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 one a paired-end yeast ChIP-seq dataset, which we will continue to use here. In order to demonstrate how to process
BWA - The Most General Mapper
. 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