Overview

Once you know you are working with the best quality data (Evaluating Raw Sequencing data tutorial) that you can be, 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 explore these basic principles using bowtie2 on TACC.

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idev  -m 240180 -r CCBB_5.23.17PM_Day_2 -A UT-2015-05-18

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/BAM format 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 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:

module load bowtie/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:

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You may recognize this as the same files we used for the fastqc and fastx_toolkit cutadapt tutorial. If you chose to improve the quality of R2 reads using fastx_toolkit 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. 

The easiest way to run the tutorial is to copy this entire directory into a new folder called "BDIBGVA_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.

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cds
cp -r $BI/gva_course/mapping/data BDIBGVA_bowtie2_mapping
cd BDIBGVA_bowtie2_mapping
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:

Reminder about Linux 1 liners

Below are several commands we've already been using, and some new ones put together to improve your skills.

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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


# OR
grep --count "^+$" 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? 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. 

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module load gcc
module load bioperl
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?

    Expand
    title 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 Code Block language bash title Sill need help? collapse true 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. Code Block 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?

    Expand
    title Click here if you need a hint
    Remember use the | character to have the output of head feed into the bp_seqconvert.pl script. Code Block language bash title Click here for the answer collapse true 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 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. After years of teaching bwa mapping along with bowtie2, last year was the first class to use only bowtie2 since we 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, and if you find a compelling reason to use bwa (or any other read mapper) rather than bowtie2, we'd love to hear from you.

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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

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:

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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 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.

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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 for your free time

• 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 2017 course schedule.