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Overview

Image RemovedImage 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.

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Even though many mapping tools exist, a few individual programs have a dominant "market share" of the NGS world. In this section, we will primarily focus on two of the most versatile general-purpose ones: BWA and Bowtie2 (the latter being part of the Tuxedo suite which includes the transcriptome-aware RNA-seq aligner Tophat2 as well as other downstream quantifiaction quantification tools).

Stage the alignment data

First connect to stampede2ls6.tacc.utexas.edu and start an idev session. This should be second nature by now

idev -pm normal180 -mN 1801 -A UT-2015-05-18OTH21164 -Nr 1 -n 68 CoreNGS-Thu

Then stage the sample datasets and references we will use.

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

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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 the most a recent miRBase annotation is  version is v21. (See here for information about many more genomes.)

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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.

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/work2work/projects/BioITeam/ref_genome/bwa/bwtsw/hg19

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/work2work/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).

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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 here: Advanced commands: 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 the most simple of regular expressions here, but learning more about them will pay handsome dividends for you in the future.

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# 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

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• The -P option tells grep to Perl-style regular expression patterns. 
  
  • This makes including special characters like Tab ( \t  ), Carriage Return carriage return ( \r ) or Linefeed 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 part of its friendly we have seen, during command line parsing and evaluation , the shell will often look for special characters metacharacters on the command line that mean something to it (for example, the 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 Those single quotes tell 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. (Note that the shell does look inside double quotes ( " ) for certain special signals, such as looking for environment variable names to evaluate. Read more about  program. (Read more about 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 as the command line pattern argument. But for 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".Exercise: How many contigs are there in the sacCer3 reference?

# 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

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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 idxstatidxstats)

We're going to skip the trimming step for now and see how it goes. We'll perform steps 2 - 5 now and leave 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). Note that bwa is available both from the standard TACC module system and as as a BioContainers. module.

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Make sure you're in

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an idev session

idev -

m 

180 -

N 

1 -A OTH21164 

-

r CoreNGS-Thu
# or
idev -m 90 -N 1 -

A OTH21164 -p development

module load biocontainers  # takes a while
module load bwa
bwa

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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.

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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 -ssf ../../fasta/sacCer3.fa
ls -l

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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 pre-built FASTA files for building references
mkdir -p $SCRATCH/core_ngs/references
cp $CORENGS/references/fasta/*.fa $SCRATCH/core_ngs/references/fasta/

# Get the FASTQ to align Copy a pre-built bwa index for sacCer3
mkdir -p $SCRATCH/core_ngs/references/alignmentbwa/fastqsacCer3
cp $CORENGS/references/bwa/alignmentsacCer3/*fastq.gz* $SCRATCH/core_ngs/references/alignmentbwa/fastqsacCer3/

# 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 -s -fsf ../fastq
ln -s -fsf ../../references/bwa/sacCer3

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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.

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# 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.

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[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: 12278.936185 sec; CPU: 12377.597598 sec

Since you have your own private compute node, you can use all its resources. It has 68 128 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 20 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: 195.872013 sec; CPU: 617142.095813 sec

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

Exercise: How many sequences were in the R1 and R2 FASTQ files combined?

bwa sampe sacCer3/sacCer3.fa yeast_R1.sai yeast_R2.sai \
  fastq/Sample_Yeast_L005_R1.cat.fastq.gz \
  fastq/Sample_Yeast_L005_R2.cat.fastq.gz > yeast_pairedend.sam

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

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This looks for the pattern  '^HWI' which is the start of every read name (which starts every alignment record).
Remember -c says just count the records, don't display them.

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

Or use the -v (invert) option to tell grep to print all lines that don't match a particular pattern; here, all header lines, which start with @.

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

# 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)?

Exercises:

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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?

Using cut to isolate fields

Recall the format of a SAM alignment record:

Image Removed

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

grep -P -c '^HWI' yeast_

pe.sam

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.

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grep -P -v -c '^@' yeast_

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_pairedend.sam | head

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

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 | head

...

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_pairedendpe.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_pairedend.sam | cut -f 3 | grep -v '*' | wc -l

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

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:

or about 51%. Not great.

Note we perform this calculation in awk's BEGIN block, which is always executed, instead of the body block, which is only executed for lines of input. And here we call awk without piping it any input. See Linux fundamentals: cut,sort,uniq,grep,awk

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:

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

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

idev -p normal -m 120 -A UT-2015-05-18 -N 1 -n 68 

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

module load samtools
samtools

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

# 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 in 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:

...

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

cd $SCRATCH/core_ngs/alignment/yeast_bwa
cat
yeast_pairedend.sam | samtools view -b -oyeast_pe.sam > yeast_pairedendpe.bam 

• the -b option tells the tool to output BAM format
• the -o option specifies the name of the output BAM file that will be created
• we pipe the entire SAM file to samtools view so that the header records are included (required for SAM → BAM conversion)
  • samtools view reads its input from standard input by default

How do you look at the BAM file contents now? That's simple. 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_pairedendpe.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_pairedendpe.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.

...

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
  --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
      --reference FILE
               Reference sequence FASTA FILE [null]
  -@, --threads INT
               Number of additional threads to use [0]
      --verbosity INT
               Set level of verbosity

...

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_pairedend.bampe.[bs]am .

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

cd $SCRATCH/core_ngs/alignment/yeast_bwa
samtools sort -O bam -T yeast_pairedendpe.tmp yeast_pairedendpe.bam > yeast_pairedendpe.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_pariedend pe .sam, .bam, and .sort.bam files and explain why they are different.

ls -lh yeast_pairedendpe.*

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.

...

samtools index yeast_pairedendpe.sort.bam

This will produce a file named yeast_pairedendpe.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.

...

ls -lh yeast_pairedendpe.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:

samtools flagstat yeast_pairedend.sort.bam | tee yeast_pariedend.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 ))

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.

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

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.

Exercise #3: PE alignment with BioITeam scripts

Now that you've done everything the hard way, let's see how to do run an alignment pipeline using a BWA alignment script maintained by the BioITeam,  /work2/projects/BioITeam/common/script/align_bwa_illumina.sh. Type in the script name to see its usage.

align_bwa_illumina.sh 2021_06_05
Align Illumina SE or PE data with bwa. Produces a sorted, indexed,
duplicate-marked BAM file and various statistics files. Usage:

align_bwa_illumina.sh <aln_mode> <in_file> <out_pfx> <assembly> [ paired trim_sz trim_sz2 seq_fmt qual_fmt ]

Required arguments:
  aln_mode  Alignment mode, either global (bwa aln) or local (bwa mem).
  in_file   For single-end alignments, path to input sequence file.
            For paired-end alignments using fastq, path to the the R1
            fastq file which must contain the string 'R1' in its name.
            The corresponding 'R2' must have the same path except for 'R1'.
  out_pfx   Desired prefix of output files in the current directory.
  assembly  One of hg38, hg19, hg38, mm10, mm9, sacCer3, sacCer1, ce11, ce10,
            danRer7, hs_mirbase, mm_mirbase, or reference index prefix.
Optional arguments:
  paired    0 = single end alignment (default); 1 = paired end.
  trim_sz   Size to trim reads to. Default 0 (no trimming)
  trim_sz2  Size to trim R2 reads to for paired end alignments.
            Defaults to trim_sz
  seq_fmt   Format of sequence file (fastq, bam or scarf). Default is
            fastq if the input file has a '.fastq' extension; scarf
            if it has a '.sequence.txt' extension.
  qual_type Type of read quality scores (sanger, illumina or solexa).
            Default is sanger for fastq, illumina for scarf.
Environment variables:
  show_only  1 = only show what would be done (default not set)
  aln_args   other bowtie2 options (e.g. '-T 20' for mem, '-l 20' for aln)
  no_markdup 1 = don't mark duplicates (default 0, mark duplicates)
  run_fastqc 1 = run fastqc (default 0, don't run). Note that output
             will be in the directory containing the fastq files.
  keep       1 = keep unsorted BAM (default 0, don't keep)
  bwa_bin    BWA binary to use. Default bwa 0.7.x. Note that bwa 0.6.2
             or earlier should be used for scarf and other short reads.
  also: NUM_THREADS, BAM_SORT_MEM, SORT_THREADS, JAVA_MEM_ARG

Examples:
  align_bwa_illumina.sh local  ABC_L001_R1.fastq.gz my_abc hg38 1
  align_bwa_illumina.sh global ABC_L001_R1.fastq.gz my_abc hg38 1 50
  align_bwa_illumina.sh global sequence.txt old sacCer3 0 '' '' scarf solexa

There are lots of bells and whistles in the arguments, but the most important are the first few:

1. aln_mode – whether to perform a global or local alignment (the 1st argument must be one of those words)
   
   • global mode uses the bwa aln workflow as we did above
   • local mode uses the bwa mem command
2. in_file – full or relative path to the FASTQ file (just the R1 fastq if paired end). Can be compressed (.gz)
3. out_pfx – prefix for all the output files produced by the script. Should relate back to what the data is.
4. assembly – genome assembly to use.
   • there are pre-built indexes for some common eukaryotes (hg38, hg19, mm10, mm9, danRer7, sacCer3) that you can use
   • or provide a full path for a bwa reference index you have built somewhere
5. paired flag – 0 means single end (the default); 1 means paired end
6. trim_sz – if you want the FASTQ hard trimmed down to a specific length before alignment, supply that number here

We're going to run this script and a similar Bowtie2 alignment script, on the yeast data using the TACC batch system. In a new directory, copy over the commands and submit the batch job. We ask for 2 hours (-t 02:00:00) with 4 tasks/node (-w 4); since we have 4 commands, this will run on 1 compute node.

# Copy over the Yeast data if needed
mkdir -p $SCRATCH/core_ngs/alignment/fastq
cp $CORENGS/alignment/Sample_Yeast*.gz $SCRATCH/core_ngs/alignment/fastq/

# Make sure you're not in an idev session by looking at the hostname
hostname
# If the hostname looks like "c455-004.stampede2.tacc.utexas.edu", exit the idev session

# Make a new alignment directory for running these scripts
mkdir -p $SCRATCH/core_ngs/alignment/bwa_script
cd $SCRATCH/core_ngs/alignment/bwa_script
ln -s -f ../fastq

# Copy the alignment commands file and submit the batch job
cp $CORENGS/tacc/aln_script.cmds .
launcher_creator.py -j aln_script.cmds -n aln_script -t 02:00:00 -w 4 -a UT-2015-05-18 -q normal
sbatch --reservation=BIO_DATA_week_1 aln_script.slurm 
showq -u

While we're waiting for the job to complete, lets look at the aln_script.cmds file.

/work2/projects/BioITeam/common/script/align_bwa_illumina.sh     global ./fastq/Sample_Yeast_L005_R1.cat.fastq.gz bwa_global sacCer3 1 50
/work2/projects/BioITeam/common/script/align_bwa_illumina.sh     local  ./fastq/Sample_Yeast_L005_R1.cat.fastq.gz bwa_local  sacCer3 1
/work2/projects/BioITeam/common/script/align_bowtie2_illumina.sh global ./fastq/Sample_Yeast_L005_R1.cat.fastq.gz bt2_global sacCer3 1 50
/work2/projects/BioITeam/common/script/align_bowtie2_illumina.sh local  ./fastq/Sample_Yeast_L005_R1.cat.fastq.gz bt2_local  sacCer3 1

Notes:

• The 1st command performs a paired-end BWA global alignment (similar to above), but asks that the 100 bp reads be trimmed to 50 first.
  
  • we refer to the pre-built index for yeast by name: sacCer3
    • this index is located in the /work/projects/BioITeam/ref_genome/bwa/bwtsw/sacCer3/ directory
  • we provide the name of the R1 FASTQ file
    • because we request a PE alignment (the 1 argument) the script will look for a similarly-named R2 file.
  • all output files associated with this command will be named with the prefix bwa_global.
• The 2nd command performs a paired-end BWA local alignment.
  
  • all output files associated with this command will be named with the prefix bwa_local.
  • no trimming is requested because the local alignment should ignore 5' and 3' bases that don't match the reference genome
• The 3rd command performs a paired-end Bowtie2 global alignment.
  
  • the Bowtie2 alignment script has the same first arguments as the BWA alignment script.
  • all output files associated with this command will be named with the prefix bt2_global.
  • again, we specify that reads should first be trimmed to 50 bp.
• The 4th command performs a paired-end Bowtie2 local alignment.
  
  • all output files associated with this command will be named with the prefix bt2_local.
  • again, no trimming is requested for the local alignment.

Output files

This alignment pipeline script performs the following steps:

• Hard trims FASTQ, if optionally specified (fastx_trimmer)
  
• Performs the global or local alignment (here, a PE alignment)
  
  • BWA global: bwa aln the R1 and R2 separately, then bwa sampe to produce a SAM file
    
  • BWA local: call bwa mem with both R1 and R2 to produce a SAM file
    
  • Bowtie2 global: call bowtie2 --global with both R1 and R2 to produce a SAM file
    
  • Bowtie2 local: call bowtie2 --local with both R1 and R2 to produce a SAM file
• Converts SAM to BAM (samtools view)
• Sorts the BAM (samtools sort)
• Marks duplicates (Picard MarkDuplicates)
• Indexes the sorted, duplicate-marked BAM (samtools index)
• Gathers statistics (samtools idxstats, samtools flagstat, plus a custom statistics script of Anna's)
• Removes intermediate files
  

There are a number of output files, with the most important being those desribed below.

1. <prefix>.align.log – Log file of the entire alignment process.
   • check the tail of this file to make sure the alignment was successful
2. <prefix>.sort.dup.bam – Sorted, duplicate-marked alignment file.
3. <prefix>.sort.dup.bam.bai – Index for the sorted, duplicate-marked alignment file
4. <prefix>.flagstat.txt – samtools flagstat output
   
5. <prefix>.idxstats.txt – samtools idxstats output
6. <prefix>.samstats.txt – Summary alignment statistics from Anna's stats script
7. <prefix>.iszinfo.txt – Insert size statistics (for paired-end alignments) from Anna's stats script

Verifying alignment success

The alignment log will have a  "I ran successfully" message at the end if all went well, and if there was an error, the important information should also be at the end of the log file. So you can use tail to check the status of an alignment. For example:

tail bwa_global.align.log

This will show something like:

..Done alignmentUtils.pl bamstats - 2020-06-14 23:19:38
.. samstats file 'bwa_global.samstats.txt' exists and is not empty - 2020-06-14 23:19:38
===============================================================================
## Cleaning up files (keep 0) - 2020-06-14 23:19:38
===============================================================================
ckRes 0 cleanup
===============================================================================
## All bwa alignment tasks completed successfully! - 2020-06-14 23:19:38
===============================================================================

Notice that success message:  "All bwa alignment tasks completed successfully!". It should only appear once in any successful alignment log.

When multiple alignment commands are run in parallel it is important to check them all, and you can use grep looking for part of the unique success message to do this. For example:

grep 'completed successfully!' *align.log | wc -l

If this command returns 4 (the number of alignment tasks we performed), all went well, and we're done.

But what if something went wrong? How can we tell which alignment task was not successful? You could tail the log files one by one to see which one(s) don't have the message, but you can also use a special grep option to do this work.

grep -L 'completed successfully' *.align.log

The -L option tells grep to only print the filenames that don't contain the pattern. Perfect! To see happens in the case of failure, try it on a file that doesn't contain that message:

grep -L 'completed successfully' aln_script.cmds

Checking alignment statistics

The <prefix>.samstats.txt statistics files produced by the alignment pipeline has a lot of good information in one place. If you look at bwa_global.samstats.txt you'll see something like this:

-----------------------------------------------
             Aligner:       bwa
     Total sequences:   1184360
        Total mapped:    539079 (45.5 %)
      Total unmapped:    645281 (54.5 %)
             Primary:    539079 (100.0 %)
           Secondary:
          Duplicates:    249655 (46.3 %)
          Fwd strand:    267978 (49.7 %)
          Rev strand:    271101 (50.3 %)
          Unique hit:    503629 (93.4 %)
           Multi hit:     35450 (6.6 %)
           Soft clip:
           All match:    531746 (98.6 %)
              Indels:      7333 (1.4 %)
             Spliced:
-----------------------------------------------
       Total PE seqs:   1184360
      PE seqs mapped:    539079 (45.5 %)
        Num PE pairs:    592180
   F5 1st end mapped:    372121 (62.8 %)
   F3 2nd end mapped:    166958 (28.2 %)
     PE pairs mapped:     80975 (13.7 %)
     PE proper pairs:     16817 (2.8 %)
-----------------------------------------------

Since this was a paired end alignment there is paired-end specific information reported.

You can also view statistics on insert sizes for properly paired reads in the bwa_global.iszinfo.txt file. This tells you the average (mean) insert size, standard deviation, mode (most common value), and fivenum values (minimum, 1st quartile, median, 3rd quartile, maximum).

  Insert size stats for: bwa_global
        Number of pairs: 16807 (proper)
 Number of insert sizes: 406
        Mean [-/+ 1 SD]: 296 [176 416]  (sd 120)
         Mode [Fivenum]: 228  [51 224 232 241 500]

A quick way to check alignment stats if you have run multiple alignments is again to use grep. For example:

grep 'Total mapped' *samstats.txt

will produce output like this:

bt2_global.samstats.txt:        Total mapped:    602893 (50.9 %)
bt2_local.samstats.txt:        Total mapped:    788069 (66.5 %)
bwa_global.samstats.txt:        Total mapped:    539079 (45.5 %)
bwa_local.samstats.txt:        Total mapped:   1008000 (76.5 %

Exercise: How would you list the median insert size for all the alignments?

grep 'Mode' *.iszinfo.txt | awk '{print $1,"Median insert size:",$7}'

TACC batch system considerations

The great thing about pipeline scripts like this is that you can perform alignments on many datasets in parallel at TACC, and they are written to take advantage of having multiple cores on TACC nodes where possible.

On the stampede2, with its 68 physical cores per node, they are designed to run best with no more than 4 tasks per node.

# 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 standard input to 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.