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Overview

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, and with sufficient (but not absolute - and this tradeoff is an important consideration when constructing alignment pipelines) accuracy align each read to its best location, if any, in a reference. Even though many mapping tools exist, a few individual programs have a dominant "market share" of the NGS world. These programs vary widely in their design, inputs, outputs, and applications. 

In this section, we will primarily focus on two of the most versatile mappers: BWA and Bowtie2, the latter being part of the Tuxedo suite (e.g. transcriptome-aware Tophat2) which also includes tools for manipulating NGS data after alignment.

Connect to login8.stampede.tacc.utexas.edu

This should be second nature by now (smile)

Sample Datasets

You have already worked with a paired-end yeast ChIP-seq dataset, which we will continue to use here.  For the sake of uniformity, however, we will set up a new directory in your scratch area called 'alignment' and fill it with our sequencing data.  To set up your scratch area properly and move into it, execute something like:

"Permanent" location for original data
mkdir -p $SCRATCH/core_ngs/alignment
cd $SCRATCH/core_ngs/alignment
mkdir fastq

Now you have created the alignment directory, moved into it, and created a subdirectory for our raw fastq files.  We will be using four data sets that consist of five files (since the paired-end data set has two separate files for each of the R1 and R2 reads).  To copy them over, execute something like:

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

We first moved ourselves into the fastq directory, then copied over all files that end in "fastq.gz" in the corral directory specified in the second line.  These are descriptions of the five files we copied:

File NameDescriptionSample
Sample_Yeast_L005_R1.cat.fastq.gzPaired-end Illumina, First of pair, FASTQYeast ChIP-seq
Sample_Yeast_L005_R2.cat.fastq.gzPaired-end 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 genome to align to.  We will use four different references here: 

  • the yeast genome (sacCer3)
  • the human genome (hg19)
  • a Vibrio cholerae genome (0395; our name: vibCho)
  • the microRNA database mirbase (v20), human subset

NOTE: For the sake of simplicity, these are not necessarily the most recent versions of these references - for example, hg19 is the second most recent human genome, with the most recent called hg38.  Similarly, the most recent mirbase annotation is v21.

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

ReferenceSpeciesBase LengthContig NumberSourceDownload
hg19Human3.1 Gbp25 (really 93)UCSCUCSC GoldenPath
sacCer3Yeast12.2 Mbp17UCSCUCSC GoldenPath
mirbase V20Human160 Kbp1908MirbaseMirbase Downloads
vibCho (O395)V. 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 later retrieval.  The aligners we are using each require a different index, but use the same method (the Burrows-Wheeler Transform) to get the job done. This 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 by the aligner when performing the sequence alignment, and subsequent alignments are reported using coordinates referencing the original FASTA reference files.

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 "point" to an existing set of hg19 index files, which are all located at:

BWA hg19 index location
/scratch/01063/abattenh/ref_genome/bwa/bwtsw/hg19

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. We will grab the FASTA files for yeast and human miRNAs two references and build each index right before we use them. We will also grab the special file that contains the V. cholerae genome sequence and annotations (a .gbk file), and generate the reference FASTA and some other interesting information when we get to that exercise. These references are currently at the following locations:

Yeast and mirbase FASTA locations
/corral-repl/utexas/BioITeam/core_ngs_tools/references/sacCer3.fa
/corral-repl/utexas/BioITeam/core_ngs_tools/references/hairpin_cDNA_hsa.fa
/corral-repl/utexas/BioITeam/core_ngs_tools/references/vibCho.O395.gbk

First stage all the reference files in your $WORK core_ngs area in a directory called references. We will add further structure to this directory later on in specific exercises, but for now the following will suffice:

Stage FASTA references
mkdir -p $WORK/core_ngs/references
cp $CLASSDIR/references/* $WORK/core_ngs/references

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

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 generally 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 course section, since those 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
bwa
 Top-level BWA help
Program: bwa (alignment via Burrows-Wheeler transformation)
Version: 0.7.12-r1039
Contact: Heng Li <lh3@sanger.ac.uk>
Usage:   bwa <command> [options]
Command: index         index sequences in the FASTA format
         mem           BWA-MEM algorithm
         fastmap       identify super-maximal exact matches
         pemerge       merge overlapping paired ends (EXPERIMENTAL)
         aln           gapped/ungapped alignment
         samse         generate alignment (single ended)
         sampe         generate alignment (paired ended)
         bwasw         BWA-SW for long queries

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

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

As you can see, bwa include many sub-commands that perform 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
sacCer3.fa
sacCer3.fa.amb
sacCer3.fa.ann
sacCer3.fa.bwt
sacCer3.fa.pac
sacCer3.fa.sa

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.

>contigName

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 (http://perldoc.perl.org/perlretut.html). 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

Notes:

  • 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?

 Hint
grep -P '^>' sacCer3.fa | wc -l

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

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


 Answer

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
ls 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?

 Hint
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. 

OptionEffect
-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, 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.

Also note that bwa writes its (binary) output to standard output by default, so we need to redirect that to a .sai file. And of course we need to redirect standard error to a log file, one per file.

Create an aln.cmds file (using nano) with the following lines:

bwa aln commands for yeast R1 and R2
bwa aln -t 8 sacCer3/sacCer3.fa fastq/Sample_Yeast_L005_R1.cat.fastq.gz > yeast_R1.sai 2> aln.yeast_R1.log
bwa aln -t 8 sacCer3/sacCer3.fa fastq/Sample_Yeast_L005_R2.cat.fastq.gz > 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 1 hour, then submit the job and monitor the queue:

Submit BWA alignment job
launcher_creator.py -n aln -j aln.cmds -t 01:00:00 -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

When it's done you should see two .sai files. Next we use the bwa sampe command to pair the reads and output SAM format data. For this command you provide the same reference prefix as for bwa aln, along with the two .sai files and the two original FASTQ files.

Again bwa writes its output to standard output, so redirect that to a .sam file. (Note that bwa sampe is "single threaded" – it does not have an option to use more than one processor for its work.) We'll just execute this at the command line – not in a batch job.

BWA global alignment of R1 reads
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 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?

 Answer
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?

 Hint

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



 Answer
There are 1,184,360 alignment records.

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

 Hint

gunzip -c fastq/Sample_Yeast_L005_R1.cat.fastq.gz | echo $((`wc -l` / 2))

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

Exercises:

  • 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?

 

 Answers
  • 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

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 of the last 10 alignments.

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?

 Answer

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?

 Answer
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 #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 GenBank files 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)

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:

cd $WORK/core_ngs/references
less vibCho.O395.gbk
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 bp_seqconvert.pl, 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:

bp_seqconvert.pl

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:

bp_seqconvert.pl --from genbank --to fasta < vibCho.O395.gbk > vibCho.O395.fa
grep ">" vibCho.O395.fa
less 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 bp_seqconvert.pl script is designed to be used to convert sequence formats, not annotation formats. Fortunately, there is another script called bp_genbank2gff3.pl 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:

bp_genbank2gff3.pl --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.fa -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
launcher_creator.py -n bt2_vibCho -j bt2_vibCho.cmds -t 01:00:00
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.

Exercise #3: Bowtie2 local alignment - Human microRNA-seq

Now we're going to switch over to a different aligner that was originally designed for very short reads and is frequently used for RNA-seq data. Accordingly, we have prepared another test microRNA-seq dataset for you to experiment with (not the same one you used cutadapt on). This data is derived from a human H1 embryonic stem cell (H1-hESC) small RNA dataset generated by the ENCODE Consortium – its about a half million reads.

However, there is a problem!  We don't know (or, well, you don't know) what the adapter structure or sequences were. So, you have a bunch of 36 base pair reads, but many of those reads will include extra sequence that can impede alignment – and we don't know where! We need an aligner that can find subsections of the read that do align, and discard (or "soft-clip") the rest away – an aligner with a local alignment mode. Bowtie2 is just such an aligner.

Overview miRNA alignment workflow with bowtie2

If the adapter structure were known, the normal workflow would be to first remove the adapter sequences with cutadapt. Since we can't do that, we will instead perform a local alignment of the single-end miRNA sequences using bowtie2. This workflow has the following steps:

  1. Prepare the mirbase v20 reference index for bowtie2 (one time) using bowtie2-build 
  2. Perform local alignment of the R1 reads with bowtie2, producing a SAM file directly
  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)

This looks so much simpler than bwa – only one alignment step instead of three! We'll see the price for this "simplicity" in a moment...

As before, we will just do the alignment steps leave samtools for the next section.

Mirbase is a collection of all known microRNAs in all species (and many speculative miRNAs). We will use the human subset of that database as our alignment reference.  This has the advantage of being significantly smaller than the human genome, while likely containing almost all sequences likely to be detected in a miRNA sequencing run.

 If it's simpler and faster, would one ever want to align a miRNA dataset to hg19 rather than mirbase? If so, why?
  1. Due to natural variation, sequencing errors, and processing issues, variation between reference sequence and sample sequence is always possible. Alignment to the human genome allows a putative "microRNA" read the opportunity to find a better alignment in a region of the genome that is not an annotated microRNA. If this occurs, we might think that a read represents a microRNA (since it aligned in the mirbase alignment), when it is actually more likely to have come from a non-miRNA area of the genome. This is a major complication involved when determining, for example, whether a potential miRNA is produced from a repetitive region.
  2. If we suspect our library contained other RNA species, we may want to quantify the level of "contamination". Aligning to the human genome will allow rRNA, tRNA, snoRNA, etc to align. We can then use programs such as bedtools, coupled with appropriate genome annotation files, to quantify these "off-target" hits. This is particularly plausible if, after a miRNA sequencing run, the alignment rate to mirbase is very low.

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

 

Building the bowtie2 mirbase 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:

bowtie2-build <reference_in> <bt2_index_base>
  • 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

 Following what we did earlier for BWA indexing, namely move our FASTA into place, create the index directory, and establish our symbolic links.

Prepare Bowtie2 index directory for mirbase
mkdir -p $WORK/core_ngs/references/bt2/mirbase.v20
mv $WORK/core_ngs/references/hairpin_cDNA_hsa.fa $WORK/core_ngs/references/fasta
cd $WORK/core_ngs/references/bt2/mirbase.v20
ln -s -f ../../fasta/hairpin_cDNA_hsa.fa
ls -la 

Now build the mirbase index with bowtie2-build like we did for the V. cholerae index:

Prepare Bowtie2 index directory for mirbase
bowtie2-build hairpin_cDNA_hsa.fa hairpin_cDNA_hsa.fa

That was very fast!  It's because the mirbase reference genome is so small compared to what programs like this are used to dealing with, which is the human genome (or bigger).  You should see the following files:

bowtie2 index files for miRNAs
hairpin_cDNA_hsa.fa
hairpin_cDNA_hsa.fa.1.bt2
hairpin_cDNA_hsa.fa.2.bt2
hairpin_cDNA_hsa.fa.3.bt2
hairpin_cDNA_hsa.fa.4.bt2
hairpin_cDNA_hsa.fa.rev.1.bt2
hairpin_cDNA_hsa.fa.rev.2.bt2

Performing the bowtie2 local alignment

Now, we're ready to actually try to do the alignment.  Remember, unlike BWA, we actually need to set some options depending on what we're after. Some of the important options for bowtie2 are:

OptionEffect
--end-to-end or --localControls whether the entire read must align to the reference, or whether soft-clipping the ends is allowed to find internal alignments. Default --end-to-end
-LControls the length of seed substrings generated from each read (default = 22)
-NControls the number of mismatches allowable in the seed of each alignment (default = 0)
-iInterval between extracted seeds. Default is a function of read length and alignment mode.
--score-minMinimum alignment score for reporting alignments. Default is a function of read length and alignment mode.

To decide how we want to go about doing our alignment, check out the file we're aligning with less:

Examine miRNA sequences to be aligned
cd $SCRATCH/core_ngs/alignment
less fastq/human_mirnaseq.fastq.gz

Lots of reads have long strings of A's, which must be an adapter or protocol artifact.  Even though we see how we might be able to fix it using some tools we've talked about, what if we had no idea what the adapter sequence was, or couldn't use cutadapt or other programs to prepare the reads?

 In that case, we need a local alignment where the seed length smaller than the expected insert size. Here, we are interested in finding any sections of any reads that align well to a microRNA, which are between 16 and 24 bases long, with most 20-22. So an acceptable alignment should have at least 16 matching bases, but could have more.

If we're also interested in detecting miRNA SNPs, we might want to allow a mismatch in the seed. So, a good set of options might look something like this:

-N 1 -L 16 --local


 If our reads were longer

Because these are short reads we do not have to adjust parameters like inter-seed distance (-i) or minimum alignment score (--min-score) that are a function of read length. If we were processing longer reads, we might need to use parameters like this, to force bowtie2 to "pretend" the read is a short, constant length:

-i C,1,0
--score-min=C,32,0

Yes, that looks complicated, and it kind of is. It's basically saying "slide the seed down the read one base at a time", and "report alignments as long as they have a minimum alignment score of 32 (16 matching bases x 2 points per match, minimum).

See the bowtie2 manual (after you have had a good stiff drink) for a full explanation.

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>]

Let's make a link to the mirbase index directory to make our command line simpler:

Link to mirbase index for bowtie2
cd $SCRATCH/core_ngs/alignment
ln -s -f $WORK/core_ngs/references/bt2/mirbase.v20 mb20

Putting this all together we have a command line that looks like this.

Local bowti2 alignment of miRNA data
bowtie2 --local -N 1 -L 16 -x mb20/hairpin_cDNA_hsa.fa -U fastq/human_mirnaseq.fastq.gz -S human_mirnaseq.sam
 What's going on?

Parameters are:

  • --local – local alignment mode
  • -L 16 – seed length 16
  • -N 1 – allow 1 mismatch in the seed
  • -x  mb20/hairpin_cDNA_hsa.fa – prefix path of index files
  • -U fastq/human_mirnaseq.fastq.gz – FASTQ file for single-end (Unpaired) alignment
  • -S human_mirnaseq.sam – tells bowtie2 to report alignments in SAM format to the specified file

Create a commands file called bt2.cmds with this task definition then generate and submit a batch job for it (time 1 hour, normal queue).

Use nano to create the bt2.cmds file. Then:

 What's going on?
Local bowti2 alignment of miRNA data
launcher_creator.py -n bt2 -j bt2.cmds -t 01:00:00 -q normal
sbatch bt2.slurm; showq -u

When the job is complete you should have a human_mirnaseq.sam file that you can examine using whatever commands you like. An example alignment looks like this.

Example miRNA alignment record
TUPAC_0037_FC62EE7AAXX:2:1:2607:1430#0/1  0  hsa-mir-302b  50  22 3S20M13S * 0 0
    TACGTGCTTCCATGTTTTANTAGAAAAAAAAAAAAG  ZZFQV]Z[\IacaWc]RZIBVGSHL_b[XQQcXQcc
    AS:i:37 XN:i:0  XM:i:1  XO:i:0  XG:i:0  NM:i:1  MD:Z:16G3       YT:Z:UU

Notes:

  • This is one alignment record, although it has been broken up below for readability.
  • This read mapped to the mature microRNA sequence hsa-mir-302b, starting at base 50 in that contig.
  • Notice the CIGAR string is 3S20M13S, meaning that 3 bases were soft clipped from one end (3S), and 13 from the other (13S).
    • If we did the same alignment using either bowtie2 --end-to-end mode, or using bwa aln as in Exercise #1, very little of this file would have aligned.
  • The 20M part of the CIGAR string says there was a block of 20 read bases that mapped to the reference.
    • If we had not lowered the seed parameter of bowtie2 from its default of 22, we would not have found many of the alignments like this one that only matched for 20 bases.

Such is the nature of bowtie2 – it it can be a powerful tool to sift out the alignments you want from a messy dataset with limited information, but doing so requires careful tuning of the parameters, which can take quite a few trials to figure out.

Exercise: About how many records in human_mirnaseq.sam represent aligned reads?

 Solution

We can use our cut / grep trick from Exercise #1, but on the human_mirnaseq.sam file. Since all read names in this file start with TUPAC, we'll use that pattern to select non-header lines.

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

This expressions returns 221086.

Use sort and uniq to create a histogram of mapping qualities

The mapping quality score is in field 5 of the human_mirnaseq.sam file. We can do this to pull out only that field:

Cut mapping quality SAM field
grep -P '^TUPAC' human_mirnaseq.sam | cut -f 5 | head

We will use the uniq create a histogram of these values. The first part of the --help for uniq says:

What uniq does
Usage: uniq [OPTION]... [INPUT [OUTPUT]]
Filter adjacent matching lines from INPUT (or standard input),
writing to OUTPUT (or standard output).

With no options, matching lines are merged to the first occurrence.
Mandatory arguments to long options are mandatory for short options too.
  -c, --count           prefix lines by the number of occurrences

To create a histogram, we want to organize all equal mapping quality score lines into an adjacent block, then use uniq -c option to count them. The sort -n command does the sorting into blocks (-n means numerical sort). So putting it all together, and piping the output to a pager just in case, we get:

Cut mapping quality SAM field
grep -P '^TUPAC' human_mirnaseq.sam | cut -f 5 | sort -n | uniq -c | more

Exercise: What is the flaw in this "program"?

 Answer

We are looking at mapping quality values for both aligned and un-aligned records, but mapping quality only makes sense for aligned reads. This expression does not distinguish between mapping quality = 0 because the read mapped to multiple locations, and mapping quality = 0 because the sequence did not align.

The proper solution will await the use of samtools to filter out unmapped reads.

In previous exercises we explored a few different commands that you might run to get different kinds of information. Those commands will provide similar information here since this file is also in SAM/BAM format. In the last exercise, we will come back to this SAM file to explore ways to compress them effectively and to extract basic quality statistics.

Exercise #4: BWA-MEM - Human mRNA-seq

After bowtie2 came out with a local alignment option, it wasn't long before bwa developed its own local alignment algorithm called BWA-MEM (for Maximal Exact Matches), implemented by the bwa mem command. bwa mem has the following advantages:

  • It incorporates a lot of the simplicity of using bwa with the complexities of local alignment, enabling straightforward alignment of datasets like the mirbase data we just examined
  • It can align different portions of a read to different locations on the genome
    • In a long RNA-seq experiment, reads will (at some frequency) span a splice junction themselves, or a pair of reads in a paired-end library will fall on either side of a splice junction. We want to be able to align reads that do this for many reasons, from accurate transcript quantification to novel fusion transcript discovery.

Thus, our last exercise will be the alignment of a human long RNA-seq dataset composed (by design) almost exclusively of reads that cross splice junctions.

bwa mem was made available when we loaded the bwa module, so take a look at its usage information. The most important parameters, similar to those we've manipulated in the past two sections, are the following:

OptionEffect
-kControls the minimum seed length (default = 19)
-wControls the "gap bandwidth", or the length of a maximum gap. This is particularly relevant for MEM, since it can determine whether a read is split into two separate alignments or is reported as one long alignment with a long gap in the middle (default = 100)
-rControls how long an alignment must be relative to its seed before it is re-seeded to try to find a best-fit local match (default = 1.5, e.g. the value of -k multiplied by 1.5)
-cControls how many matches a MEM must have in the genome before it is discarded (default = 10000)
-tControls the number of threads to use

There are many more parameters to control the scoring scheme and other details, but these are the most essential ones to use to get anything of value at all.

The test file we will be working with is just the R1 file from a paired-end total RNA-seq experiment, meaning it is (for our purposes) single-end. Go ahead and take a look at it, and find out how many reads are in the file.

 Hint:
cd $SCRATCH/core_ngs/alignment
ls fastq
gunzip -c fastq/human_rnaseq.fastq.gz | echo $((`wc -l`/4))

RNA-seq alignment with bwa aln

Now, try aligning it with bwa aln like we did in Example #1, but first link to the hg19 bwa index directory.  In this case, due to the size of the hg19 index, we are linking to Anna's scratch area INSTEAD of our own work area containing indexes that we built ourselves.

Link to BWA hg19 index directory
cd $SCRATCH/core_ngs/alignment
ln -s -f /scratch/01063/abattenh/ref_genome/bwa/bwtsw/hg19
ls hg19

You should see a set of files analogous to the yeast files we created earlier, except that their universal prefix is hg19.fa.  

Go ahead and try to do a single-end alignment of the file to the human genome using bwa aln like we did in Exercise #1, saving intermediate files with the prefix human_rnaseq_bwa. Go ahead and just execute on the command line.

Commands to bwa aln RNA-seq data
bwa aln hg19/hg19.fa fastq/human_rnaseq.fastq.gz > human_rnaseq_bwa.sai
bwa samse hg19/hg19.fa human_rnaseq_bwa.sai fastq/human_rnaseq.fastq.gz > human_rnaseq_bwa.sam

Once this is complete use less to take a look at the contents of the SAM file, using the space bar to leaf through them. You'll notice a lot of alignments look basically like this:

HWI-ST1097:228:C21WMACXX:8:1316:10989:88190     4       *       0       0       *       *       0       0
  AAATTGCTTCCTGTCCTCATCCTTCCTGTCAGCCATCTTCCTTCGTTTGATCTCAGGGAAGTTCAGGTCTTCCAGCCGCTCTTTGCCACTGATCTCCAGCT
  CCCFFFFFHHHHHIJJJJIJJJJIJJJJHJJJJJJJJJJJJJJIIIJJJIGHHIJIJIJIJHBHIJJIIHIEGHIIHGFFDDEEEDDCDDD@CDEDDDCDD

Notice that the contig name (field 3) is just an asterisk ( * ) and the alignment flags value is a 4 (field 2), meaning the read did not align (decimal 4 = hex 0x4 = read did not map).

Essentially, nothing (with a few exceptions) aligned. Why?

 Answer

Because this file was generated exclusively from reads in a larger dataset that cross at least one splice junction. The sequences as they exists in most of the reads do not correspond to a single location in the genome. However subsections of each read do exist somewhere in the genome.

So, we need an aligner that is capable aligning different parts of the read to different genomic loci.

RNA-seq alignment with bwa mem

Exercise: use bwa mem to align the same data

Based on the following syntax and the above reference path, use bwa mem to align the same file, saving output files with the prefix human_rnaseq_mem. Go ahead and just execute on the command line.

bwa mem <ref.fa> <reads.fq> > outfile.sam
 Hint:
bwa mem hg19/hg19.fa fastq/human_rnaseq.fastq.gz > human_rnaseq_mem.sam

Check the length of the SAM file you generated with wc -l. Since there is one alignment per line, there must be 586266 alignments (minus no more than 100 header lines), which is more than the number of sequences in the FASTQ file. This is bwa mem can report multiple alignment records for the same read, hopefully on either side of a splice junction. These alignments can still be tied together because they have the same read ID.

 More advanced piping...

To get an idea of how often each read aligned, and what the 'real' alignment rate is, use the following commands:

# This gives you a view where each read is listed next to the number of entries it has in the SAM file
cut -f 1 human_rnaseq_mem.sam | sort | uniq -c | less

#This gives essentially a histogram of the number of times each read aligned - a plurality of reads aligned twice, which seems reasonable since these are all reads crossing a junction, but plenty aligned more or less
cut -f 1 human_rnaseq_mem.sam | sort | uniq -c | awk '{print $1}' | sort | uniq -c | less	

#This gives a better idea of the alignment rate, which is how many reads aligned at least once to the genome.  Divided by the number of reads in the original file, the real alignment rate is much higher.
cut -f 1 human_rnaseq_mem.sam | sort | uniq | wc -l	

# NOTE: Some of these one-liners are only reasonably fast if the files are relatively small (around a million reads or less). For bigger files, there are better ways to get this information, mostly using samtools.

This alignment rate is pretty good, but it could get better by playing around with the finer details of bwa mem.

Be aware that some downstream tools (for example the Picard suite, often used before SNP calling) do not like it when a read name appears more than once in the SAM file. To mark the extra alignment records as secondary, specify the bwa mem -M option. This option leaves the best (longest) alignment for a read as -is but marks additional alignments for the read as secondary (the 0x100 BAM flag). This designation also allows you to easily filter the secondary reads with samtools if desired.

BWA-MEM vs Tophat

Another approach to aligning long RNA-seq data is to use an aligner that is more explicitly concerned with sensitivity to splice sites, namely a program like Tophat. Tophat uses either bowtie (tophat) or bowtie2 (tophat2) as the actual aligner, but performs the following steps:

  • aligns reads to the genome
  • reads that do not align to the genome are aligned against a transcriptome, if provided
    • if they align, the transcriptome coordinates are converted back to genomic coordinates, with gaps represented in the CIGAR string, for example as 196N
  • reads that do not align to the transcriptome are split into smaller pieces, each of which Tophat attempts to map to the genome

Note that Tophat also reports secondary alignments, but they have a different meaning. Tophat always reports spliced alignments as one alignment records with the N CIGAR string operator indicating the gaps. Secondary alignments for Tophat (marked with the 0x100 BAM flag) represent alternate places in the genome where a  read (spliced or not) may have mapped.

As you can imagine from this series of steps, Tophat is very computationally intensive and takes much longer than bwa mem – very large alignments (hundreds of millions of reads) may not complete in stampede's 48 hour maximum job time!

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 an 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, each of which takes only one line to fully run: 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, we will explore samtools much 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.  However, the exact same commands can be run on the other files by changing the names, so feel free to try executing the same commands 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
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]
Commands:
  -- 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

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

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.  Thus, samtools sort is a piece of samtools that provides the ability to re-order entries in the SAM file either by coordinate or by read name.  If you execute samtools sort without any options, you will see its help page as follows:

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
  -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>
Options:
  -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 would want to sort the file by position rather than by name.  The required options are -O and -T, with -o being useful to specify the output file (either -o or '>' can be used to direct the output to the proper location).  So, to sort the paired-end yeast SAM file by coordinate, and get a SAM file named cholera_rnaseq.sort.sam out as output, we execute the following command, and use less to evaluate the output:

samtools sort -O sam -T yeast_pairedend -o yeast_pairedend.sort.sam yeast_pairedend.sam
less yeast_pairedend.sort.sam

Samtools view

You may have noticed in the last help page that samtools sort can specify a BAM file as input or output, which is the smaller, binary form of a SAM file.  This is a viable option if the file needs sorting - however, in many cases you may just want to compress a SAM file by conversion to BAM without any modifications.  The utility samtools view provides a way of converting SAM files to BAM files directly.  It also provides many, many other functions which we will discuss in the next section.  To get a preview, execute samtools view without any other arguments.  You will 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.sort.sam -o yeast_pairedend.bam

Above, the -b option tells the tool to output BAM, and the -o option specifies the name of the BAM file that will be created.  All the other options have their uses, but we will not discuss them right now.  It is worth noting, however, that if you wanted to convert back from BAM to SAM to read some alignments, you would simply remove the -b option and adjust the file names accordingly.

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 all alignments that are within a particular gene, than the alignments on other chromosomes generally do not need to be loaded.  In order to speed up access, BAI files are BAM index files that allow other programs to navigate directly to the alignments of interest.  This is especially important when you have many alignments.  The utility samtools index directly creates an index that has the exact name as the input file, with '.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]
Options:
  -b       Generate BAI-format index for BAM files [default]
  -c       Generate CSI-format index for BAM files
  -m INT   Set minimum interval size for CSI indices to 2^INT [14]

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

samtools index yeast_pairedend.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 provided it is in the same directory as the BAM file that it was produced from, and shares the same name up until the '.bai'' extension.

Samtools idxstats

Now that we have a sorted, compressed, and 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 literally could not be simpler - just type the following command:

 

samtools idxstats yeast_pairedend.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, and (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 pair mapped to, but still flagged as unmapped.

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.bam

Ignore the "+ 0" addition to each line - that is a carried-over convention that is no longer necessary.  The most important statistic is arguably alignment 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.

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