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).
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:
So, the following are the data you will need:
|Sample_Yeast_L005_R1.cat.fastq.gz||Paired-end Illumina, First of pair, FASTQ||Yeast ChIP-seq|
|Sample_Yeast_L005_R2.cat.fastq.gz||Paired-end Illumina, Second of pair, FASTQ||Yeast ChIP-seq|
|human_rnaseq.fastq.gz||Paired-end Illumina, First of pair only, FASTQ||Human RNA-seq|
|human_mirnaseq.fastq.gz||Single-end Illumina, FASTQ||Human microRNA-seq|
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 "
Do you believe that I gave you files of any reasonable quality? I wouldn't, so you should check it out.
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.
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):
|Reference||Species||Base Length||Contig Number||Source||Download Link|
|Hg19||Human||3,137,161,264||25 (really 93)||UCSC|
|Mm10||Mouse||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
|Option||Effect||Best Practice Setting|
Bowtie2 and Local Alignment - Human microRNA-seq
BWA-MEM (and Tophat2) - Human mRNA-seq