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. The paired end data should be located at:
We will also use two additional RNA-seq datasets, which are located at:
Set up a new directory in your scratch area called 'fastq_align', and populate it with copies the following files, derived from the locations given above:
|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|
Do a fast quality check on the two new data files like you did earlier on the yeast files, and move all files and directories that are produced from the fastQC commands into a new subdirectory called 'fastqc_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.
These are the three reference genomes we will be using today, with some information about them (and here is information about many more genomes):
|Reference||Species||Base Length||Contig Number||Source||Download|
|Hg19||Human||3.1 Gbp||25 (really 93)||UCSC||UCSC GoldenPath|
|SacCer3||Yeast||12.2 Mbp||17||UCSC||UCSC GoldenPath|
|MirbaseV20||Human||160 Kbp||1908||Mirbase||Mirbase Downloads|
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. Hg19 is way too big for us to index here, so we're not going to do it, and it's not included in the core_ngs_tools directory at Corral that we've been copying from. Instead, all hg19 index files are located at:
Now, we're going to grab the other two references, and we will build each index right before we use it in each set of exercise below. These two references are located at:
Now, stage the yeast and mirbase reference fasta files in your scratch area in a directory called 'references'.
With that, we're ready to get started on the first exercise.
Exercise #1: BWA - Yeast ChIP-seq
Like other tools you've worked with so far, you first need to load bwa using the module system. So, go ahead and do that now, and then enter 'bwa' with no arguments to view the help page.
As you can see, there are several commands one can use with bwa to do different things. To test out one of them, we're going to index the genome with the 'index' command. If you enter 'bwa index' with no arguments, you will see a list of the required arguments. Here, we only need to specify two things: whether to use 'bwtsw' or 'is' indexing, and the name of the fasta file. We will specify 'bwtsw' as the indexing option, and the name of the FASTA file is, obviously, sacCer3.fa. The output of this command, importantly, is a group of files that are all required together as the index. So, within the references directory, we will create another directory called "yeast", move the yeast FASTA file into that directory, and run the index commands from there:
This command should produce a set of output files that look like this:
Now, we're ready to execute the actual alignment, with the goal of producing a SAM/BAM file from the input FASTQ files and reference. We will first generate SAI files from each of the FASTQ files with the reference individually using the 'aln' command, then combine them (with the reference) into one SAM/BAM output file using the 'sampe' command. We need a directory to put the alignments when they are finished, as well as any intermediate files, so create a directory called 'alignments'. The command flow, all together, is as follows. Notice how each file is in its proper directory, which requires us to specify the whole file path in the alignment commands.
You did it! In the alignments directory, there should exist the two intermediate files (the SAI files), along with the SAM file that contains the alignments. It's just a text file, so take a look with head, more, less, tail, or whatever you feel like. In the next section, with samtools, you'll learn some additional ways to analyze the data. BWA, however, is a lot more complex than the above commands let on. If you look at the help pages for 'bwa aln' in particluar, there are numerous options that can increase the alignment rate (as well as decrease it), and all sorts of other things. There are lots of options, but here is a summary of the most important ones.
|-k||Controls the number of mismatches allowable in the seed of each alignment (default = 2)|
|-n||Controls the number of mismatches (or fraction of bases in a given alignment that can be mismatches) in the entire alignment (including the seed) (default = 0.04)|
|-l||Controls the length of the seed (default = 32)|
|-t||Controls the number of threads|
The rest of the options control the details of how much a mismatch or gap is penalized, limits on the number of acceptable hits per read, and so on. Much more information can be accessed at the BWA manual page.
Exercise #2: Bowtie2 and Local Alignment - Human microRNA-seq
Now we're going to switch over to a different aligner that was originally designed for very short reads, and is frequently used for RNA-seq data. Accordingly, we have prepared a test microRNA-seq dataset for you to experiment with. This data is derived from a human H1 embryonic stem cell (H1-hESC) small RNA dataset generated by the ENCODE Consortium (its about a half million reads). However, there is a problem! We don't know (or, well, you don't know) what the adapter structure or sequences were. So, you have a bunch of 36 base pair reads, but many of those reads will include extra sequence that will impede alignment! We need an aligner that can find subsections of the read that align, and discard (or "soft-clip") the rest away. Bowtie2 is just such an aligner. Go ahead and load it up so we can examine some help pages and options. To do that, you must first load the "perl" module, and then the bowtie2 module designated "bowtie/2.2.0".
Now that it's loaded, check out the options. There are a LOT of them! This is the key to using Bowtie2 - it allows you to control almost everything about its behavior, but that also makes it easier to screw things up. Before getting to using the tool, though, we've got to build a mirbase index. This requires using the command "bowtie2-build" - go ahead and check out its options. Unlike for the aligner itself, we only need to worry about a few things here:
Here, the reference_in file is just our FASTA file containing mirbase sequences, and the bt2_index_base is the prefix of where we want the files to go. Following what we did earlier for BWA indexing:
That was very fast! It's because the mirbase reference genome is so small compared to what programs like this are used to dealing with, which is the human genome (or bigger). Now, your $SCRATCH/references/mirbase directory should be filled with the following files:
Now, we're ready to actually try to do the alignment. Remember, unlike BWA, we actually need to set some options depending on what we're after. These are the most important options when using Bowtie2:
|-N||Controls the number of mismatches allowable in the seed of each alignment (default = 0)|
|-L||Controls the length of seed substrings generated from each read (default = 22)|
|--end-to-end or --local||Controls whether the entire read must align to the reference, or whether soft-clipping the ends is allowed to find internal alignments|
|-ma||Controls the alignment score contribution of a matching base (0 for --end-to-end, 2 for --local|
To decide how we want to go about doing our alignment, check out the file we're aligning with 'less'.
Lots of those reads have long strings of A's, which must be an adapter or protocol artifact. Even though we see how we might be able to fix it using some tools we've talked about, what if we had no idea what the adapter sequence was, or couldn't use cutadapt or other programs to prepare the reads? In that case, we need a local alignment where the seed length is the small boundary of the acceptable internal alignments. Here, we are interested in finding any sections of any reads that align well to a microRNA. These sequences are between 16 and 22 bases long, so any good alignment should have at least 16 matching bases, but could have more. Also, maybe we want to allow a mismatch or two in the seed, since we might be interested in miRNA SNPs. So, a good set of options might look something like this:
This leaves the default scoring method as "-ma 2", meaning that a 16 base pair alignment will have a score of 32, and so on. This is VERY different from the alignment scores assigned by other aligners, so it's worth remembering.
As you can tell from looking at the Bowtie2 help message, the syntax looks like this:
As such, our alignment command (now that we have the FASTQ file and the reference sequence ready) could be this (make sure you are located in your scratch directory!):
Now, you should have a human_mirnaseq.sam file in your alignments directory, that you can check out using whatever commands you like. An example alignment looks like this:
Note how the CIGAR string is 3S20M13S, meaning that 13 bases were soft clipped from one end, and 3 from the other. If we did the same alignment using either --end-to-end mode, or using BWA in the same way as we did in Exercise #1, very little of this file would have aligned. However, if we had not lowered the seed parameter of Bowtie2 from its default of 22, we would not have found many of the alignments like the one shown above, because the read only matched for 20 bases - a matching 22 base seed does not exist. Such is the nature of Bowtie2 - it can be a powerful tool to sift out the alignments you want from a messy dataset with limited information, but doing so requires careful tuning of the parameters, which can in itself take a lot of time to perfect.
Exercise #3: BWA-MEM (and Tophat2) - Human mRNA-seq
After Bowtie2 came out with a local alignment option, it wasn't long before BWA generated their own local-aligner called BWA-MEM (for Maximal Exact Matches). This aligner is very, very nice because it incorporates a lot of the simplicity of using BWA with the complexities of local alignment. This functionality, while enabling the alignment of datasets like the mirbase data we just examined, also permits more complex alignments, such as that of spliced mRNAs. In a long RNA-seq experiment, reads will (at some frequency) span a splice junction themselves, or a pair of reads in a paired-end library will fall on either side of a splice junction. We want to be able to align reads that do this for many reasons, from accurate transcript quantification to novel fusion transcript discovery. Thus, our last exercise will be the alignment of a human LONG RNA-seq dataset composed (by design) almost exclusively of reads that cross splice junctions.
BWA-MEM should have been loaded when we loaded the BWA module, so to look at the details of MEM alignment, just enter "bwa mem" to get the help menu with the options list. The most important parameters, similar to those we've manipulated in the past two sections, are the following:
|-k||Controls the minimum seed length (default = 19)|
|-w||Controls the "gap bandwidth", or the length of a maximum gap. This is particularly relevant for MEM, since it can determine whether a read is split into two separate alignments, or one long alignment with a long gap in the middle (default = 100)|
|-r||Controls how long an alignment must be relative to its seed before it is re-seeded to try to find a best-fit local match (default = 1.5, e.g. the value of -k multiplied by 1.5)|
|-c||Controls how many matches a MEM must have in the genome before it is discarded (default = 10000)|
|-t||Controls the number of threads to use|
There are many more parameters to control the scoring scheme and other details, but these are the most essential parameters to use to get anything of value at all.
The test file we will be working with is JUST the R1 file from a paired-end total RNA-seq experiment, meaning it is (for our purposes) single-end.