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After raw sequence files are generated (in FASTQ format), quality-checked, and pre-processed in some way, the next step in most 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. In this section, we will primarily focus on two of the most versatile mappers: 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 tools.

Connect to

This should be second nature by now (smile)

Sample datasets

First stage the sample datasets and references we will use.

"Permanent" location for original data
mkdir -p $SCRATCH/core_ngs/alignment/fastq
cp $CORENGS/alignment/*fastq.gz $SCRATCH/core_ngs/alignment/fastq/

mkdir -p $SCRATCH/core_ngs/references
cp $CORENGS/references/* $SCRATCH/core_ngs/references/

These are descriptions of the files we copied:

File NameDescriptionSample Illumina, First of pair, FASTQYeast ChIP-seq Illumina, Second of pair, FASTQYeast ChIP-seq
human_rnaseq.fastq.gzPaired-end Illumina, First of pair only, FASTQHuman RNA-seq
human_mirnaseq.fastq.gzSingle-end Illumina, FASTQHuman microRNA-seq
cholera_rnaseq.fastq.gzSingle-end Illumina, FASTQV. cholerae RNA-seq

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

ReferenceSpeciesBase LengthContig NumberSourceDownload
hg19Human3.1 Gbp25 (really 93)UCSCUCSC GoldenPath
sacCer3Yeast12.2 Mbp17UCSCUCSC GoldenPath
mirbase v20Human subset160 Kbp1908miRBasemiRBase Downloads
vibCho (O395)Vibrio cholerae~4 Mbp2GenBankGenBank Downloads

Searching genomes is computationally hard work and takes a long time if done on un-indexed, 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 chromosome (or contig) 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.

hg19 is way too big for us to index here, so we're not going to do it (especially not all at the same time!). Instead, we will use an existing set of BWA hg19 index files which the BioITeam maintains located at:

BWA hg19 index location

We can index the references for the yeast genome, the human miRNAs, and the V. cholerae genome, because they are all tiny compared to the human genome, so we'll grab the FASTA files for yeast and human miRNAs references and build each index right before we use them. We will also obtain the special GenBank file that contains both the V. cholerae genome sequence and annotations (a .gbk file). These files, which you staged above, are:

Yeast and mirbase FASTA locations

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 idxstat)

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 the next 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 using the module system. Go ahead and 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).

module load bwa
 Top-level BWA help
Program: bwa (alignment via Burrows-Wheeler transformation)
Version: 0.7.12-r1039
Contact: Heng Li <>
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 most of the tasks we are interested in.

Building the BWA sacCer3 index

We're going to index the genome with the index command. To learn what this sub-command needs in the way of options and arguments, enter bwa index with no arguments.

Usage:   bwa index [-a bwtsw|is] [-c] <in.fasta>
Options: -a STR    BWT construction algorithm: bwtsw or is [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. Please choose `-a'
         according to the length of the genome.

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.

Importantly, the output of this command is a group of files that are all required together as the index. So, within the references directory, we will create another directory called bwa/sacCer3, make a symbolic link to the yeast FASTA there, and run the index command in that directory.  So, first we will create a directory called fasta to hold our reference FASTA file, then create the bwa/sacCer3 directory, and construct the symbolic links.

Prepare BWA reference directory for sacCer3
mkdir -p $WORK/core_ngs/references/bwa/sacCer3
mkdir -p $WORK/core_ngs/references/fasta
mv $WORK/core_ngs/references/sacCer3.fa $WORK/core_ngs/references/fasta
cd $WORK/core_ngs/references/bwa/sacCer3
ln -s ../../fasta/sacCer3.fa
ls -la

Now execute the bwa index command.

Build BWA index for sacCer3
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:

BWA index files for sacCer3

Exploring the FASTA with grep

It is frequently useful to have a list of all contigs/chromosomes/genes/features in a file. You'll usually want to know this before you start the 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 as "chr1", "1", "I", and more in different references, and it can get weird for non-model organisms.

We saw that a FASTA consists of a number of contig entries, each one starting with a name line of the form below, followed by many lines of bases.


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.

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 a few different flavors of them). But the most common is the Perl style ( 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 (there's a reason Perl was used a lot when assembling the human genome).

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

grep to match contig names in a FASTA file
grep -P '^>' sacCer3.fa | more


  • The -P option tells grep to use 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 really 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 our pattern, '^>'

First, the single quotes around the pattern – they are only a signal for the bash shell. As part of its friendly command line parsing and evaluation, the shell will often look for special characters 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. (Aside: We've see that the shell does look inside double quotes ( " ) for certain special signals, such as looking for environment variable names to evaluate.)

So what does ^> mean to grep? Well, from our contig name format description above we see 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 argument. But for grep, the more specific the pattern, the better. So we constrain where the > can appear on the line. The special carat ( ^ ) character represents "beginning of line". So ^> means "beginning of a line followed by a > character, followed by anything. (Aside: the dollar sign ( $ ) character represents "end of line" in a regex. There are many other special characters, including period ( . ), question mark ( ? ), pipe ( | ), parentheses ( ( ) ), and brackets ( [ ] ), to name the most common.)

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

grep -P '^>' sacCer3.fa | wc -l

Or use grep's -c option that says "just count the line matches"

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


There are 17 contigs.

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 go to the align directory, and link to the sacCer3 reference directory (this will make our commands more readable).

Prepare to align yeast data
cd $SCRATCH/core_ngs/alignment
ln -s $WORK/core_ngs/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. Since these alignments are completely independent, we can execute them in parallel in a batch job.

What does bwa aln needs in the way of arguments?

bwa aln

There are lots of options, but here is a summary of the most important ones. BWA, is a lot more complex than the options let on. If you look at the BWA manual on the web for the aln sub-command, you'll see numerous options that can increase the alignment rate (as well as decrease it), and all sorts of other things. 

-lControls the length of the seed (default = 32)
-kControls the number of mismatches allowable in the seed of each alignment (default = 2)
-nControls the number of mismatches (or fraction of bases in a given alignment that can be mismatches) in the entire alignment (including the seed) (default = 0.04)
-tControls the number of threads

The rest of the 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 accessed at the BWA manual page.

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

Also 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. (We can do this since we're on the special login8 head node!) Each command you execute should only take a minute or so.

bwa aln commands for yeast R1 and R2
bwa aln sacCer3/sacCer3.fa fastq/ > yeast_R1.sai
bwa aln sacCer3/sacCer3.fa fastq/ > yeast_R2.sai

When all is done you should have two .sai files.

 How you would use the batch system for these commands

Here's how you would use the TACC batch system for these commands.

For a simple alignment like this, we can just go with the default alignment parameters, with one exception. At TACC, we want to optimize our alignment speed by allocating more than one thread (-t) to the alignment. We want to run 2 tasks, and will use a minimum of one 16-core node. So we can assign 8 cores to each alignment by specifying -t 8.

Create an aln.cmds file (using nano) with the following lines. Here we redirect standard error to a log file, one per file.

bwa aln commands for yeast R1 and R2
bwa aln -t 8 sacCer3/sacCer3.fa fastq/ > yeast_R1.sai 2> aln.yeast_R1.log
bwa aln -t 8 sacCer3/sacCer3.fa fastq/ > yeast_R2.sai 2> aln.yeast_R2.log

Create the batch submission script specifying a wayness of 2 (2 tasks per node) on the normal queue and a time of 10 minutes, then submit the job and monitor the queue:

Submit BWA alignment job -n aln -j aln.cmds -t 00:10:00 -q normal -w 2
sbatch aln.slurm
showq -u

Since you have directed standard error to log files, you can use a neat trick to monitor the progress of the alignment: tail -f. The -f means "follow" the tail, so new lines at the end of the file are displayed as they are added to the file.

Monitor alignment progress with tail -f
# Use Ctrl-c to stop the output any time
tail -f aln.yeast_R1.log

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.

BWA global alignment of R1 reads
bwa sampe sacCer3/sacCer3.fa yeast_R1.sai yeast_R2.sai fastq/ fastq/ > yeast_pairedend.sam

You did it!  You should now have a 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 once you create a BAM file.

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

The SAM or BAM has a number of header lines, which all start with an at sign ( @ ). The @SQ lines describe each contig and its length. There is also a @PG  line that describes the way the bwa sampe was performed.

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


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 the header lines starting with @.

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

There are 1,184,360 alignment records.

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


gunzip -c fastq/ | echo $((`wc -l` / 2))

There were a total of 1,184,360 original sequences


  • Do both R1 and R2 reads have separate alignment records?
  • Does the SAM file contain both aligned and un-aligned reads?
  • What is the order of the alignment records in this SAM file?
  • Do both R1 and R2 reads have separate alignment records?
    • yes, they must, because there were 1,184,360 R1+R2 reads and an equal number of alignment records
  • Does the SAM file contain both aligned and un-aligned reads?
    • yes, it must, because there were 1,184,360 R1+R2 reads and an equal number of alignment records
  • What is the order of the alignment records in this SAM file?
    • the names occur in the exact same order as they did in the FASTQ, except that they come in pairs
      • the R1 read comes first, then its corresponding R2
    • this ordering is called read name ordering

Using cut to isolate fields

Recall the format of a SAM/BAM file alignment record:

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.

Cut syntax for a single field
tail yeast_pairedend.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, of course, 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, and all fields starting with 12 (SAM tag fields).

Cut syntax for multiple fields
tail yeast_pairedend.sam | cut -f 2-6,9,12-

You may have noticed that some alignment records contain contig names (e.g. chrV) in field 3 while others contain an asterisk ( * ). Usually the * means the record didn't align. (This isn't always true – later you'll see how to properly distinguish between mapped and unmapped reads using samtools.) We're going to use this heuristic along with cut to see about how many records represent aligned sequences.

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:

Grep pattern that doesn't match header
# the ^HWI pattern matches lines starting with HWI (the start of all read names in column 1)
grep -P '^HWI' yeast_pairedend.sam | head

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

Get contig name info with cut
grep -P '^HWI' 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:

Filter contig name of * (unaligned)
grep -P '^HWI' yeast_pairedend.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).

Count aligned SAM records
grep -P '^HWI' yeast_pairedend.sam | cut -f 3 | grep -v '*' | wc -l

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


The expression above returns 612,968. There were 1,184,360 records total, so the percentage is:

Calculate alignment rate
echo $((612968 * 100/ 1184360))

or about 51%. Not great.

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

Recall that these are 100 bp reads and we did not remove adapter contamination. There will be a distribution of fragment sizes – some will be short – and those short fragments may not align without adapter removal (fastx_trimmer or cutadapt).

Exercise #5: Simple SAMtools Utilities

We have used several alignment methods that all generate results in the form of the near-universal SAM/BAM file format.  The SAMtools program is a ubiquitously used set of tools that allow a user to manipulate SAM/BAM files in many different ways, ranging from simple tasks (like SAM/BAM interconversion) to more complex functions (like removal of PCR duplicates).  It is available in the TACC module system in the typical fashion.

In this exercise, we will use five very simple 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 section tomorrow we will explore samtools in more in depth.

For the sake of time and simplicity, here we are only going to run these commands on the yeast paired-end alignment file. The same commands can be run on the other files by changing the names, so feel free to try them on other SAM files. Indeed, it is very common in practice to use bash loops to generate many commands for a large set of alignments and deposit those commands into a batch job cmds file for submission.

To start, we will move to the directory containing our SAM files, among other things, and load up samtools using the module system. After loading it, just run the samtools command to see what the available tools are (and to see what the syntax of an actual command is).

cd $SCRATCH/core_ngs/alignment
ls -la
module load samtools

You will see the following screen after running samtools with no other options:

Program: samtools (Tools for alignments in the SAM format)
Version: 1.2 (using htslib 1.2.1)
Usage:   samtools <command> [options]
  -- indexing
         faidx       index/extract FASTA
         index       index alignment
  -- editing
         calmd       recalculate MD/NM tags and '=' bases
         fixmate     fix mate information
         reheader    replace BAM header
         rmdup       remove PCR duplicates
         targetcut   cut fosmid regions (for fosmid pool only)
  -- file operations
         bamshuf     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
         bam2fq      converts a BAM to a FASTQ
  -- stats
         bedcov      read depth per BED region
         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

SAMtools version differences

Be sure to check what version of samtools you are using!

The most recent edition of SAMtools is 1.2, which has some important differences from the last version, 0.1.19.  Most commands for this section are the same between the two versions, but if you see code from other sources using samtools, the version differences may be important.

Samtools view

The utility samtools view provides a way of converting SAM (text format) files to BAM (binary, compressed) files directly. 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]
         -T FILE  reference sequence FASTA FILE [null]
         -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]
         -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 bits set in INT set in FLAG [0]
         -F INT   only include reads with none of the bits set in INT
                  set in FLAG [0]
         -x STR   read tag to strip (repeatable) [null]
         -B       collapse the backward CIGAR operation
         -s FLOAT integer part sets seed of random number generator [0];
                  rest sets fraction of templates to subsample [no subsampling]
         -@ INT   number of BAM compression threads [0]
         -?       print long help, including note about region specification
         -S       ignored (input format is auto-detected)

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 say that we're inputing a SAM instead of a BAM. We just need to tell the tool to output the file in BAM format, and provide the name of the destination BAM file. This command is as follows:

samtools view -b yeast_pairedend.sam -o yeast_pairedend.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

How do you look at the BAM file contents now? That's simple. Just use samtools view. without the -b option. Remember to pipe output to a pager!

samtools view yeast_pairedend.bam | more

Samtools sort

Look at the SAM file briefly using less. You will notice, if you scroll down, that the alignments are in no particular order, with chromosomes and start positions all mixed up. This makes searching through the file very inefficient. samtools sort is a piece of samtools that provides the ability to re-order entries in the SAM file either by 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]
  -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
  -o FILE    Write final output to FILE rather than standard output
  -O FORMAT  Write output as FORMAT ('sam'/'bam'/'cram')   (either -O or
  -T PREFIX  Write temporary files to PREFIX.nnnn.bam       -T is required)
  -@ INT     Set number of sorting and compression threads [1]
Legacy usage: samtools sort [options...] <in.bam> <out.prefix>
  -f         Use <out.prefix> as full final filename rather than prefix
  -o         Write final output to stdout rather than <out.prefix>.bam
  -l,m,n,@   Similar to corresponding options above

In most cases you will be sorting a BAM file by position rather than by name. You can use either -o or reidrection with > to control the output.

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

samtools sort -O bam -T yeast_pairedend.sort yeast_pairedend.bam > yeast_pairedend.sort.bam
  • The -O options says the output format should be BAM
  • The -T options gives a prefix for temporary files produced during sorting
  • By default sort writes its output to standard output, so we use > to redirect to a file named yeast_pairedend.sort.bam

Samtools index

Many tools (like the UCSC Genome Browser) only need to use sub-sections of the BAM file at a given point in time. For example, if you are viewing alignments that are within a particular gene alignments on other chromosomes generally do not need to be loaded. In order to speed up access, BAM files are indexed, producing BAI files which allow other programs to navigate directly to the alignments of interest. This is especially important when you have many alignments.

The utility samtools index creates an index that has the exact name as the input BAM file, with suffix .bai appended. The help page, if you execute samtools index with no arguments, is as follows:

Usage: samtools index [-bc] [-m INT] <in.bam> [out.index]
  -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]

Here, the syntax is way, way easier. We want a BAI-format index which is the default. (CSI-format is used with extremely long contigs, which we aren't considering here - the most common use case are highly polyploid plant genomes).

So all we have to type is:

samtools index yeast_pairedend.sort.bam

This will produce a file named yeast_pairedend.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.

Samtools idxstats

Now that we have a sorted, indexed BAM file, we might like to get some simple statistics about the alignment run. For example, we might like to know how many reads aligned to each chromosome/contig. The samtools idxstats is a very simple tool that provides this information. If you type the command without any arguments, you will see that it could not be simpler - just type the following command:

samtools idxstats yeast_pairedend.sort.bam

The output is a text file with four tab-delimited columns with the following meanings:

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

The reason that the "unmapped reads" field for the named chromosomes is not zero is that, if one half of a pair of reads aligns while the other half does not, the unmapped read is still assigned to the chromosome its mate mapped to, but is flagged as unmapped.

If you're mapping to a non-genomic reference such as miRBase miRNAs or another set of genes (a transcriptome), samtools idxstats gives you a quick look at quantitative alignment results.

Samtools flagstat

Finally, we might like to obtain some other statistics, such as the percent of all reads that aligned to the genome. The samtools flagstat tool provides very simple analysis of the SAM flag fields, which includes information like whether reads are properly paired, aligned or not, and a few other things. Its syntax is identical to that of samtools idxstats:

samtools flagstat yeast_pairedend.sort.bam

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%:-nan%)
1184360 + 0 paired in sequencing
592180 + 0 read1
592180 + 0 read2
473114 + 0 properly paired (39.95%:-nan%)
482360 + 0 with itself and mate mapped
65304 + 0 singletons (5.51%:-nan%)
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 necessary.

The most important statistic is the mapping rate, but this readout 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 #6: Yeast BWA PE alignment with Anna's script

Now that you've done everything the hard way, let's see how to do run an alignment pipeline using Anna's script.

First the setup:

mkdir -p $SCRATCH/core_ngs/align2/fastq
cd $SCRATCH/core_ngs/align2/fastq
cp /corral-repl/utexas/BioITeam/core_ngs_tools/alignment/*fastq.gz .

Before executing the script you need to have one environment variable set. We'll do it at the command line here, but you could put it in your .bashrc file.

export path_code=/work/01063/abattenh/code

Now change into the directory and call the script with no arguments to see usage

cd $SCRATCH/core_ngs/align2

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

  1. FASTQ file – full or relative path to the FASTQ file (just the R1 fastq if paired end). Can be compressed (.gz)
  2. output prefix – prefix for all the output files produced by the script. Should relate back to what the data is.
  3. assembly – genome assembly to use.
    • there are pre-built indexes for some common eukaryotes (hg19, hg18, mm10, mm9, danRer7, sacCer3) that you can use
    • or provide a full path for a bwa reference index you have built somewhere
  4. paired flag – 0 means single end (the default); 1 means paired end
  5. hard trim length – if you want the FASTQ hard trimmed down to a specific length, supply that number here

Now run the pipeline. By piping the output to tee <filename> we can see the script's progress at the terminal, and it also is written to <filename>.

$path_code/script/align/ ./fastq/ yeast_chip sacCer3 1 2>&1 | tee aln.yeast_chip.log

Output files

This alignment pipeline script performs the following steps:

  • Hard trims FASTQ, if optionally specified (fastx_trimmer)
  • Aligns the R1 FASTQ (bwa aln)
  • Aligns the R2 FASTQ, if paired end alignment specified (bwa aln)
  • Reports the alignments as SAM (bwa samse for single end, or bwa sampe for paired end)
  • 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. aln.<prefix>.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>.samstats.txt – Summary alignment statistics 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:

Checking the alignment log file
tail aln.yeast_chip.log

This will show something like:

..samstats file 'yeast_chip.samstats.txt' exists Thu May 28 16:36:01 CDT 2015
..samstats file file 'yeast_chip.samstats.txt' size ok Thu May 28 16:36:01 CDT 2015
Cleaning up files...
ckRes 0 cleanup
All bwa alignment tasks completed successfully!
Thu May 28 16:36:01 CDT 2015

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, suppose I have run 6 alignments and have these 6 log files:

aln.delswr1_htz1_tap1t0.log   aln.delswr1_htz1_tap1t30.log  aln.wt_htz1_tap1t15.log
aln.delswr1_htz1_tap1t15.log  aln.wt_htz1_tap1t0.log        aln.wt_htz1_tap1t30.log

I can check that all 6 completed with this command:

Count the number of successful alignments
grep 'completed successfully' aln.*.log | wc -l

If this command returns 6, I'm done. But what if it doesn't? If you grep -v (lines that don't contain the pattern), you'll get every line in every log file except the success message line, which is not what you want at all.

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:

Count the number of successful alignments
grep -L 'completed successfully' aln.*.log

The -L option tells grep to only print the filenames that don't contain the pattern. Perfect!

Checking alignment statistics

The <prefix>.samstats.txt statistics file produced by the alignment pipeline has a lot of good information in one place. If you use cat or more to view it you'll see this:

             Aligner:       bwa
     Total sequences:   1184360
        Total mapped:    547664 (46.2 %)
      Total unmapped:    636696 (53.8 %)
             Primary:    547664 (100.0 %)
          Duplicates:    324280 (59.2 %)
          Fwd strand:    272898 (49.8 %)
          Rev strand:    274766 (50.2 %)
           Multi hit:     18688 (3.4 %)
           Soft clip:    222451 (40.6 %)
           All match:    319429 (58.3 %)
              Indels:      6697 (1.2 %)
       Total PE seqs:   1184360
      PE seqs mapped:    547664 (46.2 %)
        Num PE pairs:    592180
   F5 1st end mapped:    300477 (50.7 %)
   F3 2nd end mapped:    247187 (41.7 %)
     PE pairs mapped:    241180 (40.7 %)
     PE proper pairs:    236557 (39.9 %)
  Insert size stats for: yeast_chip
        Number of pairs: 236557 (proper)
 Number of insert sizes: 212
        Mean [-/+ 1 SD]: 215 [153 277]  (sd 62)
         Mode [Fivenum]: 223  [105 210 220 229 321]

Since this was a paired end alignment there is paired-end specific information reported, including insert size statistics: mean/standard deviation, mode (most common insert size value) and fivenum (min, q1, median, q3 max insert sizes).

A quick way to check alignment stats if you have run multiple alignments is again to use grep. For example, for the 6 alignment files shown earlier, running this:

Review multiple alignment rates
grep 'Total map' *samstats.txt

will produce output like this:

delswr1_htz1_tap1t0.samstats.txt:        Total mapped:  32761761 (86.8 %)
delswr1_htz1_tap1t15.samstats.txt:        Total mapped:  33699464 (89.2 %)
delswr1_htz1_tap1t30.samstats.txt:        Total mapped:  28441655 (87.6 %)
wt_htz1_tap1t0.samstats.txt:        Total mapped:  28454847 (89.5 %)
wt_htz1_tap1t15.samstats.txt:        Total mapped:  33245627 (90.9 %)
wt_htz1_tap1t30.samstats.txt:        Total mapped:  32567026 (90.7 %)

 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.

Anna's alignment pipeline scripts are written to take advantage of having multiple cores on TACC nodes, and are thus designed to run with at most two pipeline commands per TACC node.

Always specify wayness 2 for these pipeline scripts

These pipeline scripts should always be run with a wayness of 2 (-w 2) in the TACC batch system, meaning two commands per node.

Assuming you have your alignment commands in a file called aln.cmds, here's how to create and submit a batch job for the commands.

Submit BWA alignment pipeline job -n aln -j aln.cmds -t 12:00:00 -q normal -w 2
sbatch aln.slurm
showq -u

Note the maximum run time specified here is 12 hours (-t 12:00:00). This is a reasonable value for a higher eukaryote with 20-40 M reads, and is way more than a yeast alignment would need (~ 4 hours). For very deeply sequenced eukaryotes (e.g. human genome re-sequencing with hundresd of millions of reads), you may want to specify the maximum job time of 48 hours.

Exercise: What would alignment commands look like if you were putting it in a batch system .cmds file?


Assuming you have $path_code set properly before submitting the job, the batch command would look like the command above, but you don't need the tee pipe. Instead, just redirect all output to a file. The example below shows how you would run alignments on two yeast samples in a batch file, adjusting the output prefix (yeast1, yeast2) and log file (aln.yeast1.log, aln.yeast2.log) accordingly.

$path_code/script/align/ ./fastq/ yeast1 sacCer3 1 2>&1 > aln.yeast1.log
$path_code/script/align/ ./fastq/ yeast2 sacCer3 1 2>&1 > aln.yeast2.log


Exercise #2: Bowtie2 global alignment - Vibrio cholerae RNA-seq

While we have focused on aligning eukaryotic data, the same tools can be used to perform identical functions with prokaryotic data.  The major differences are less about the underlying data and much more about the external/public databases established to store and distribute reference data.  For example, the Illumina iGenome resource provides pre-processed and uniform reference data, designed to be out-of-the-box compatible with aligners like bowtie2 and bwa.  However, the limited number of available species are heavily biased towards model eukaryotes. If we wanted to study a prokaryote, the reference data must be downloaded from a resource like GenBank, and processed/indexed similarly to the procedure for mirbase.  

While the alignment procedure for prokaryotes is broadly analogous, the reference preparation process is somewhat different, and will involve use of a biologically-oriented scripting library called BioPerl.  In this exercise, we will use some RNA-seq data from Vibrio cholerae, published last year on GEO here, and align it to a reference genome.

Overview of Vibrio cholerae alignment workflow with Bowtie2

Alignment of this prokaryotic data follows the workflow below. Here we will concentrate on steps 1 and 2.

  1. Prepare the vibCho reference index for bowtie2 from a GenBank record using BioPerl
  2. Align reads using bowtie2, producing a SAM file
  3. Convert the SAM file to a BAM file (samtools view) 
  4. Sort the BAM file by genomic location (samtools sort)
  5. Index the BAM file (samtools index)
  6. Gather simple alignment statistics (samtools flagstat and samtools idxstat)

Obtaining the GenBank record(s)

V. cholerae has two chromosomes. We download each separately.

  1. Navigate to
    • click on the Send down arrow (top right of page)
    • select Complete Record
    • select Clipboard as Destination
    • click Add to Clipboard
  2. Perform these steps in your Terminal window
  3. Repeat steps 1 and 2 fot the 2nd chromosome
  4. Combine the 2 files into one using cat
    • cat NC_012582  NC_012583 > vibCho.gbk

Converting GenBank records into sequence (FASTA) and annotation (GFF) files

As noted earlier, many microbial genomes are available through repositories like GenBank that use specific file format conventions for storage and distribution of genome sequence and annotations. The GenBank file format is a text file that can be parsed to yield other files that are compatible with the pipelines we have been implementing.

Go ahead and look at some of the contents of a GenBank file with the following commands (execute these one at a time):

cd $WORK/core_ngs/references
less vibCho.O395.gbk # use q to quit less
grep -A 5 ORIGIN vibCho.O395.gbk

As the less command shows, the file begins with a description of the organism and some source information, and the contains annotations for each bacterial gene. The grep command shows that, indeed, there is sequence information here (flagged by the word ORIGIN) that could be exported into a FASTA file. There are a couple ways of extracting the information we want, namely the reference genome and the gene annotation information, but a convenient one (that is available through the module system at TACC) is BioPerl.

We load BioPerl like we have loaded other modules, with the caveat that we must load regular Perl before loading BioPerl:

module load perl
module load bioperl

These commands make several scripts directly available to you. The one we will use is called, and it is a BioPerl script used to inter-convert file formats like FASTA, GBK, and others. This script produces two output files:

  • a FASTA format file for indexing and alignment
  • GFF file (standing for General Feature Format) contains information about all genes (or, more generally, features) in the genome
    • remember, annotations such as GFFs must always match the reference you are using

To see how to use the script, just execute:

Clearly, there are many file formats that we can use this script to convert.  In our case, we are moving from genbank to fasta, so the commands we would execute to produce and view the FASTA files would look like this:

cd $WORK/core_ngs/references --from genbank --to fasta < vibCho.O395.gbk > vibCho.O395.fa
mv vibCho.O395.fa fasta/
grep ">" fasta/vibCho.O395.fa
less fasta/vibCho.O395.fa

Now we have a reference sequence file that we can use with the bowtie2 reference builder, and ultimately align sequence data against.

Recall from when we viewed the GenBank file that there are genome annotations available as well that we would like to extract into GFF format.  However, the script is designed to be used to convert sequence formats, not annotation formats. Fortunately, there is another script called that can take a GenBank file and produce a GFF3 (the most recent format convention for GFF files) file. To run it and see the output, run these commands: --format Genbank vibCho.O395.gbk
mv vibCho.O395.gbk.gff vibCho.O395.gff
less vibCho.O395.gff

After the header lines, each feature in the genome is represented by a line that gives chromosome, start, stop, strand, and other information.  Features are things like "mRNA," "CDS," and "EXON."  As you would expect in a prokaryotic genome it is frequently the case that the gene, mRNA, CDS, and exon annotations are identical, meaning they share coordinate information. You could parse these files further using commands like grep  and awk  to extract, say, all exons from the full file or to remove the header lines that begin with #.

Introducing bowtie2

Go ahead and load the bowtie2 module so we can examine some help pages and options. To do that, you must first load the perl module, and then the a specific version of bowtie2

module load perl
module load bowtie/2.2.0

 Now that it's loaded, check out the options. There are a lot of them! In fact for the full range of options and their meaning, Google "Bowtie2 manual" and bring up that page. The Table of Contents is several pages long! Ouch!

This is the key to using bowtie2 - it allows you to control almost everything about its behavior, but that also makes it is much more challenging to use than bwa – and it's easier to screw things up too!

Building the bowtie2 vibCho index

Before the alignment, of course, we've got to build a mirbase index using bowtie2-build (go ahead and check out its options). Unlike for the aligner itself, we only need to worry about a few things here:

  • reference_in file is just the FASTA file containing mirbase v20 sequences
  • bt2_index_base is the prefix of where we want the files to go

To build the reference index for alignment, we actually only need the FASTA file, since annotations are often not necessary for alignment. (This is not always true - extensively spliced transcriptomes requires splice junction annotations to align RNA-seq data properly, but for now we will only use the FASTA file.)

mkdir -p $WORK/core_ngs/references/bt2/vibCho
mv $WORK/core_ngs/references/vibCho.O395.fa $WORK/core_ngs/references/fasta
cd $WORK/core_ngs/references/bt2/vibCho
ln -s -f ../../fasta/vibCho.O395.fa
ls -la

Now build the index using bowtie2-build:

Prepare Bowtie2 index files
bowtie2-build vibCho.O395.fa vibCho.O395

This should also go pretty fast. You can see the resulting files using ls like before.

Performing the bowtie2 alignment

Now we will go back to our scratch area to do the alignment, and set up symbolic links to the index in the work area to simplify the alignment command:

cd $SCRATCH/core_ngs/alignment
ln -s -f $WORK/core_ngs/references/bt2/vibCho vibCho

Note that here the data is from standard mRNA sequencing, meaning that the DNA fragments are typically longer than the reads. There is likely to be very little contamination that would require using a local rather than global alignment, or many other pre-processing steps (e.g. adapter trimming). Thus, we will run bowtie2 with default parameters, omitting options other than the input, output, and reference index.

As you can tell from looking at the bowtie2 help message, the general syntax looks like this:

bowtie2 [options]* -x <bt2-idx> {-1 <m1> -2 <m2> | -U <r>} [-S <sam>]

So our command would look like this:

bowtie2 -x vibCho/vibCho.O395 -U fastq/cholera_rnaseq.fastq.gz -S cholera_rnaseq.sam
 What's going on?

Parameters are:

  • -x  vibCho/vibCho.O395.fa – prefix path of index files
  • -U fastq/cholera_rnaseq.fastq.gz – FASTQ file for single-end (Unpaired) alignment
  • -S cholera_rnaseq.sam – tells bowtie2 to report alignments in SAM format to the specified file

Create a commands file called bt2_vibCho.cmds with this task definition then generate and submit a batch job for it (time 1 hour, development queue).

 What's going on?

Use nano to create the bt2_vibCho.cmds file. Then:

Local bowti2 alignment of miRNA data -n bt2_vibCho -j bt2_vibCho.cmds -t 01:00:00 -A UT-2015-05-18
sbatch bt2_vibCho.slurm; showq -u

When the job is complete you should have a cholera_rnaseq.sam file that you can examine using whatever commands you like.

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