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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:

$SCRATCH/core_ngs_tools/human_stuff 

So, the following are the files you will need:

File NameDescriptionSample
Sample_Yeast_L005_R1.cat.fastq.gzPaired-end Illumina, First of pair, FASTQYeast ChIP-seq
Sample_Yeast_L005_R2.cat.fastq.gzPaired-end Illumina, Second of pair, FASTQYeast ChIP-seq
human_rnaseq.fastq.gzPaired-end Illumina, First of pair only, FASTQHuman RNA-seq
human_mirnaseq.fastq.gzSingle-end Illumina, FASTQHuman microRNA-seq

Do a fast quality check on the two new data files like you did earlier on the yeast files.

 Hint:
cd $SCRATCH/core_ngs_tools
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.

 If it's simpler and faster, would one ever want to align a miRNA dataset to hg19 rather than mirbase? If so, why?

Due to natural variation, sequencing errors, and processing issues, variation between reference sequence and sample sequence is always possible. Alignment to the human genome allows a putative "microRNA" read the opportunity to find a better alignment in a region of the genome that is not an annotated microRNA relative to the microRNA reference sequence. If this occurs, we might think that a read represents a microRNA (since it aligned in the mirbase alignment), when it is actually more likely to have come from a non-miRNA area of the genome.

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 here are many more):

ReferenceSpeciesBase LengthContig NumberSourceDownload Link
Hg19Human3,137,161,26425 (really 93)UCSC 
MirbaseV20Human 1908Mirbase 
SacCer3Yeast 17UCSC 
Mm10Mouse 22 (really 66)UCSC 

The yeast and mirbase fasta files (with just reference sequence information) are located on the path:

/

 

Hg19 is way too big for us to index here, so we've already done it.  


BWA - Yeast ChIP-seq

 

OptionEffectBest Practice Setting
-k  
-n  
-l  
-t  

 

Bowtie2 and Local Alignment - Human microRNA-seq

 

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

 

Future Directions

 

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