Alignment

Table of Contents

Overview and Objectives

Once raw sequence files are generated (in FASTQ format) and quality-checked, the next step in most NGS pipelines is mapping to a reference genome. For individual sequences of interest, it is common to use a tool like BLAST to identify genes or species of origin. However, a typical example will have millions of reads, and a reference space that is frequently billions of bases, which BLAST and similar tools are not really designed to handle.

Thus, a large set of computational tools have been developed to quickly, and with sufficient (but NOT absolute) accuracy align each read to its best location, if any, in a reference. Even though many mapping tools exist, a few individual programs have a dominant "market share" of the NGS world. These programs vary widely in their design, inputs, outputs, and applications. In this section, we will primarily focus on two of the most versatile mappers: BWA and Bowtie2, the latter being part of the Tuxedo suite (e.g. Tophat2).

Sample Datasets

You have already worked with a paired-end yeast ChIP-seq dataset, which we will continue to use here.  We will also use two additional RNA-seq datasets.  The additional data are located in the path:

/corral-repl/utexas/BioITeam/core_ngs_tools/human_stuff 

So, the following are the data you will need:

Now we need to set up the raw data for processing. Stage these files on Stampede from Corral in the fewest possible commands in a directly called "

cds
mkdir raw_data
cp /corral-repl/utexas/BioITeam/core_ngs_tools/*/*.fastq.gz ./raw_data/
cd raw_data/

Do you believe that I gave you files of any reasonable quality?  I wouldn't, so you should check it out.

cds; cd raw_data
module load fastqc
fastqc human_rnaseq.fastq.gz
fastqc human_mirnaseq.fastq.gz

Reference Genomes

Before we get to alignment, we need a genome to align to.  We will use three different references here:  the human genome (hg19), the yeast genome (sacCer3), and mirbase (v20).  Mirbase is a collection of all known microRNAs in all species, and 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 containing all the sequences we are actually interested in.

Searching genomes, however, is hard work and takes a long time if done on an un-indexed, linear genomic sequence.  So, most aligners require that references be indexed for quick access  The aligners we are using each require a different index, but use the same method (the Burrows-Wheeler Transform) to get the job done.  This requires taking a FASTA file as input, with each chromosome (or contig) as a separate entry, and producing some aligner-specific set of files as output.  Then, those output files are used by the aligner when executing a given alignment command. Here are some details of where you can find the references we need now (and many more):

BWA - Yeast ChIP-seq

Bowtie2 and Local Alignment - Human microRNA-seq

BWA-MEM (and Tophat2) - Human mRNA-seq

Future Directions