Overview

The first step in nearly every next-gen sequence analysis pipeline is to map sequencing reads to a reference genome. In this tutorial we'll run some common mapping tools on TACC.

The world of read mappers seems to be settling down a bit after being a bioinformatics Wild West where there was a new gun in town every week that promised to be a faster and more accurate shot than the current record holder. Things seem to have reached the point where there is mainly a trade-off between speed, accuracy, and configurability among read mappers that have remained popular. There are over 50 read mapping programs listed here. Each mapper has its own set of limitations (on the lengths of reads it accepts, on how it outputs read alignments, on how many mismatches there can be, on whether it produces gapped alignments, on whether it supports SOLiD colorspace data, etc.). As evidence of how things are settling down, we're going to (mainly) stick to just bowtie2 in this course.

Learning Objectives

This tutorial covers the commands necessary to use several common read mapping programs.

• Become comfortable with the basic steps of indexing a reference genome, mapping reads, and converting output to SAM/BAM format for downstream analysis.
• Use bowtie2 and BWA to map reads from an E. coli Illumina data set to a reference genome and compare the output.

Theory

Please see the Introduction to mapping presentation for more details of the theory behind read mapping algorithms and critical considerations for using these tools correctly.

Mapping tools summary

The tutorial currently available on  the Lonestar cluster at TACC is as follows:

module load bowtie/2.1.0

Modules also exist at the current time for: bwa, bowtie, and SHRiMP.

Tutorial: E. coli genome re-sequencing data

The following DNA sequencing read data files were downloaded from the NCBI Sequence Read Archive via the corresponding European Nucleotide Archive record. They are Illumina Genome Analyzer sequencing of a paired-end library from a (haploid) E. coli clone that was isolated from a population of bacteria that had evolved for 20,000 generations in the laboratory as part of a long-term evolution experiment (Barrick et al, 2009). The reference genome is the ancestor of this E. coli population (strain REL606), so we expect the read sample to have differences from this reference that correspond to mutations that arose during the evolution experiment.

Transferring Data

We have already downloaded data files for this example and put them in the path:

$BI/gva_course/mapping/data

The easiest way to run the tutorial is to copy this entire directory into a new folder called "bowtie2mapping" on your $SCRATCH space and then run all of the commands from inside that directory. See if you can figure out how to do that. When you're in the right place, you should get output like this from the ls command.

tacc:~$ ls
NC_012967.1.gbk  SRR030257_1.fastq  SRR030257_2.fastq

cds
cp -r $BI/gva_course/mapping/data bowtie2MappingTutorial
cd bowtie2MappingTutorial
ls

Useful commands

Often you will have general questions about your sequencing files that you want to answer before or after starting your actual analysis. Here we show you some very handy commands after a warning:

Linux 1 liners

head * # will show the top 10 lines to give you an idea of the file type/structure
tail * # will show the last 10 lines to determine if the file is a repetitive structure

wc -l *  # can be very useful to determine if it can be printed to screen or opened in a text editor

wc -l *  # and then divide by 4 using the your knowledge of fastq files
 
# OR
 
grep ^@SRR030257 SRR030257_1.fastq | wc -l
 
# OR
grep --count ^@SRR030257 SRR030257_1.fastq

sed -n 2p SRR030257_1.fastq | awk -F"[ATCGatcg]" '{print NF-1}'

Converting sequence file formats

Occasionally you might download a sequence or have it emailed to you by a collaborator in one format, and then the program that you want to use demands that it be in another format. Why do they have to be so picky?

The bp_seqconvert.pl script that is installed as part of Bioperl is one helpful utility for converting between many common sequence formats. On TACC, the Bioperl modules are installed, but the helper script isn't. So, we've put it in a place that you can run it from for your convenience. However, remember that any time that you use the script you must have the bioperl module loaded. We also took care of this for you when we edited your ~/.profile_user file in the Linux introduction.

Run the script without any arguments to get the help message:

bp_seqconvert.pl

Exercises

The file NC_012967.1.gbk is in Genbank format. The files SRR030257_*.fastq are in FASTQ format.

• Convert NC_012967.1.gbk to EMBL format. Call the output NC_012967.1.embl.

  • Does EMBL format have sequence features (like genes) annotated?

     Click here for a hint

    Try reading through the program help when you run the bp_seqconvert.pl without any options to see the syntax required

    Sill need help? Expand source

    bp_seqconvert.pl --from genbank --to embl < NC_012967.1.gbk > NC_012967.1.embl
    head -n 100 NC_012967.1.embl
    

    You might get an error or a warning like the following, even if the bp_seqconvert.pl script executed correctly so don't worry.

    Use of uninitialized value in substitution (s///) at /opt/apps/bioperl/1.6.901/Bio/SeqIO/embl.pm line 777, <STDIN> line 164674.
    Use of uninitialized value in concatenation (.) or string at /opt/apps/bioperl/1.6.901/Bio/SeqIO/embl.pm line 779, <STDIN> line 164674.

    From the head command, you should see that yes, EMBL files do maintain gene annotation features.

• Convert only the first 10,000 lines of SRR030257_1.fastq to FASTA format.

  • What information was lost by this conversion?

     Click here if you need a hint

    Remember use the | character to have the output of head feed into the bp_seqconvert.pl script.

    Click here for the answer Expand source

    head -n 10000 SRR030257_1.fastq | bp_seqconvert.pl --from fastq --to fasta > SRR030257_1.fasta
    head SRR030257_1.fastq
    head SRR030257_1.fasta
    

    The line of funny ASCII characters was lost. Remember, those are your "base quality scores". Many mappers will use the base quality scores to improve how the reads are aligned by not placing as much emphasis on poor bases.

Mapping with bowtie2

Bowtie2 is a complete rewrite of bowtie. It is currently the latest and greatest in the eyes of one very picky instructor (and his postdoc/gradstudent) in terms of configurability, sensitivity, and speed.

Create a fresh output directory named bowtie2. We are going to create a specific output directory for the bowtie2 mapper within the directory that has the input files so that you can compare the results of other mappers if you choose to do the other tutorials.

mkdir bowtie2

Next, load the bowtie2 module (we use module spider to get the current name, which may not be bowtie/2.1.0 if you re-run this tutorial):

module spider bowtie
module load bowtie/2.1.0

bowtie2 --version
module list
which bowtie2

Now convert the reference file from GenBank to FASTA using what you learned above. Name the new output file NC_012967.1.fasta and put it in the same directory as NC_012967.1.gbk.

bp_seqconvert.pl --from genbank --to fasta < NC_012967.1.gbk > NC_012967.1.fasta

Generally speaking, the first step in mapping is quite often indexing the reference file regardless of what mapping program is used. Put the output of this command into the bowtie directory we created a minute ago. The command you need is:

bowtie2-build

Try typing this alone in the terminal and figuring out what to do from the help show just from typing the command by itself.

bowtie2-build NC_012967.1.fasta bowtie2/NC_012967.1

Why do so many different mapping programs create an index as a first step you may be wondering?

Like an index for a book (in the olden days before Kindles and Nooks), creating an index for a computer database allows quick access to any "record" given a short "key". In the case of mapping programs, creating an index for a reference sequence allows it to more rapidly place a read on that sequence at a location where it knows at least a piece of the read matches perfectly or with only a few mismatches. By jumping right to these spots in the genome, rather than trying to fully align the read to every place in the genome, it saves a ton of time.

Indexing is a separate step in running most mapping programs because it can take a LONG time if you are indexing a very large genome (like our own overly complicated human genome). Furthermore, you only need to index a genome sequence once, no matter how many samples you want to map. Keeping it as a separate step means that you can skip it later when you want to align a new sample to the same reference sequence.

Finally, map the reads! The command you need is:

bowtie2

Try reading the help to figure out how to run the command yourself. This command takes a while (~5 minutes). This is longer than we want to run a job on the head node (especially when all of us are doing it at once). In fact, TACC noticed the spike in usage last time we taught the class and we got in trouble.

So, you will want to submit the full job to the cluster like you learned in the introduction.

But first, try to figure out the command and start it in interactive mode. Remember these are paired-end reads. Use control-c to stop the job once you are sure it is running without an immediate error! Then, submit your command that is working to the TACC queue.

bowtie2 -t -x bowtie2/NC_012967.1 -1 SRR030257_1.fastq -2 SRR030257_2.fastq -S bowtie2/SRR030257.sam  # the -t command is not required for the mapping, but it can be particularly informative when you begin comparing different mappers

launcher_creator.py -n "bowtie2" -t 00:10:00

Your final output file is in SAM format. It's just a text file, so you can peek at it and see what it's like inside. Two warnings though:

1. SAM files can be enormously humongous text files (maybe >1 gigabytes). Attempting to open the entire file at once can cause your computer to lock up or your text editor to crash. You are generally safer only looking at a portion at a time using linux commands like head or grep or using a viewer like IGV, which we will cover later.
2. SAM files have some rather complicated information encoded as text, like a binary encoded FLAGS field and CIGAR strings. We'll take a look at some of these later, if we have time.

Still, you should recognize some of the information on a line in a SAM file from the input FASTQ, and some of the other information is relatively straightforward to understand, like the position where the read mapped. Give this a try:

head bowtie2/SRR030257.sam

More reading about SAM files

• The official SAM file specification
• http://genome.sph.umich.edu/wiki/SAM

Multithreaded execution

We have actually massively under-utilized Lonestar in this example. We submitted a job that reserved a single node on the cluster, but that node has 12 processors. Bowtie was only using one of those processors (a single "thread")! For programs that support multithreaded execution (and most mappers do because they are obsessed with speed) we could have sped things up by using all 12 processors for the bowtie process.

bowtie2 -t -p 12 -x bowtie2/NC_012967.1 -1 SRR030257_1.fastq -2 SRR030257_2.fastq -S bowtie2/SRR030257.sam

If you want to launch many processes as part of one job, so that they are distributed one per node and use the maximum number of processors available, then you need to learn about the "wayness" of how you request nodes on Lonestar and possibly edit your *.sge script.

One consequence of using multithreading that might be confusing is that the aligned reads might appear in your output SAM file in a different order than they were in the input FASTQ. This happens because small sets of reads get continuously packaged, "sent" to the different processors, and whichever set "returns" fastest is written first. You can force them to appear in the same order (at a slight cost in speed) by adding the --reorder flag to your command, but is typically only necessary if the reads are already ordered or you intend to do some comparison between the input and output.

Optional Exercises

• In the bowtie2 example, we mapped in --local mode. Try mapping in --end-to-end mode (aka global mode).

• Do the BWA tutorial so you can compare their outputs.
  • Did bowtie2 or BWA map more reads?
  • In our examples, we mapped in paired-end mode. Try to figure out how to map the reads in single-end mode and create this output.
  • Which aligner took less time to run? Are there any options you can change that:
    • Lead to a larger percentage of the reads being mapped? (increase sensitivity)
    • Speed up performance without causing many fewer reads to be mapped? (increase performance)

Next steps...

The next steps are often to view the output using a specific viewer on your local machine, or to begin identifying variant locations where the reads differ from the reference sequence. These will be the next things we cover in the course. Here is a link to help you return to the GVA 2015 course schedule.