQC commands into a new subdirectory called 'fastqc_out'.

cd $SCRATCH/fastq_align
mkdir fastqc_out
module load fastqc
fastqc human_rnaseq.fastq.gz
fastqc human_mirnaseq.fastq.gz
mv *fastqc* fastqc_out

Reference Genomes

Before we get to alignment, we need a genome to align to.  We will use three different references here:  the human genome (hg19), the yeast genome (sacCer3), and mirbase (v20).  Mirbase is a collection of all known microRNAs in all species, and we will use the human subset of that database as our alignment reference.  This has the advantage of being significantly smaller than the human genome, while containing all the sequences we are actually interested in.

These are the three reference genomes we will be using today, with some information about them (and here is information about many more genomes):

Searching genomes, however, is hard work and takes a long time if done on an un-indexed, linear genomic sequence.  So, most aligners require that references be indexed for quick access  The aligners we are using each require a different index, but use the same method (the Burrows-Wheeler Transform) to get the job done.  This requires taking a FASTA file as input, with each chromosome (or contig) as a separate entry, and producing some aligner-specific set of files as output.  Then, those output files are used by the aligner when executing a given alignment command. Hg19 is way too big for us to index here, so we're not going to do it, and it's not included in the core_ngs_tools directory at Corral that we've been copying from.  Instead, all hg19 index files are located at:

/scratch/01063/abattenh/ref_genome/bwa/bwtsw/hg19

Now, we're going to grab the other two references, and we will build each index right before we use it in each set of exercise below.  These two references are located at:

/corral-repl/utexas/BioITeam/core_ngs_tools/references/sacCer3.fa
/corral-repl/utexas/BioITeam/core_ngs_tools/references/hairpin_cDNA_hsa.fa

Now, stage the yeast and mirbase reference fasta files in your scratch area in a directory called 'references'.

cp -r /corral-repl/utexas/BioITeam/core_ngs_tools/references/*.fa $SCRATCH/

With that, we're ready to get started on the first exercise.

Exercise #1: BWA - Yeast ChIP-seq

Like other tools you've worked with so far, you first need to load bwa using the module system.  So, go ahead and do that now, and then enter 'bwa' with no arguments to view the help page.

module load bwa
bwa

As you can see, there are several commands one can use with bwa to do different things.  To test out one of them, we're going to index the genome with the 'index' command.  If you enter 'bwa index' with no arguments, you will see a list of the required arguments.  Here, we only need to specify two things: whether to use 'bwtsw' or 'is' indexing, and the name of the fasta file.  We will specify 'bwtsw' as the indexing option, and the name of the FASTA file is, obviously, sacCer3.fa.  The output of this command, importantly, is a group of files that are all required together as the index.  So, within the references directory, we will create another directory called "yeast", move the yeast FASTA file into that directory, and run the index commands from there:

cd $SCRATCH/references/
mkdir yeast
mv sacCer3.fa yeast
cd yeast
bwa index -a bwtsw sacCer3.fa

This command should produce a set of output files that look like this:

sacCer3.fa
sacCer3.fa.amb
sacCer3.fa.ann
sacCer3.fa.bwt
sacCer3.fa.pac
sacCer3.fa.sa

Now, we're ready to execute the actual alignment, with the goal of producing a SAM/BAM file from the input FASTQ files and reference.  We will first generate SAI files from each of the FASTQ files with the reference individually using the 'aln' command, then combine them (with the reference) into one SAM/BAM output file using the 'sampe' command.   We need a directory to put the alignments when they are finished, as well as any intermediate files, so create a directory called 'alignments'.  The command flow, all together, is as follows. Notice how each file is in its proper directory, which requires us to specify the whole file path in the alignment commands.

cds
mkdir alignments
bwa aln references/yeast/sacCer3.fa fastq_align/Sample_Yeast_L005_R1.cat.fastq.gz > alignments/yeast_R1.sai
bwa aln references/yeast/sacCer3.fa fastq_align/Sample_Yeast_L005_R2.cat.fastq.gz > alignments/yeast_R2.sai
bwa sampe references/yeast/sacCer3.fa alignments/yeast_R1.sai alignments/yeast_R2.sai fastq_align/Sample_Yeast_L005_R1.cat.fastq.gz fastq_align/Sample_Yeast_L005_R2.cat.fastq.gz > alignments/yeast_pairedend.sam

You did it!  In the alignments directory, there should exist the two intermediate files (the SAI files), along with the SAM file that contains the alignments.  It's just a text file, so take a look with head, more, less, tail, or whatever you feel like.  In the next section, with samtools, you'll learn some additional ways to analyze the data.

Exercise #2: Bowtie2 and Local Alignment - Human microRNA-seq

Exercise #3: BWA-MEM (and Tophat2) - Human mRNA-seq

Future Directions

Reference Genomes

Before we get to alignment, we need a reference to align to. This is usually an organism's genome, but can also be any set of names sequences, such as a transcriptome or other set of genes.

Here are the four reference genomes we will be using today, with some information about them. These are not necessarily the most recent versions of these references (e.g. the newest human reference genome is hg38 and a recent miRBase version is v21. (See here for information about many more genomes.)

Searching genomes is computationally hard work and takes a long time if done on linear genomic sequence. So aligners require that references first be indexed to accelerate lookup. The aligners we are using each require a different index, but use the same method (the Burrows-Wheeler Transform) to get the job done.

Building a reference index involves taking a FASTA file as input, with each contig (contiguous string of bases, e.g. a chromosome) as a separate FASTA entry, and producing an aligner-specific set of files as output. Those output index files are then used to perform the sequence alignment, and alignments are reported using coordinates referencing names and offset positions based on the original FASTA file contig entries.

We can quickly index the references for the yeast genome, the human miRNAs, and the V. cholerae genome, because they are all small, so we'll build each index from the appropriate FASTA files right before we use them. 

hg19 is way too big for us to index here so we will use an existing set of BWA hg19 index files located at:

/work/projects/BioITeam/ref_genome/bwa/bwtsw/hg19

/work/projects/BioITeam/ref_genome/
   bowtie2/
   bwa/
   hisat2/
   kallisto/
   star/
   tophat/

Exploring FASTA with grep

It is often useful to know what chromosomes/contigs are in a FASTA file before you start an alignment so that you're familiar with the contig naming convention – and to verify that it's the one you expect.  For example, chromosome 1 is specified differently in different references and organisms: chr1 (USCS human), chrI (UCSC yeast), or just 1 (Ensembl human GRCh37).

A FASTA file consists of a number of contig name entries, each one starting with a right carat ( > ) character, followed by many lines of base characters. E.g.:

>chrI
CCACACCACACCCACACACCCACACACCACACCACACACCACACCACACC
CACACACACACATCCTAACACTACCCTAACACAGCCCTAATCTAACCCTG
GCCAACCTGTCTCTCAACTTACCCTCCATTACCCTGCCTCCACTCGTTAC
CCTGTCCCATTCAACCATACCACTCCGAACCACCATCCATCCCTCTACTT

How do we dig out just the lines that have the contig names and ignore all the sequences? Well, the contig name lines all follow the pattern above, and since the > character is not a valid base, it will never appear on a sequence line.

We've discovered a pattern (also known as a regular expression) to use in searching, and the command line tool that does regular expression matching is grep (general regular expression parser). (Read more about grep and regular expressions)

Regular expressions are so powerful that nearly every modern computer language includes a "regex" module of some sort. There are many online tutorials for regular expressions, and several slightly different "flavors" of them. But the most common is the Perl style (http://perldoc.perl.org/perlretut.html), which was one of the fist and still the most powerful (there's a reason Perl was used extensively when assembling the human genome). We're only going to use simple regular expressions here, but learning more about them will pay handsome dividends for you in the future.

Here's how to execute grep to list contig names in a FASTA file.

# Stage the FASTA files
cds
mkdir -p core_ngs/references/fasta
cd core_ngs/references/fasta
cp $CORENGS/references/fasta/*.fa .

cd $SCRATCH/core_ngs/references/fasta
grep -P '^>' sacCer3.fa | more

Notes:

• The -P option tells grep to Perl-style regular expression patterns. 
  
  • This makes including special characters like Tab ( \t ), carriage return ( \r ) or linefeed ( \n ) much easier that the default POSIX paterns.
  • While it is not required here, it generally doesn't hurt to include this option.

• '^>' is the regular expression describing the pattern we're looking for (described below)

• sacCer3.fa is the file to search. 
  
  • lines with text that match our pattern will be written to standard output
  • non matching lines will be omitted
• We pipe to more just in case there are a lot of contig names.

Now down to the nuts and bolts of the pattern: '^>'

First, the single quotes around the pattern – this tells the bash shell to pass the exact string contents to grep.

As we have seen, during command line parsing and evaluation the shell will often look for special metacharacters on the command line that mean something to it (for example, the $ in front of an environment variable name, like in $SCRATCH). Well, regular expressions treat the $ specially too – but in a completely different way! Those single quotes tell the shell "don't look inside here for special characters – treat this as a literal string and pass it to the program". The shell will obey, will strip the single quotes off the string, and will pass the actual pattern, ^>, to the grep program. (Read more about Literal characters and metacharacters and Quoting in the shell)

So what does ^> mean to grep? We know that contig name lines always start with a > character, so > is a literal for grep to use in its pattern match.

We might be able to get away with just using this literal alone as our regex, specifying '>' as the command line pattern argument. But for grep, the more specific the pattern, the better. So we constrain where the > can appear on the line. The special carat ( ^ )  metacharacter represents "beginning of line". So ^> means "beginning of a line followed by a > character".

# Copy the FASTA files for building references
mkdir -p $SCRATCH/core_ngs/references/fasta
cp $CORENGS/references/fasta/*.fa $SCRATCH/core_ngs/references/fasta/

Exercise: How many contigs are there in the sacCer3 reference?

cd $SCRATCH/core_ngs/references/fasta
grep -P '^>' sacCer3.fa | wc -l

grep -P -c '^>' sacCer3.fa

Aligner overview

There are many aligners available, but we will concentrate on two of the most popular general-purpose ones: bwa and bowtie2. The table below outlines the available protocols for them.

Exercise #1: BWA global alignment – Yeast ChIP-seq

Overview ChIP-seq alignment workflow with BWA

We will perform a global alignment of the paired-end Yeast ChIP-seq sequences using bwa. This workflow has the following steps:

1. Trim the FASTQ sequences down to 50 with fastx_clipper
   • this removes most of any 5' adapter contamination without the fuss of specific adapter trimming w/cutadapt
2. Prepare the sacCer3 reference index for bwa using bwa index
   
   • this is done once, and re-used for later alignments
3. Perform a global bwa alignment on the R1 reads (bwa aln) producing a BWA-specific binary .sai intermediate file
4. Perform a global bwa alignment on the R2 reads (bwa aln) producing a BWA-specific binary .sai intermediate file
5. Perform pairing of the separately aligned reads and report the alignments in SAM format using bwa sampe
6. Convert the SAM file to a BAM file (samtools view)
7. Sort the BAM file by genomic location (samtools sort)
8. Index the BAM file (samtools index)
9. Gather simple alignment statistics (samtools flagstat and samtools idxstats)

We're going to skip the trimming step and perform steps 2 - 5 now, leaving samtools for a later exercise since steps 6 - 10 are common to nearly all post-alignment workflows.

Introducing BWA

Like other tools you've worked with so far, you first need to load bwa. Do that now, and then enter bwa with no arguments to view the top-level help page (many NGS tools will provide some help when called with no arguments). bwa is available as a BioContainers. module.

Make sure you're in an idev session

idev -m 180 -N 1 -A OTH21164 -r CoreNGS      # or -A TRA23004

idev -m 120 -N 1 -A OTH21164 -p development  # or -A TRA23004

module load biocontainers  # takes a while
module load bwa
bwa

Program: bwa (alignment via Burrows-Wheeler transformation)
Version: 0.7.17-r1188
Contact: Heng Li <lh3@sanger.ac.uk>

Usage:   bwa <command> [options]

Command: index         index sequences in the FASTA format
         mem           BWA-MEM algorithm
         fastmap       identify super-maximal exact matches
         pemerge       merge overlapping paired ends (EXPERIMENTAL)
         aln           gapped/ungapped alignment
         samse         generate alignment (single ended)
         sampe         generate alignment (paired ended)
         bwasw         BWA-SW for long queries

         shm           manage indices in shared memory
         fa2pac        convert FASTA to PAC format
         pac2bwt       generate BWT from PAC
         pac2bwtgen    alternative algorithm for generating BWT
         bwtupdate     update .bwt to the new format
         bwt2sa        generate SA from BWT and Occ

Note: To use BWA, you need to first index the genome with `bwa index'.
      There are three alignment algorithms in BWA: `mem', `bwasw', and
      `aln/samse/sampe'. If you are not sure which to use, try `bwa mem'
      first. Please `man ./bwa.1' for the manual.

As you can see, bwa include many sub-commands that perform the tasks we are interested in.

Building the BWA sacCer3 index

We will index the genome with the bwa index command. Type bwa index with no arguments to see usage for this sub-command.

Usage:   bwa index [options] <in.fasta>

Options: -a STR    BWT construction algorithm: bwtsw, is or rb2 [auto]
         -p STR    prefix of the index [same as fasta name]
         -b INT    block size for the bwtsw algorithm (effective with -a bwtsw) [10000000]
         -6        index files named as <in.fasta>.64.* instead of <in.fasta>.*

Warning: `-a bwtsw' does not work for short genomes, while `-a is' and
         `-a div' do not work not for long genomes.

Based on the usage description, we only need to specify two things:

• The name of the FASTA file  
• Whether to use the bwtsw or is algorithm for indexing

Since sacCer3 is relative large (~12 Mbp) we will specify bwtsw as the indexing option (as indicated by the "Warning" message), and the name of the FASTA file is sacCer3.fa.

The output of this command is a group of files that are all required together as the index. So, within our references directory, we will create another directory called references/bwa/sacCer3 and build the index there. To remind ourselves which FASTA was used to build the index, we create a symbolic link to our references/fasta/sacCer3.fa file (note the use of the ../.. relative path syntax).

mkdir -p $SCRATCH/core_ngs/alignment/fastq
mkdir -p $SCRATCH/core_ngs/references/fasta
cp $CORENGS/alignment/*fastq.gz   $SCRATCH/core_ngs/alignment/fastq/
cp $CORENGS/references/fasta/*.fa $SCRATCH/core_ngs/references/fasta/

mkdir -p $SCRATCH/core_ngs/references/bwa/sacCer3
cd $SCRATCH/core_ngs/references/bwa/sacCer3
ln -sf ../../fasta/sacCer3.fa
ls -l

Now execute the bwa index command.

bwa index -a bwtsw sacCer3.fa

Since the yeast genome is not large when compared to human, this should not take long to execute (otherwise we would do it as a batch job). When it is complete you should see a set of index files like this:

sacCer3.fa
sacCer3.fa.amb
sacCer3.fa.ann
sacCer3.fa.bwt
sacCer3.fa.pac
sacCer3.fa.sa

Performing the bwa alignment

Now, we're ready to execute the actual alignment, with the goal of initially producing a SAM file from the input FASTQ files and reference. First prepare a directory for this exercise and link the sacCer3 reference directories there (this will make our commands more readable).

# Copy the FASTA files for building references
mkdir -p $SCRATCH/core_ngs/references
cp $CORENGS/references/fasta/*.fa $SCRATCH/core_ngs/references/fasta/

# Copy a pre-built bwa index for sacCer3
mkdir -p $SCRATCH/core_ngs/references/bwa/sacCer3
cp $CORENGS/references/bwa/sacCer3/*.* $SCRATCH/core_ngs/references/bwa/sacCer3/

# Get the FASTQ to align
mkdir -p $SCRATCH/core_ngs/alignment/fastq
cp $CORENGS/alignment/*fastq.gz $SCRATCH/core_ngs/alignment/fastq/

mkdir -p $SCRATCH/core_ngs/alignment/yeast_bwa
cd $SCRATCH/core_ngs/alignment/yeast_bwa
ln -sf ../fastq
ln -sf ../../references/bwa/sacCer3

As our workflow indicated, we first use bwa aln on the R1 and R2 FASTQs, producing a BWA-specific .sai intermediate binary files.

What does bwa aln needs in the way of arguments?

bwa aln

Required arguments are a <prefix> of the bwa index files, and the input FASTQ file. There are lots of options, but here is a summary of the most important ones.

Other options control the details of how much a mismatch or gap is penalized, limits on the number of acceptable hits per read, and so on. Much more information can be found on the BWA manual page.

For a basic alignment like this, we can just go with the default alignment parameters.

Note that bwa writes its (binary) output to standard output by default, so we need to redirect that to a .sai file.

For simplicity, we will just execute these commands directly, one at a time. Each command should only take few minutes and you will see bwa's progress messages in your terminal.

# If not already loaded:
module load biocontainers
module load bwa

cd $SCRATCH/core_ngs/alignment/yeast_bwa
bwa aln sacCer3/sacCer3.fa fastq/Sample_Yeast_L005_R1.cat.fastq.gz > yeast_pe_R1.sai
bwa aln sacCer3/sacCer3.fa fastq/Sample_Yeast_L005_R2.cat.fastq.gz > yeast_pe_R2.sai

When all is done you should have two .sai files: yeast_pe_R1.sai and yeast_pe_R2.sai.

Exercise: How long did it take to align the R2 file?

[bwa_aln] 17bp reads: max_diff = 2
[bwa_aln] 38bp reads: max_diff = 3
[bwa_aln] 64bp reads: max_diff = 4
[bwa_aln] 93bp reads: max_diff = 5
[bwa_aln] 124bp reads: max_diff = 6
[bwa_aln] 157bp reads: max_diff = 7
[bwa_aln] 190bp reads: max_diff = 8
[bwa_aln] 225bp reads: max_diff = 9
[bwa_aln_core] calculate SA coordinate... 50.76 sec
[bwa_aln_core] write to the disk... 0.07 sec
[bwa_aln_core] 262144 sequences have been processed.
[bwa_aln_core] calculate SA coordinate... 50.35 sec
[bwa_aln_core] write to the disk... 0.07 sec
[bwa_aln_core] 524288 sequences have been processed.
[bwa_aln_core] calculate SA coordinate... 13.64 sec
[bwa_aln_core] write to the disk... 0.01 sec
[bwa_aln_core] 592180 sequences have been processed.
[main] Version: 0.7.17-r1188
[main] CMD: /usr/local/bin/bwa aln sacCer3/sacCer3.fa fastq/Sample_Yeast_L005_R1.cat.fastq.gz
[main] Real time: 85.584 sec; CPU: 83.825 sec

Since you have your own private compute node, you can use all its resources. It has 128 cores, so re-run the R2 alignment asking for 60 execution threads.

bwa aln -t 60 sacCer3/sacCer3.fa fastq/Sample_Yeast_L005_R2.cat.fastq.gz > yeast_pe_R2.sai

Exercise: How much of a speedup did you seen when aligning the R2 file with 60 threads?

[bwa_aln] 17bp reads: max_diff = 2
[bwa_aln] 38bp reads: max_diff = 3
[bwa_aln] 64bp reads: max_diff = 4
[bwa_aln] 93bp reads: max_diff = 5
[bwa_aln] 124bp reads: max_diff = 6
[bwa_aln] 157bp reads: max_diff = 7
[bwa_aln] 190bp reads: max_diff = 8
[bwa_aln] 225bp reads: max_diff = 9
[bwa_aln_core] calculate SA coordinate... 266.70 sec
[bwa_aln_core] write to the disk... 0.04 sec
[bwa_aln_core] 262144 sequences have been processed.
[bwa_aln_core] calculate SA coordinate... 268.94 sec
[bwa_aln_core] write to the disk... 0.03 sec
[bwa_aln_core] 524288 sequences have been processed.
[bwa_aln_core] calculate SA coordinate... 72.26 sec
[bwa_aln_core] write to the disk... 0.01 sec
[bwa_aln_core] 592180 sequences have been processed.
[main] Version: 0.7.17-r1188
[main] CMD: /usr/local/bin/bwa aln -t 60 sacCer3/sacCer3.fa fastq/Sample_Yeast_L005_R2.cat.fastq.gz
[main] Real time: 7.931 sec; CPU: 179.153 sec

Next we use the bwa sampe command to pair the reads and output SAM format data. Just type that command in with no arguments to see its usage.

For this command you provide the same reference index prefix as for bwa aln, along with the two .sai files and the two original FASTQ files. Also, bwa writes its output to standard output, so redirect that to a .sam file.

Here is the command line statement you need. Just execute it on the command line.

# Copy the FASTA files for building references
mkdir -p $SCRATCH/core_ngs/references
cp $CORENGS/references/fasta/*.fa $SCRATCH/core_ngs/references/fasta/

# Copy a pre-built bwa index for sacCer3
mkdir -p $SCRATCH/core_ngs/references/bwa/sacCer3
cp $CORENGS/references/bwa/sacCer3/*.* $SCRATCH/core_ngs/references/bwa/sacCer3/

# Get the FASTQ to align
mkdir -p $SCRATCH/core_ngs/alignment/fastq
cp $CORENGS/alignment/*fastq.gz $SCRATCH/core_ngs/alignment/fastq/

# Stage the BWA .sai files
mkdir -p $SCRATCH/core_ngs/alignment/yeast_bwa
cd $SCRATCH/core_ngs/alignment/yeast_bwa
ln -sf ../fastq
ln -sf ../../references/bwa/sacCer3
cp $CORENGS/catchup/yeast_bwa/*.sai .

cd $SCRATCH/core_ngs/alignment/yeast_bwa
bwa sampe sacCer3/sacCer3.fa yeast_pe_R1.sai yeast_pe_R2.sai \
  fastq/Sample_Yeast_L005_R1.cat.fastq.gz \
  fastq/Sample_Yeast_L005_R2.cat.fastq.gz > yeast_pe.sam

You should now have a SAM file (yeast_pe.sam) that contains the alignments.

Exercise: How many lines does the SAM file have? How does this compare to the number of input sequences (R1+R2)?

It's just a text file, so take a look with head, more, less, tail, or whatever you feel like. Later you'll learn additional ways to analyze the data with samtools once you create a BAM file.

Exercise: What kind of information is in the first lines of the SAM file?

Exercise: How many alignment records (not header records) are in the SAM file?

grep -P -c '^HWI' yeast_pe.sam

grep -P -v -c '^@' yeast_pe.sam

Exercises:

• Does the SAM file contain both mapped and un-mapped reads?
• What is the order of the alignment records in this SAM file?
  
  

Using cut to isolate fields

Recall the format of a SAM alignment record:

Image Added

Suppose you wanted to look only at field 3 (contig name) values in the SAM file. You can do this with the handy cut command. Below is a simple example where you're asking cut to display the 3rd column value for the last 10 alignment records.

# Stage the aligned SAM file
mkdir -p $SCRATCH/core_ngs/alignment/yeast_bwa
cd $SCRATCH/core_ngs/alignment/yeast_bwa
cp $CORENGS/catchup/yeast_bwa/yeast_pe.sam .

tail yeast_pe.sam | cut -f 3

By default cut assumes the field delimiter is Tab, which is the delimiter used in the majority of NGS file formats. You can specify a different delimiter with the -d option.

You can also specify a range of fields, and mix adjacent and non-adjacent fields. This displays fields 2 through 6, field 9:

tail -20 yeast_pe.sam | cut -f 2-6,9

You may have noticed that some alignment records contain contig names (e.g. chrV) in field 3 while others contain an asterisk ( * ). The * means the record didn't map. We're going to use this heuristic along with cut to see about how many records represent aligned sequences. (Note this is not the strictly correct method of finding unmapped reads because not all unmapped reads have an asterisk in field 3. Later you'll see how to properly distinguish between mapped and unmapped reads using samtools.)

First we need to make sure that we don't look at fields in the SAM header lines. We're going to end up with a series of pipe operations, and the best way to make sure you're on track is to enter them one at a time piping to head:

# the ^@ pattern matches lines starting with @ (only header lines), 
# and -v says output lines that don't match
grep -v -P '^@' yeast_pe.sam | head

Ok, it looks like we're seeing only alignment records. Now let's pull out only field 3 using cut:

grep -v -P '^@' yeast_pairedend.sam | cut -f 3 | head

Cool, we're only seeing the contig name info now. Next we use grep again, piping it our contig info and using the -v (invert) switch to say print lines that don't match the pattern:

grep -v -P '^@' yeast_pe.sam | cut -f 3 | grep -v '*' | head

Perfect! We're only seeing real contig names that (usually) represent aligned reads. Let's count them by piping to wc -l (and omitting omit head of course – we want to count everything).

grep -v -P '^@' yeast_pe.sam | cut -f 3 | grep -v '*' | wc -l

Read more at Some Linux commands: Advanced commands

Exercise: About how many records represent aligned sequences? What alignment rate does this represent?

awk 'BEGIN{print 612968/1184360}'

Exercise: What might we try in order to improve the alignment rate?

Exercise #2: Basic SAMtools Utilities

The SAMtools program is a commonly used set of tools that allow a user to manipulate SAM/BAM files in many different ways, ranging from simple tasks (like SAM/BAM format conversion) to more complex functions (like sorting, indexing and statistics gathering).  It is available in the TACC module system (as well as in BioContainers). Load that module and see what samtools has to offer:

idev -m 120 -N 1 -A OTH21164 -r CoreNGS     # or -A TRA23004

idev -m 90 -N 1 -A OTH21164 -p development  # or -A TRA23004

# If not already loaded
module load biocontainers  # takes a while

module load samtools
samtools

Program: samtools (Tools for alignments in the SAM format)
Version: 1.9 (using htslib 1.9)

Usage:   samtools <command> [options]

Commands:
  -- Indexing
     dict           create a sequence dictionary file
     faidx          index/extract FASTA
     fqidx          index/extract FASTQ
     index          index alignment

  -- Editing
     calmd          recalculate MD/NM tags and '=' bases
     fixmate        fix mate information
     reheader       replace BAM header
     targetcut      cut fosmid regions (for fosmid pool only)
     addreplacerg   adds or replaces RG tags
     markdup        mark duplicates

  -- File operations
     collate        shuffle and group alignments by name
     cat            concatenate BAMs
     merge          merge sorted alignments
     mpileup        multi-way pileup
     sort           sort alignment file
     split          splits a file by read group
     quickcheck     quickly check if SAM/BAM/CRAM file appears intact
     fastq          converts a BAM to a FASTQ
     fasta          converts a BAM to a FASTA

  -- Statistics
     bedcov         read depth per BED region
     coverage       alignment depth and percent coverage
     depth          compute the depth
     flagstat       simple stats
     idxstats       BAM index stats
     phase          phase heterozygotes
     stats          generate stats (former bamcheck)

  -- Viewing
     flags          explain BAM flags
     tview          text alignment viewer
     view           SAM<->BAM<->CRAM conversion
     depad          convert padded BAM to unpadded BAM

In this exercise, we will explore five utilities provided by samtools: view, sort, index, flagstat, and idxstats. Each of these is executed in one line for a given SAM/BAM file. In the SAMtools/BEDtools sections tomorrow we will explore samtools capabilities more in depth.

samtools view

The samtools view utility provides a way of converting between SAM (text) and BAM (binary, compressed) format. It also provides many, many other functions which we will discuss lster. To get a preview, execute samtools view without any other arguments. You should see:

Usage: samtools view [options] <in.bam>|<in.sam>|<in.cram> [region ...]

Options:
  -b       output BAM
  -C       output CRAM (requires -T)
  -1       use fast BAM compression (implies -b)
  -u       uncompressed BAM output (implies -b)
  -h       include header in SAM output
  -H       print SAM header only (no alignments)
  -c       print only the count of matching records
  -o FILE  output file name [stdout]
  -U FILE  output reads not selected by filters to FILE [null]
  -t FILE  FILE listing reference names and lengths (see long help) [null]
  -X       include customized index file
  -L FILE  only include reads overlapping this BED FILE [null]
  -r STR   only include reads in read group STR [null]
  -R FILE  only include reads with read group listed in FILE [null]
  -d STR:STR
           only include reads with tag STR and associated value STR [null]
  -D STR:FILE
           only include reads with tag STR and associated values listed in
           FILE [null]
  -q INT   only include reads with mapping quality >= INT [0]
  -l STR   only include reads in library STR [null]
  -m INT   only include reads with number of CIGAR operations consuming
           query sequence >= INT [0]
  -f INT   only include reads with all  of the FLAGs in INT present [0]
  -F INT   only include reads with none of the FLAGS in INT present [0]
  -G INT   only EXCLUDE reads with all  of the FLAGs in INT present [0]
  -s FLOAT subsample reads (given INT.FRAC option value, 0.FRAC is the
           fraction of templates/read pairs to keep; INT part sets seed)
  -M       use the multi-region iterator (increases the speed, removes
           duplicates and outputs the reads as they are ordered in the file)
  -x STR   read tag to strip (repeatable) [null]
  -B       collapse the backward CIGAR operation
  -?       print long help, including note about region specification
  -S       ignored (input format is auto-detected)
  --no-PG  do not add a PG line
      --input-fmt-option OPT[=VAL]
               Specify a single input file format option in the form
               of OPTION or OPTION=VALUE
  -O, --output-fmt FORMAT[,OPT[=VAL]]...
               Specify output format (SAM, BAM, CRAM)
      --output-fmt-option OPT[=VAL]
               Specify a single output file format option in the form
               of OPTION or OPTION=VALUE
  -T, --reference FILE
               Reference sequence FASTA FILE [null]
  -@, --threads INT
               Number of additional threads to use [0]
      --write-index
               Automatically index the output files [off]
      --verbosity INT
               Set level of verbosity

That is a lot to process! For now, we just want to read in a SAM file and output a BAM file. The input format is auto-detected, so we don't need to specify it (although you do in v0.1.19). We just need to tell the tool to output the file in BAM format, and to include the header records.

mkdir -p $SCRATCH/core_ngs/alignment/yeast_bwa
cd $SCRATCH/core_ngs/alignment/yeast_bwa
cp $CORENGS/catchup/yeast_bwa/yeast_pe.sam .

cd $SCRATCH/core_ngs/alignment/yeast_bwa
samtools view -b yeast_pe.sam > yeast_pe.bam 

• the -b option tells the tool to output BAM format

The BAM file is a binary file, not a text file, so how do you look at its contents now? Just use samtools view without the -b option. Remember to pipe output to a pager!

samtools view yeast_pe.bam | more

Notice that this does not show us the header record we saw at the start of the SAM file.

Exercise: What samtools view option will include the header records in its output? Which option would show only the header records?

samtools sort

Looking at some of the alignment record information (e.g. samtools view yeast_pe.bam | cut -f 1-4 | more), you will notice that read names appear in adjacent pairs (for the R1 and R2), in the same order they appeared in the original FASTQ file. Since that means the corresponding mappings are in no particular order, searching through the file very inefficient. samtools sort re-orders entries in the SAM file either by locus (contig name + coordinate position) or by read name.

If you execute samtools sort without any options, you see its help page:

Usage: samtools sort [options...] [in.bam]
Options:
  -l INT     Set compression level, from 0 (uncompressed) to 9 (best)
  -m INT     Set maximum memory per thread; suffix K/M/G recognized [768M]
  -n         Sort by read name
  -t TAG     Sort by value of TAG. Uses position as secondary index (or read name if -n is set)
  -o FILE    Write final output to FILE rather than standard output
  -T PREFIX  Write temporary files to PREFIX.nnnn.bam
      --input-fmt-option OPT[=VAL]
               Specify a single input file format option in the form
               of OPTION or OPTION=VALUE
  -O, --output-fmt FORMAT[,OPT[=VAL]]...
               Specify output format (SAM, BAM, CRAM)
      --output-fmt-option OPT[=VAL]
               Specify a single output file format option in the form
               of OPTION or OPTION=VALUE
      --reference FILE
               Reference sequence FASTA FILE [null]
  -@, --threads INT
               Number of additional threads to use [0]

In most cases you will be sorting a BAM file from name order to locus order. You can use either -o or redirection with > to control the output.

# Stage the aligned yeast SAM and BAM files
mkdir -p $SCRATCH/core_ngs/alignment/yeast_bwa
cd $SCRATCH/core_ngs/alignment/yeast_bwa
cp $CORENGS/catchup/yeast_bwa/yeast_pe.[bs]am .

To sort the paired-end yeast BAM file by position, and get a BAM file named yeast_pe.sort.bam as output, execute the following command:

cd $SCRATCH/core_ngs/alignment/yeast_bwa
samtools sort -O bam -T yeast_pe.tmp yeast_pe.bam > yeast_pe.sort.bam

• The -O options says the Output format should be BAM
• The -T options gives a prefix for Temporary files produced during sorting
  • sorting large BAMs will produce many temporary files during processing
  • make sure the temporary file prefix is different from the input BAM file prefix!
• By default sort writes its output to standard output, so we use > to redirect to a file named yeast_pairedend.sort.bam

Exercise: Compare the file sizes of the yeast_pe .sam, .bam, and .sort.bam files and explain why they are different.

ls -lh yeast_pe.*

samtools index

Many tools (like IGV, the Integrative Genomics Viewer) only need to use portions of a BAM file at a given point in time. For example, if you are viewing alignments that are within a particular gene, alignment records on other chromosomes do not need to be loaded. In order to speed up access, BAM files are indexed, producing BAI files which allow fast random access. This is especially important when you have many alignment records.

The utility samtools index creates an index that has the same name as the input BAM file, with suffix .bai appended. Here's the samtools index usage:

Usage: samtools index [-bc] [-m INT] <in.bam> [out.index]
Options:
  -b       Generate BAI-format index for BAM files [default]
  -c       Generate CSI-format index for BAM files
  -m INT   Set minimum interval size for CSI indices to 2^INT [14]
  -@ INT   Sets the number of threads [none]

The syntax here is way, way easier. We want a BAI-format index which is the default. (CSI-format is used with extremely long contigs, which don't apply here - the most common use case is for polyploid plant genomes).

So all we have to provide is the sorted BAM:

samtools index yeast_pe.sort.bam

This will produce a file named yeast_pe.bam.bai.

Most of the time when an index is required, it will be automatically located as long as it is in the same directory as its BAM file and shares the same name up until the .bai extension.

Exercise: Compare the sizes of the sorted BAM file and its BAI index.

ls -lh yeast_pe.sort.bam*

samtools flagstat

Since the BAM file contains records for both mapped and unmapped reads, just counting records doesn't provide information about the mapping rate of our alignment. The samtools flagstat tool provides a simple analysis of mapping rate based on the the SAM flag fields.

Here's how to run samtools flagstat and both see the output in the terminal and save it in a file – the samtools flagstat standard output is piped to tee, which both writes it to the specified file and sends it to its standard output:

# Stage the aligned yeast SAM and BAM files
mkdir -p $SCRATCH/core_ngs/alignment/yeast_bwa
cd $SCRATCH/core_ngs/alignment/yeast_bwa
cp $CORENGS/catchup/yeast_bwa/yeast_pe.sort.bam* .

samtools flagstat yeast_pe.sort.bam | tee yeast_pe.flagstat.txt

You should see something like this:

1184360 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 secondary
0 + 0 supplementary
0 + 0 duplicates
547664 + 0 mapped (46.24% : N/A)
1184360 + 0 paired in sequencing
592180 + 0 read1
592180 + 0 read2
473114 + 0 properly paired (39.95% : N/A)
482360 + 0 with itself and mate mapped
65304 + 0 singletons (5.51% : N/A)
534 + 0 with mate mapped to a different chr
227 + 0 with mate mapped to a different chr (mapQ>=5)

Ignore the "+ 0" addition to each line - that is a carry-over convention for counting "QA-failed reads" that is no longer relevant.

The most important statistic is the mapping rate (here 46%) but this readout also allows you to verify that some common expectations (e.g. that about the same number of R1 and R2 reads aligned, and that most mapped reads are proper pairs) are met.

Exercise: What proportion of mapped reads were properly paired?

awk 'BEGIN{ print 473114 / 547664 }'
# or
echo $(( 473114 * 100 / 547664 ))
# or
echo "473114 547664" | awk '{printf("%0.1f%%\n", 100*$1/$2)}'

samtools idxstats

More information about the alignment is provided by the samtools idxstats report, which shows how many reads aligned to each contig in your reference. Note that samtools idxstats must be run on a sorted, indexed BAM file.

# Stage the aligned yeast SAM and BAM files
mkdir -p $SCRATCH/core_ngs/alignment/yeast_bwa
cd $SCRATCH/core_ngs/alignment/yeast_bwa
cp $CORENGS/catchup/yeast_bwa/yeast_pe.sort.bam* .

samtools idxstats yeast_pe.sort.bam | tee yeast_pe.idxstats.txt

Here we use the tee command which reports its outputto standard output before also writing it to the specified file.

chrI    230218  8820    1640
chrII   813184  36616   4026
chrIII  316620  13973   1530
chrIV   1531933 72675   8039
chrV    576874  27466   2806
chrVI   270161  10866   1222
chrVII  1090940 50893   5786
chrVIII 562643  24672   3273
chrIX   439888  16246   1739
chrX    745751  31748   3611
chrXI   666816  28017   2776
chrXII  1078177 54783   10124
chrXIII 924431  40921   4556
chrXIV  784333  33070   3703
chrXV   1091291 48714   5150
chrXVI  948066  44916   5032
chrM    85779   3268    291
*       0       0       571392

The output has four tab-delimited columns:

1. contig name
2. contig length
3. number of mapped reads
4. number of unmapped reads

The reason that the "unmapped reads" field for named chromosomes is not zero is that the aligner may initially assign a potential mapping (contig name and start coordinate) to a read, but then mark it later as unampped if it does meet various quality thresholds.