Overview
Once you know you are working with the best quality data (Evaluating Raw Sequencing data tutorial) possible, the first step in nearly every NGS analysis pipeline is to map sequencing reads to a reference genome. In this tutorial we'll explore these basic principles using bowtie2 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). It is possible a different read mapper would be better for your set of experiments. More will be discussed about selecting a good tool on Friday.
Other read mappers
Previous versions of this class and tutorial have covered using bowtie and bwa. Please consult these tutorials for more specific information on each mapping program. A previous version of this tutorial included a trimmed down version of the bwa tutorial if you just want the 'flavor' of what other read mappers involve.
Learning Objectives
This tutorial covers the commands necessary to use bowtie2 to map reads to a reference genome, and concepts applicable to many more mappers.
- Become comfortable with the basic steps of indexing a reference genome, mapping reads, and converting output to
SAM/BAMformat for downstream analysis. - Use bowtie2 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 on the course outline for more details of the theory behind read mapping algorithms and critical considerations for using these tools and references correctly.
Mapping tools summary
The tutorial currently available on the Lonestar cluster at TACC is as follows:
Tool | TACC | Version | Download | Manual | Example |
|---|---|---|---|---|---|
Bowtie2 | module load bowtie/2.3.4 You may recall we added this to our .bashrc file yesterday so it is already loaded | 2.3.4 |
Modules also exist on lonestar5 for bwa.
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
You may recognize this as the same files we used for the fastqc and cutadapt tutorial. If you chose to improve the quality of R2 reads using cutadapt as you did for R1 in the tutorial, you could use the improved reads in this tutorial to see what a difference the improved reads can make for read mapping.
File Name | Description | Sample |
|---|---|---|
| Paired-end Illumina, First of pair, FASTQ format | Re-sequenced E. coli genome |
| Paired-end Illumina, Second of pair, FASTQ format | Re-sequenced E. coli genome |
| Reference Genome in Genbank format | E. coli B strain REL606 |
The easiest way to run the tutorial is to copy this entire directory into a new folder called "GVA_bowtie2_mapping" on your $SCRATCH space and then run all of the commands from inside that directory. See if you can figure out how to do that. When you're in the right place, you should get output like this from the ls command.
tacc:/scratch/<#>/<UserName>/GVA_bowtie2_mapping$ ls NC_012967.1.gbk SRR030257_1.fastq SRR030257_2.fastq SRR030257_2.fastq.gz
Reminders about working with sequencing files
Beware the cat command when working with NGS data
NGS data can be quite large, a single lane of an Illumina Hi-Seq run generates 2 files each with 100s of millions of lines. Printing all of that can take an enormous amount of time and will likely crash your terminal long before it finishes. If you find yourself in a seemingly endless scroll of sequence (or anything else for that matter) remember control+c will kill whatever command you just executed.
If hitting control+c several times doesn't work, control +z will stop the process, you then need to kill the process using kill %1 if control+z doesn't work, you may be best off closing the window, opening a new window, logging back in, and picking up where you left off. Note that for the purpose of this class, you should make sure to restart an idev session.
Remember, from the introduction tutorial, there are multiple ways to look at our sequencing files without using cat:
| Command | useful for | bad if |
|---|---|---|
| head | seeing the first lines of a file (10 by default) | file is binary |
| tail | seeing the last lines of a file (10 by default) | file is binary |
| cat | print all lines of a file to the screen | the file is big and/or binary |
| less | opens the entire file in a separate program but does not allow editing | if you are going to type a new command based on the content, or forget the q key exits the view, or file is binary |
| more | prints 1 page worth of a file to the screen, can hold enter key down to see next line repeatedly. Contents will remain when you scroll back up. | you forget that you hit the q key to stop stop looking at the file, or file is binary |
grep -c "^+$" SRR030257_1.fastq
sed -n 2p SRR030257_1.fastq | awk -F"[ATCGNatcgn]" '{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? Everybody has own favorite formats and/or those that they are the most familiar with but humans can typically pick the information they need out of comparable formats. Programs can only be written to assume a single type of format (or allow you to specify a format if the author is particularly generous), and can only find things in single locations based on that format.
The bp_seqconvert.pl script is a common script written in Bioperl that is a 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. As it is fairly rare that you need to convert sequence files between different format, bioperl is actually not listed as one of the modules on your .bashrc file in your $HOME directory that you set up yesterday. Additionally it gives an opportunity to have you working with the module system. If you find yourself needing to do lots of sequence conversions, you may want to add a 'module load bioperl/1.007002' line to your .bashrc file.
Run the script without any arguments to get the help message:
module load gcc module load bioperl bp_seqconvert.pl
Convert a gbk reference to a embl reference
Convert the Genbank file NC_012967.1.gbk to EMBL format, and name this new file NC_012967.1.embl.
It is somewhat frustrating or confusing that this command does not give us any output saying it was successful. The fact that you get your prompt back is often the only sign the computer has finished doing something.
Last year some students were getting the following error message when they execute this command even though the new file seems to be generated correctly. As I am not able to reconstruct the error, please send a message or say something on zoom if you do encounter it so I know it is still present
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.
Converting from fastq to fasta format
Sometimes you only want to work with a subset of a full data file to check for overall trends, or to try out a new piece of code. Convert only the first 10,000 lines of SRR030257_1.fastq to FASTA format.
head -n 10000 SRR030257_1.fastq | bp_seqconvert.pl --from fastq --to fasta > SRR030257_1.fasta
Mapping with bowtie2
Bowtie2 is a complete rewrite of an older program bowtie. In terms of configurability, sensitivity, and speed it is useful for a wide range of projects. After years of teaching bwa mapping along with bowtie2, bowtie2 alone is now taught as I never recommend anyone use bwa, and based on positive feedback we continue with this set up. For some more details about how read mappers work see the bonus presentation about read mapping details and file formats on the course home page, and if you find a compelling reason to use bwa (or any other read mapper) rather than bowtie2 after the course is over, I'd love to hear from you.
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.
First you need to 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.
Next, we want to make sure the bowtie2 module is loaded (we use module spider to get the current name, which may not be bowtie/2.3.4 if you re-run this tutorial later):
# Currently Loaded Modules Matching: bowtie # None found. # Inactive Modules Matching: bowtie # 1) bowtie/2.3.4
Further, when we try to load bowtie/2.3.4 we get an error message.
Lmod has detected the following error: These module(s) or extension(s) exist but cannot be loaded as requested: "bowtie/2.3.4" Try: "module spider bowtie/2.3.4" to see how to load the module(s).
See if you can figure out how to load bowtie using the information above.
For many read mappers, the first step 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.
Take a look at your output directory using ls bowtie2 to see what new files have appeared. These files are binary files, so looking at them with head or tail isn't instructive and can cause issues with your terminal. If you insist on looking at them (or accidentally do so before you read this) and your terminal begins behaving oddly, simply close it and log back into lonestar with a new terminal, and start a new idev session.
you may be wondering why creating an index is a common first step for many different mapping programs.
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, it's time to map the reads!
IMPORTANT
This command can take a while (~5 minutes) and is extremely taxing. 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, in previous years, TACC has noticed the spike in usage when multiple students forgot to make sure they were on idev nodes and complained pretty forcefully to us about it. Let's not have this be one of those years. Use the showq -u command to make sure you are on an idev node. If you are not sure if you are on an idev node, speak up on zoom and I'll show(q) -u what you are looking for. Yes, your instructor likes bad puns. My apologies.
If you are not on an idev node, and need help to relaunch it, click over to the idev tutorial.
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:
- SAM files can be enormously humongous text files (potentially >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
headorgrepor more or using a viewer like IGV, which we will cover in a later tutorial. - 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, or they are covered in the bonus presentation about read mapping and file formats which you can find on the home page.
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
Multithreaded execution
We have actually massively under-utilized Lonestar in this example. We ran the command using only a single processor (a single "thread") rather than the 48 we have available. 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 48 processors for the bowtie process.
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 think about the "wayness" of how you request nodes on Lonestar (we'll go over this more on this on Friday), or make better use of running commands in the background using the & symbol at the end of the command line.
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.
What comes after mapping?
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.
Optional exercises
In the bowtie2 example, we mapped in
--localmode. Try mapping in--end-to-endmode (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 run time without causing many fewer reads to be mapped? (increase performance)
Here is a link to help you return to the GVA 2020 course schedule.
