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 and Reference Genomes
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||Single-end Illumina, 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.
Do you believe that I gave you files of any reasonable quality? I wouldn't, so you should check it out.
BWA - Yeast ChIP-seq
Bowtie2 and Local Alignment - Human microRNA-seq
BWA-MEM (and Tophat2) - Human mRNA-seq