Copy of good Alignment workflow

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 many NGS pipelines is mapping to a reference genome.

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

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

Stage the alignment data

First connect to login5.ls5.tacc.utexas.edu. This should be second nature by now

Then stage the sample datasets and references we will use.

Get the alignment exercises files

These are descriptions of the FASTQ files we copied:

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.)

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

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

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

Reference FASTA locations

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

BWA hg19 index location

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 a later exercise since steps 6 - 10 are common to nearly all post-alignment workflows.

Introducing BWA

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

BWA suite usage

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

Building the BWA sacCer3 index

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

bwa index usage

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

• The name of the FASTA file  

• Whether to use the bwtsw or is algorithm for indexing

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

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

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

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

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

We've discovered a pattern (also known as a regular expression) to use in searching, and the command line tool that does regular expression matching is grep (general regular expression parser). Read more about grep here: Advanced commands: 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 several slightly different "flavors" of them. But the most common is the Perl style (http://perldoc.perl.org/perlretut.html), which was one of the fist and still the most powerful (there's a reason Perl was used extensively when assembling the human genome). We're only going to use the most simple of regular expressions here, but learning more about them will pay handsome dividends for you in the future.

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 Perl-style regular expression patterns.

  • This makes including special characters like Tab ( \t ), Carriage Return ( \r ) or Linefeed ( \n ) much easier that the default POSIX paterns.

  • While it is not required here, it generally doesn't hurt to include this option.

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

• sacCer3.fa is the file to search.

  • lines with text that match our pattern will be written to standard output

  • non matching lines will be omitted

• We pipe to more just in case there are a lot of contig names.

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

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

As 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. (Note that the shell does look inside double quotes ( " ) for certain special signals, such as looking for environment variable names to evaluate. Read more about Quoting in the shell.)

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

We might be able to get away with just using this literal alone as our regex, specifying '>' as the command line 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".

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 prepare a directory for this exercise and link the sacCer3 reference directories there (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.

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.

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

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

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

We will execute these commands directly (not in a batch job), but since they are fairly large files we will first set up an interactive development (idev) session, which will give us a compute node for 3 hours:

Start an idev session

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

bwa aln commands for yeast R1 and R2

When all is done you should have two .sai files: yeast_R1.sai and yeast_R2.sai. Double check that output was written by doing ls -lh and making sure the file sizes listed are not 0.

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

Since you have your own private compute node, you can use all its resources. It has 24 cores, so re-run the R2 alignment asking for 20 execution threads.

Exercise: How much of a speedup did you seen when aligning the R2 file with 20 threads?

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.

Pairing of BWA R1 and R2 aligned reads

You should now have a SAM file (yeast_pairedend.sam) that contains the alignments. It's just a text file, so take a look with head, more, less, tail, or whatever you feel like. Later you'll learn additional ways to analyze the data with samtools once you create a BAM file.

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

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

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 mapped and un-mapped reads?

• What is the order of the alignment records in this SAM file?
  
  

Using cut to isolate fields

Recall the format of a SAM alignment record:

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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 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:

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