More Alignment exercises 0

Exercise #4: Bowtie2 global alignment - Vibrio cholerae RNA-seq

While we have focused on aligning eukaryotic data, the same tools can be used with prokaryotic data. The major differences are less about the underlying data and much more about the external/public databases that store and distribute reference data. If we want to study a prokaryote, the reference data is usually downloaded from a resource like GenBank.  

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

Overview of Vibrio cholerae alignment workflow with Bowtie2

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

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

Obtaining the GenBank record(s)

First prepare a directory to work in, and change to it:

mkdir -p $SCRATCH/core_ngs/alignment/vibrio/tmp
cd $SCRATCH/core_ngs/alignment/vibrio/tmp

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

1. Navigate to http://www.ncbi.nlm.nih.gov/nuccore/NC_012582
   • click on the Send to down arrow (top right of page)
   • select Complete Record
   • select Clipboard as Destination
   • click Add to Clipboard
2. Perform these steps in your Terminal window
   
   • cd $SCRATCH/core_ngs/alignment/vibrio/tmp
   • type wget then paste the URL from the clipboard
     • should be http://www.ncbi.nlm.nih.gov/nuccore/NC_012582
3. Repeat steps 1 and 2 fot the 2nd chromosome
   • NCBI URL is http://www.ncbi.nlm.nih.gov/nuccore/NC_012583
   • you should now have 2 files, NC_012582  and NC_012583
4. Combine the 2 files into one using cat
   
   • cat NC_012582  NC_012583 > vibCho.gbk

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

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

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

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

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

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

module load perl
module load bioperl

These commands make several scripts directly available to you. The one we will use is called 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
• a 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:

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

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

Recall from when we viewed the GenBank file that there are genome annotations available as well that we would like to extract into GFF format.  However, the 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:

bowtie2-build vibCho.O395.fa vibCho.O395

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

Performing the bowtie2 alignment

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

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

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

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

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

So our command would look like this:

bowtie2 -x vibCho/vibCho.O395 -U fastq/cholera_rnaseq.fastq.gz -S cholera_rnaseq.sam

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).

launcher_creator.py -n bt2_vibCho -j bt2_vibCho.cmds -t 01:00:00 -A UT-2015-05-18
sbatch bt2_vibCho.slurm; showq -u

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

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.

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.

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 
module load perl
module load bowtie/2.2.0

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

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

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, development queue).

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

launcher_creator.py -n bt2 -j bt2.cmds -t 01:00:00 -A UT-2015-05-18
sbatch bt2.slurm; showq -u

#copy from Amelia's scratch:
cp /scratch/01786/awh394/core_ngs.test/alignment/human_mirnaseq.sam .

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 -v '^@' 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 -v '^@' 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 -v '^@' human_mirnaseq.sam | cut -f 5 | sort -n | uniq -c | more

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

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))

A word about real splice-aware aligners

Using BWA mem for RNA-seq alignment is sort of a "poor man's" RNA-seq alignment method. Real splice-aware aligners like tophat2 or star have more complex algorithms (as shown below) – and take a lot more time!

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 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.

# 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!