Alignment (archive)

Overview

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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, 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 login5.ls5.tacc.utexas.edu

This should be second nature by now

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

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:

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:

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):

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

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

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

 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).

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.

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

Now execute the bwa index command.

Build BWA index for sacCer3

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 (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

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?

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

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?

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. 

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

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

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

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?

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

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

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?

Using cut to isolate fields

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

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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

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

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

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

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)

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

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

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