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 is settling down 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, etc). 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.

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.

1. Become comfortable with the basic steps of indexing a reference genome, mapping reads, and converting output to SAM/BAM format for downstream analysis.

2. 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.

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

Rather than having to download these files from the SRA or EUN and NCBI, these data files are available in the following directory:

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. 

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.

Reminders about working with sequencing files

Remember, from the introduction tutorial, there are multiple ways to look at our sequencing files without using cat:

How to determine how many reads are in a fastq file

How to determine how long the reads are in a fastq file

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. 

While you could write your own sequence converter, hopefully it jumps out at you that this is something someone else must have done before. In situations like this, you can often spend a few minutes on google finding a stackoverlow question/answer that deals with something like this. Some will be in reference to how to code such things, but the particularly useful ones will be the ones that point to a program or repository where someone has already done this for you. 

In this case the bp_seqconvert.pl perl script is included as part of the bioperl module package. Rather than attempt to find it as part of a conda package, or in some other repository we will use the module version. If needing this script in the future outside of TACC, https://metacpan.org/dist/BioPerl/view/bin/bp_seqconvert. 

Load the bioperl module and run the script without any options to display the help contents