Alignment

Table of Contents

Overview and Objectives

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

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:

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:

Reference Genomes

Before we get to alignment, we need a genome to align to.  We will use four different references here: 

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

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.

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.

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:

/scratch/01063/abattenh/ref_genome/bwa/bwtsw/hg19

However, 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:

/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:

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 – 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

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 subcommands 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.

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.

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:

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 -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?

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

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

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).

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?

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. 

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 -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 8 (8 tasks per node) on the normal queue and a time of 1 hour, then submit the job and monitor the queue:

launcher_creator.py -n aln -j aln.cmds -t 01:00:00 -q normal -w 8
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.

# 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 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?

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

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

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

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

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.

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).

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:

# 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:

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:

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).

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?

echo $((612968 * 100/ 1184360))

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

Exercise #2: Bowtie2 and 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.

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 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.

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 index with bowtie2-build:

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:

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:

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

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

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:

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.

bowtie2 --local -N 1 -L 16 -x mb20/hairpin_cDNA_hsa.fa -U fastq/human_mirnaseq.fastq.gz -S human_mirnaseq.sam

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).

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.

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?

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

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:

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:

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:

grep -P '^TUPAC' human_mirnaseq.sam | cut -f 5 | sort -n | uniq -c | more

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

Exercise #3: Bowtie2 - 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.  However, while the alignment procedure 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

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 files into usable reference FASTA files and genome annotations (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 $SCRATCH/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 interconvert file formats like FASTA, GBK, and others.  We will use this script to get both a FASTA format for indexing and alignment, and also a GFF file (standing for General Feature Format) that will contain information about all genes (or, more generally, features) in the genome.  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.  However, 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 "#".

Building the bowtie2 vibCho index

To build the reference index for alignment, we actually only need the FASTA file, since annotations are frequently not necessary for alignment purposes. (This is not always true, as extensively spliced transcriptomes requires splice junction annotations to align RNA-seq data properly, but for now we will only use the FASTA file.)  We build the reference files exactly as we did with mirbase, only swapping out the FASTA files as follows:

bp_genbank2gff3.pl --format Genbank vibCho.O395.gbk
mv vibCho.O395.gbk.gff vibCho.O395.gff
less vibCho.O395.gff

Performing the bowtie2 alignment

(this will stop here because samtools index/idxstats/flagstat will be Exercise #5)

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:

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.

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.

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.

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?

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

bwa mem hg19/hg19.fa fq/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.

# 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.

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 extremely widespread set of tools that allow a user to manipulate SAM/BAM files in many different ways, ranging from simple tasks like converting SAM to BAM (and vice versa) to more complex functions like the removal of PCR duplicates or the filtering of alignments by various metrics.  In this exercise, we will explore three very simple utilities provided by samtools: index, flagstat, and idxstats.  Each of these is executed in one line for a given SAM/BAM file.  In the next section, we will explore samtools much more in depth, and show how it can be connected to other post-alignment tools.

Samtools Index

Samtools Idxstats

Samtools Flagstat