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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:


Code Block
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.

titleIf it's simpler and faster, would one ever want to align to hg19 rather than mirbase for a miRNA dataset? 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 many more):

ReferenceSpeciesLengthContig NumberSourceDownload Link
Hg19Human  UCSC 
MirbaseV20Human  Mirbase 
SacCer3Yeast  UCSC 
Mm9Mouse  UCSC 

BWA - Yeast ChIP-seq


Bowtie2 and Local Alignment - Human microRNA-seq