Connect to login8.stampede.tacc.utexas.edu. Sample Datasets

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1 Alignment Overview Connect to login8.stampede.tacc.utexas.edu Sample Datasets Reference Genomes Exercise #1: BWA global alignment Yeast ChIP-seq Overview ChIP-seq alignment workflow with BWA Introducing BWA Building the BWA saccer3 index Exploring the FASTA with grep Performing the bwa alignment Using cut to isolate fields Exercise #2: Bowtie2 global alignment - Vibrio cholerae RNA-seq Overview of Vibrio cholerae alignment workflow with Bowtie2 Obtaining the GenBank record(s) Converting GenBank records into sequence (FASTA) and annotation (GFF) files Introducing bowtie2 Building the bowtie2 vibcho index Performing the bowtie2 alignment Exercise #3: Bowtie2 local alignment - Human microrna-seq Overview mirna alignment workflow with bowtie2 Building the bowtie2 mirbase index Performing the bowtie2 local alignment Use sort and uniq to create a histogram of mapping qualities Exercise #4: BWA-MEM - Human mrna-seq A word about real splice-aware aligners RNA-seq alignment with bwa aln RNA-seq alignment with bwa mem BWA-MEM vs Tophat Exercise #5: Simple SAMtools Utilities Samtools view Samtools sort Samtools index Samtools idxstats Samtools flagstat Exercise #6: Yeast BWA PE alignment with Anna's script Output files Verifying alignment success Checking alignment statistics TACC batch system considerations Overview

2 After raw sequence files are generated (in FASTQ format), quality-checked, and pre-processed in some way, the next step in most NGS pipelines is mapping to a reference genome. For individual sequences, it is common to use a tool like BLAST to identify genes or species of origin. However, a normal NGS dataset will have tens to hundreds of millions of sequences, which BLAST and similar tools are not designed to handle. Thus, a large set of computational tools have been developed to quickly, and with sufficient (but not absolute - and this tradeoff is an important consideration when constructing alignment pipelines) 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. transcriptome-aware Tophat2) which also includes tools for manipulating NGS data after alignment. Connect to login8.stampede.tacc.utexas.edu This should be second nature by now Sample Datasets You have already worked with a paired-end yeast ChIP-seq dataset, which we will continue to use here. For the sake of uniformity, however, we will set up a new directory in your scratch area called 'alignment' and fill it with our sequencing data. To set up your scratch area properly and move into it, execute something like:

3 mkdir -p $SCRATCH/core_ngs/alignment cd $SCRATCH/core_ngs/alignment mkdir fastq "Permanent" location for original data Now you have created the alignment directory, moved into it, and created a subdirectory for our raw fastq files. We will be using four data sets that consist of five files (since the paired-end data set has two separate files for each of the R1 and R2 reads). To copy them over, execute something like: cd $SCRATCH/core_ngs/alignment/fastq cp /corral-repl/utexas/bioiteam/core_ngs_tools/alignment/*fastq.gz. We first moved ourselves into the fastq directory, then copied over all files that end in "fastq.gz" in the corral directory specified in the second line. These are descriptions of the five files we copied: File Name Description Sample 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 cholera_rnaseq.fastq.gz Single-end Illumina, FASTQ V. cholerae RNA-seq Reference Genomes Before we get to alignment, we need a genome to align to. We will use four different references here: the yeast genome ( saccer3) the human genome ( hg19) a Vibrio cholerae genome ( 0395; our name: vibcho) the microrna database mirbase ( v20), human subset NOTE: For the sake of simplicity, these are not necessarily the most recent versions of these references - for example, hg19 is the second most recent human genome, with the most recent called hg38. Similarly, the most recent mirbase annotation is v21. These are the four 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 mirbase V20 Human 160 Kbp 1908 Mirbase Mirbase Downloads vibcho (O395) V. cholerae ~4 Mbp 2 GenBank GenBank Downloads Searching genomes is computationally hard work and takes a long time if done on un-indexed, linear genomic sequence. So aligners require that references first be indexed to accelerate later retrieval. The aligners we are using each require a different index, but use the same method (the B urrows-wheeler Transform) to get the job done. This involves taking a FASTA file as input, with each chromosome (or contig) as a separate FASTA entry, and producing an aligner-specific set of files as output. Those output index files are then used by the aligner when performing the sequence alignment, and subsequent alignments are reported using coordinates referencing the original FASTA reference files. hg19 is way too big for us to index here, so we're not going to do it (especially not all at the same time!). Instead, we will "point" to an existing set of hg19 index files, which are all located at:

4 BWA hg19 index location /scratch/01063/abattenh/ref_genome/bwa/bwtsw/hg19 We can index the references for the yeast genome, the human mirnas, and the V. cholerae genome, because they are all tiny compared to the human genome. We will grab the FASTA files for yeast and human mirnas two references and build each index right before we use them. We will also grab the special file that contains the V. cholerae genome sequence and annotations (a.gbk file), and generate the reference FASTA and some other interesting information when we get to that exercise. These references are currently at the following locations: Yeast and mirbase FASTA locations /corral-repl/utexas/bioiteam/core_ngs_tools/references/saccer3.fa /corral-repl/utexas/bioiteam/core_ngs_tools/references/hairpin_cdna_hsa.fa /corral-repl/utexas/bioiteam/core_ngs_tools/references/vibcho.o395.gbk First stage all the reference files in your $WORK core_ngs area in a directory called references. We will add further structure to this directory later on in specific exercises, but for now the following will suffice: Stage FASTA references mkdir -p $WORK/core_ngs/references cp $CLASSDIR/references/* $WORK/core_ngs/references $CLASSDIR = /corral-repl/utexas/bioiteam/core_ngs_tools With that, we're ready to get started on the first exercise. Exercise #1: BWA global alignment Yeast ChIP-seq Overview ChIP-seq alignment workflow with BWA We will perform a global alignment of the paired-end Yeast ChIP-seq sequences using bwa. This workflow generally has the following steps: 1. Trim the FASTQ sequences down to 50 with fastx_clipper this removes most of any 5' adapter contamination without the fuss of specific adapter trimming w/cutadapt 2. Prepare the saccer3 reference index for bwa using bwa index this is done once, and re-used for later alignments 3. Perform a global bwa alignment on the R1 reads ( bwa aln ) producing a BWA-specific binary.sai intermediate file 4. Perform a global bwa alignment on the R2 reads ( bwa aln ) producing a BWA-specific binary.sai intermediate file 5. Perform pairing of the separately aligned reads and report the alignments in SAM format using bwa sampe 6. Convert the SAM file to a BAM file ( samtools view) 7. Sort the BAM file by genomic location ( samtools sort) 8. Index the BAM file ( samtools index) 9. Gather simple alignment statistics ( samtools flagstat and samtools idxstat) We're going to skip the trimming step for now and see how it goes. We'll perform steps 2-5 now and leave samtools for the next course section, since those steps (6-10) are common to nearly all post-alignment workflows. Introducing BWA Like other tools you've worked with so far, you first need to load bwa using the module system. Go ahead and do that now, and then enter bwa wi th no arguments to view the top-level help page (many NGS tools will provide some help when called with no arguments).

5 module load bwa bwa Top-level BWA help Program: bwa (alignment via Burrows-Wheeler transformation) Version: r1039 Contact: Heng Li Usage: bwa <command> [options] Command: index index sequences in the FASTA format mem BWA-MEM algorithm fastmap identify super-maximal exact matches pemerge merge overlapping paired ends (EXPERIMENTAL) aln gapped/ungapped alignment samse generate alignment (single ended) sampe generate alignment (paired ended) bwasw BWA-SW for long queries shm fa2pac pac2bwt pac2bwtgen bwtupdate bwt2sa manage indices in shared memory convert FASTA to PAC format generate BWT from PAC alternative algorithm for generating BWT update.bwt to the new format generate SA from BWT and Occ Note: To use BWA, you need to first index the genome with `bwa index'. There are three alignment algorithms in BWA: `mem', `bwasw', and `aln/samse/sampe'. If you are not sure which to use, try `bwa mem' first. Please `man./bwa.1' for the manual. As you can see, bwa include many sub-commands that perform most of the tasks we are interested in. Building the BWA saccer3 index We're going to index the genome with the index command. To learn what this sub-command needs in the way of options and arguments, enter b wa index with no arguments. Usage: bwa index [-a bwtsw is] [-c] <in.fasta> Options: -a STR BWT construction algorithm: bwtsw or is [auto] -p STR prefix of the index [same as fasta name] -b INT block size for the bwtsw algorithm (effective with -a bwtsw) [ ] -6 index files named as <in.fasta>.64.* instead of <in.fasta>.* Warning: `-a bwtsw' does not work for short genomes, while `-a is' and `-a div' do not work not for long genomes. Please choose `-a' according to the length of the genome. Based on the "Usage" description, we only need to specify two things: the name of the FASTA file

6 whether to use the bwtsw or is algorithm for indexing Since saccer3 is relative large (~12 Mbp) we will specify bwtsw as the indexing option (as indicated by the "Warning" message), and the name of the FASTA file is saccer3.fa. Importantly, the output of this command is a group of files that are all required together as the index. So, within the references directory, we will create another directory called bwa/saccer3, make a symbolic link to the yeast FASTA there, and run the index command in that directory. So, first we will create a directory called fasta to hold our reference FASTA file, then create the bwa/saccer3 directory, and construct the symbolic links. Prepare BWA reference directory for saccer3 mkdir -p $WORK/core_ngs/references/bwa/sacCer3 mkdir -p $WORK/core_ngs/references/fasta mv $WORK/core_ngs/references/sacCer3.fa $WORK/core_ngs/references/fasta cd $WORK/core_ngs/references/bwa/sacCer3 ln -s../../fasta/saccer3.fa ls -la Now execute the bwa index command. bwa index -a bwtsw saccer3.fa Build BWA index for saccer3 Since the yeast genome is not large when compared to human, this should not take long to execute (otherwise we would do it as a batch job). When it is complete you should see a set of index files like this: saccer3.fa saccer3.fa.amb saccer3.fa.ann saccer3.fa.bwt saccer3.fa.pac saccer3.fa.sa BWA index files for saccer3 Exploring the FASTA with grep It is frequently useful to have a list of all contigs/chromosomes/genes/features in a file. You'll usually want to know this before you start the alignment so that you're familiar with the contig naming convention and to verify that it's the one you expect. For example, chromosome 1 is specified as "chr1", "1", "I", and more in different references, and it can get weird for non-model organisms. We saw that a FASTA consists of a number of contig entries, each one starting with a name line of the form below, followed by many lines of bases. >contigname How do we dig out just the lines that have the contig names and ignore all the sequences? Well, the contig name lines all follow the pattern above, and since the > character is not a valid base, it will never appear on a sequence line. We've discovered a pattern (also known as a regular expression) to use in searching, and the command line tool that does regular expression matching is grep. Regular expressions are so powerful that nearly every modern computer language includes a "regex" module of some sort. There are many online tutorials for regular expressions (and a few different flavors of them). But the most common is the Perl style ( We're only going to use the most simple of regular expressions here, but learning more about them will pay handsome dividends for you in

7 the future (there's a reason Perl was used a lot when assembling the human genome). Here's how to execute grep to list contig names in a FASTA file. grep -P '^>' saccer3.fa more grep to match contig names in a FASTA file Notes: The -P option tells grep to use Perl-style regular expression patterns. This makes including special characters like Tab ( \t ), Carriage Return ( \r ) or Linefeed ( \n ) much easier that the default Posix paterns. While it is not really required here, it generally doesn't hurt to include this option. '^>' is the regular expression describing the pattern we're looking for (described below) saccer3.fa is the file to search. Lines with text that match our pattern will be written to standard output; non matching lines will be omitted. We pipe to more just in case there are a lot of contig names. Now down to the nuts and bolts of our pattern, '^>' First, the single quotes around the pattern they are only a signal for the bash shell. As part of its friendly command line parsing and evaluation, the shell will often look for special characters on the command line that mean something to it (for example, the $ in front of an environment variable name, like in $SCRATCH ). Well, regular expressions treat the $ specially too but in a completely different way! Those single quotes te ll the shell "don't look inside here for special characters treat this as a literal string and pass it to the program". The shell will obey, will strip the single quotes off the string, and will pass the actual pattern, ^>, to the grep program. (Aside: We've see that the shell does look inside double quotes ( " ) for certain special signals, such as looking for environment variable names to evaluate.) So what does ^> mean to grep? Well, from our contig name format description above we see that contig name lines always start with a > charact er, so > is a literal for grep to use in its pattern match. We might be able to get away with just using this literal alone as our regex, specifying '>' as the command line argument. But for grep, the more specific the pattern, the better. So we constrain where the > can appear on the line. The special carat ( ^ ) character represents "beginning of line". So ^> means "beginning of a line followed by a > character, followed by anything. (Aside: the dollar sign ( $ ) character represents "end of line" in a regex. There are many other special characters, including period (. ), question mark (? ), pipe ( ), parentheses ( ( ) ), and brackets ( [ ] ), to name the most common.) Exercise: How many contigs are there in the saccer3 reference? Hint grep -P '^>' saccer3.fa wc -l Or use grep's -c option that says "just count the line matches" grep -P -c '^>' saccer3.fa Answer There are 17 contigs. Performing the bwa alignment Now, we're ready to execute the actual alignment, with the goal of initially producing a SAM file from the input FASTQ files and reference. First go to the align directory, and link to the saccer3 reference directory (this will make our commands more readable).

8 Prepare to align yeast data cd $SCRATCH/core_ngs/alignment ln -s $WORK/core_ngs/references/bwa/sacCer3 ls As our workflow indicated, we first use bwa aln on the R1 and R2 FASTQs, producing a BWA-specific.sai intermediate binary files. Since these alignments are completely independent, we can execute them in parallel in a batch job. What does bwa aln needs in the way of arguments? Hint bwa aln There are lots of options, but here is a summary of the most important ones. BWA, is a lot more complex than the options let on. If you look at the BWA manual on the web for the aln sub-command, you'll see numerous options that can increase the alignment rate (as well as decrease it), and all sorts of other things. Option Effect -l Controls the length of the seed (default = 32) -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) -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. For a simple alignment like this, we can just go with the default alignment parameters. Also note that bwa writes its (binary) output to standard output by default, so we need to redirect that to a.sai file. For simplicity, we will just execute these commands directly, one at a time. (We can do this since we're on the special login8 head node!) Each command you execute should only take a minute or so. bwa aln commands for yeast R1 and R2 bwa aln saccer3/saccer3.fa fastq/sample_yeast_l005_r1.cat.fastq.gz > yeast_r1.sai bwa aln saccer3/saccer3.fa fastq/sample_yeast_l005_r2.cat.fastq.gz > yeast_r2.sai When all is done you should have two.sai files. How you would use the batch system for these commands Here's how you would use the TACC batch system for these commands. For a simple alignment like this, we can just go with the default alignment parameters, with one exception. At TACC, we want to optimize our alignment speed by allocating more than one thread (-t) to the alignment. We want to run 2 tasks, and will use a minimum of one 16-core node. So we can assign 8 cores to each alignment by specifying -t 8. Create an aln.cmds file (using nano) with the following lines. Here we redirect standard error to a log file, one per file.

9 bwa aln commands for yeast R1 and R2 bwa aln -t 8 saccer3/saccer3.fa fastq/sample_yeast_l005_r1.cat.fastq.gz > yeast_r1.sai 2> aln.yeast_r1.log bwa aln -t 8 saccer3/saccer3.fa fastq/sample_yeast_l005_r2.cat.fastq.gz > yeast_r2.sai 2> aln.yeast_r2.log Create the batch submission script specifying a wayness of 2 (2 tasks per node) on the normal queue and a time of 10 minutes, then submit the job and monitor the queue: Submit BWA alignment job launcher_creator.py -n aln -j aln.cmds -t 00:10:00 -q normal -w 2 sbatch aln.slurm showq -u Since you have directed standard error to log files, you can use a neat trick to monitor the progress of the alignment: tail -f. The -f means " fo llow" the tail, so new lines at the end of the file are displayed as they are added to the file. Monitor alignment progress with tail -f # Use Ctrl-c to stop the output any time tail -f aln.yeast_r1.log Next we use the bwa sampe command to pair the reads and output SAM format data. Just type that command in with no arguments to see its usage. For this command you provide the same reference index prefix as for bwa aln, along with the two.sai files and the two original FASTQ files. Also, bwa writes its output to standard output, so redirect that to a.sam file. Here is the command line statement you need. Just execute it on the command line. BWA global alignment of R1 reads bwa sampe saccer3/saccer3.fa yeast_r1.sai yeast_r2.sai fastq/sample_yeast_l005_r1.cat.fastq.gz fastq/sample_yeast_l005_r2.cat.fastq.gz > yeast_pairedend.sam You did it! You should now have a 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 once you create a BAM file. Exercise: What kind of information is in the first lines of the SAM file? Answer The SAM or BAM has a number of header lines, which all start with an at sign ). lines describe each contig and its length. There is also line that describes the way the bwa sampe was performed. Exercise: How many alignment records (not header records) are in the SAM file? Hint This looks for the pattern '^HWI' which is the start of every read name (which starts every alignment record). Remember -c says just count the records, don't display them. grep -P -c '^HWI' yeast_pairedend.sam Or use the -v (inv ert) option to tell grep to print all lines that don't match a particular pattern, here the header lines starting

10 grep -P -v -c yeast_pairedend.sam Answer There are 1,184,360 alignment records. Exercise: How many sequences were in the R1 and R2 FASTQ files combined? Hint gunzip -c fastq/sample_yeast_l005_r1.cat.fastq.gz echo $((`wc -l` / 2)) Answer There were a total of 1,184,360 original sequences Exercises: Do both R1 and R2 reads have separate alignment records? Does the SAM file contain both aligned and un-aligned reads? What is the order of the alignment records in this SAM file? Answers Do both R1 and R2 reads have separate alignment records? yes, they must, because there were 1,184,360 R1+R2 reads and an equal number of alignment records Does the SAM file contain both aligned and un-aligned reads? yes, it must, because there were 1,184,360 R1+R2 reads and an equal number of alignment records What is the order of the alignment records in this SAM file? the names occur in the exact same order as they did in the FASTQ, except that they come in pairs the R1 read comes first, then its corresponding R2 this ordering is called read name ordering Using cut to isolate fields Recall the format of a SAM/BAM file alignment record: Suppose you wanted to look only at field 3 (contig name) values in the SAM file. You can do this with the handy cut command. Below is a simple example where you're asking cut to display the 3rd column value for the last 10 alignment records. tail yeast_pairedend.sam cut -f 3 Cut syntax for a single field By default cut assumes the field delimiter is Tab, which is the delimiter used in the majority of NGS file formats. You can, of course, specify a different delimiter with the -d option. You can also specify a range of fields, and mix adjacent and non-adjacent fields. This displays fields 2 through 6, field 9, and all fields starting with 12 (SAM tag fields).

11 Cut syntax for multiple fields tail yeast_pairedend.sam cut -f 2-6,9,12- You may have noticed that some alignment records contain contig names (e.g. chrv ) in field 3 while others contain an asterisk ( * ). Usually the * means the record didn't align. (This isn't always true later you'll see how to properly distinguish between mapped and unmapped reads using sa mtools.) We're going to use this heuristic along with cut to see about how many records represent aligned sequences. First we need to make sure that we don't look at fields in the SAM header lines. We're going to end up with a series of pipe operations, and the best way to make sure you're on track is to enter them one at a time piping to head: Grep pattern that doesn't match header # the ^HWI pattern matches lines starting with HWI (the start of all read names in column 1) grep -P '^HWI' yeast_pairedend.sam head Ok, it looks like we're seeing only alignment records. Now let's pull out only field 3 using cut: Get contig name info with cut grep -P '^HWI' yeast_pairedend.sam cut -f 3 head Cool, we're only seeing the contig name info now. Next we use grep again, piping it our contig info and using the -v (invert) switch to say print lines that don't match the pattern: Filter contig name of * (unaligned) grep -P '^HWI' yeast_pairedend.sam cut -f 3 grep -v '*' head Perfect! We're only seeing real contig names that (usually) represent aligned reads. Let's count them by piping to wc -l (and omitting omit head of course we want to count everything). Count aligned SAM records grep -P '^HWI' yeast_pairedend.sam cut -f 3 grep -v '*' wc -l Exercise: About how many records represent aligned sequences? What alignment rate does this represent? Answer The expression above returns 612,968. There were 1,184,360 records total, so the percentage is: echo $(( * 100/ )) Calculate alignment rate or about 51%. Not great. Exercise: What might we try in order to improve the alignment rate? Answer Recall that these are 100 bp reads and we did not remove adapter contamination. There will be a distribution of fragment sizes some will be short and those short fragments may not align without adapter removal ( fastx_trimmer or cutadapt).

12 Exercise #2: Bowtie2 global alignment - Vibrio cholerae RNA-seq While we have focused on aligning eukaryotic data, the same tools can be used to perform identical functions with prokaryotic data. The major differences are less about the underlying data and much more about the external/public databases established to store and distribute reference data. For example, the Illumina igenome resource provides pre-processed and uniform reference data, designed to be out-of-the-box compatible with aligners like bowtie2 and bwa. However, the limited number of available species are heavily biased towards model eukaryotes. If we wanted to study a prokaryote, the reference data must be downloaded from a resource like GenBank, and processed/indexed similarly to the procedure for mirbase. While the alignment procedure for prokaryotes is broadly analogous, the reference preparation process is somewhat different, and will involve use of a biologically-oriented scripting library called BioPerl. In this exercise, we will use some RNA-seq data from Vibrio cholerae, published last year on GEO here, and align it to a reference genome. Overview of Vibrio cholerae alignment workflow with Bowtie2 Alignment of this prokaryotic data follows the workflow below. Here we will concentrate on steps 1 and Prepare the vibcho reference index for bowtie2 from a GenBank record using BioPerl Align reads using bowtie2, producing a SAM file Convert the SAM file to a BAM file (samtools view) Sort the BAM file by genomic location ( samtools sort) Index the BAM file ( samtools index) Gather simple alignment statistics ( samtools flagstat and samtools idxstat) Obtaining the GenBank record(s) V. cholerae has two chromosomes. We download each separately. 1. Navigate to click on the Send down arrow (top right of page) select Complete Record select Clipboard as Destination click Add to Clipboard 2. Perform these steps in your Terminal window mkdir $WORK/tmp; cd $WORK/tmp type wget then paste the URL from the clipboard should be 3. Repeat steps 1 and 2 fot the 2nd chromosome NCBI URL is you should now have 2 files, NC_ and NC_ Combine the 2 files into one using cat cat NC_ NC_ > vibcho.gbk Converting GenBank records into sequence (FASTA) and annotation (GFF) files As noted earlier, many microbial genomes are available through repositories like GenBank that use specific file format conventions for storage and distribution of genome sequence and annotations. The GenBank file format is a text file that can be parsed to yield other files that are compatible with the pipelines we have been implementing. Go ahead and look at some of the contents of a GenBank file with the following commands (execute these one at a time): cd $WORK/core_ngs/references less vibcho.o395.gbk # use q to quit less grep -A 5 ORIGIN vibcho.o395.gbk As the less command shows, the file begins with a description of the organism and some source information, and the contains annotations for each bacterial gene. The grep command shows that, indeed, there is sequence information here (flagged by the word ORIGIN) that could be exported into a FASTA file. There are a couple ways of extracting the information we want, namely the reference genome and the gene annotation information, but a convenient one (that is available through the module system at TACC) is BioPerl. We load BioPerl like we have loaded other modules, with the caveat that we must load regular Perl before loading BioPerl:

13 module load perl module load bioperl These commands make several scripts directly available to you. The one we will use is called bp_seqconvert.pl, and it is a BioPerl script used to inter-convert file formats like FASTA, GBK, and others. This script produces two output files: a FASTA format file for indexing and alignment a GFF file (standing for General Feature Format) contains information about all genes (or, more generally, features) in the genome remember, annotations such as GFFs must always match the reference you are using To see how to use the script, just execute: bp_seqconvert.pl Clearly, there are many file formats that we can use this script to convert. In our case, we are moving from genbank to fasta, so the commands we would execute to produce and view the FASTA files would look like this: cd $WORK/core_ngs/references bp_seqconvert.pl --from genbank --to fasta < vibcho.o395.gbk > vibcho.o395.fa mv vibcho.o395.fa fasta/ grep ">" fasta/vibcho.o395.fa less fasta/vibcho.o395.fa Now we have a reference sequence file that we can use with the bowtie2 reference builder, and ultimately align sequence data against. Recall from when we viewed the GenBank file that there are genome annotations available as well that we would like to extract into GFF format. However, the bp_seqconvert.pl script is designed to be used to convert sequence formats, not annotation formats. Fortunately, there is another script called bp_genbank2gff3.pl that can take a GenBank file and produce a GFF3 (the most recent format convention for GFF files) file. To run it and see the output, run these commands: bp_genbank2gff3.pl --format Genbank vibcho.o395.gbk mv vibcho.o395.gbk.gff vibcho.o395.gff less vibcho.o395.gff After the header lines, each feature in the genome is represented by a line that gives chromosome, start, stop, strand, and other information. Features are things like "mrna," "CDS," and "EXON." As you would expect in a prokaryotic genome it is frequently the case that the gene, mrna, CDS, and exon annotations are identical, meaning they share coordinate information. You could parse these files further using commands like grep and awk to extract, say, all exons from the full file or to remove the header lines that begin with #. Introducing bowtie2 Go ahead and load the bowtie2 module so we can examine some help pages and options. To do that, you must first load the perl module, and then the a specific version of bowtie2. module load perl module load bowtie/2.2.0 Now that it's loaded, check out the options. There are a lot of them! In fact for the full range of options and their meaning, Google "Bowtie2 manual" and bring up that page. The Table of Contents is several pages long! Ouch! This is the key to using bowtie2 - it allows you to control almost everything about its behavior, but that also makes it is much more challenging to use than bwa and it's easier to screw things up too!

14 Building the bowtie2 vibcho index Before the alignment, of course, we've got to build a mirbase index using bowtie2-build (go ahead and check out its options). Unlike for the aligner itself, we only need to worry about a few things here: reference_in file is just the FASTA file containing mirbase v20 sequences bt2_index_base is the prefix of where we want the files to go To build the reference index for alignment, we actually only need the FASTA file, since annotations are often not necessary for alignment. (This is not always true - extensively spliced transcriptomes requires splice junction annotations to align RNA-seq data properly, but for now we will only use the FASTA file.) mkdir -p $WORK/core_ngs/references/bt2/vibCho mv $WORK/core_ngs/references/vibCho.O395.fa $WORK/core_ngs/references/fasta cd $WORK/core_ngs/references/bt2/vibCho ln -s -f../../fasta/vibcho.o395.fa ls -la Now build the index using bowtie2-build: Prepare Bowtie2 index files bowtie2-build vibcho.o395.fa vibcho.o395 This should also go pretty fast. You can see the resulting files using ls like before. Performing the bowtie2 alignment Now we will go back to our scratch area to do the alignment, and set up symbolic links to the index in the work area to simplify the alignment command: cd $SCRATCH/core_ngs/alignment ln -s -f $WORK/core_ngs/references/bt2/vibCho vibcho Note that here the data is from standard mrna sequencing, meaning that the DNA fragments are typically longer than the reads. There is likely to be very little contamination that would require using a local rather than global alignment, or many other pre-processing steps (e.g. adapter trimming). Thus, we will run bowtie2 with default parameters, omitting options other than the input, output, and reference index. As you can tell from looking at the bowtie2 help message, the general syntax looks like this: bowtie2 [options]* -x <bt2-idx> {-1 <m1> -2 <m2> -U <r>} [-S <sam>] So our command would look like this: bowtie2 -x vibcho/vibcho.o395 -U fastq/cholera_rnaseq.fastq.gz -S cholera_rnaseq.sam What's going on? Parameters are: -x vibcho/vibcho.o395.fa prefix path of index files -U fastq/cholera_rnaseq.fastq.gz FASTQ file for single-end ( Unpaired) alignment -S cholera_rnaseq.sam tells bowtie2 to report alignments in SAM format to the specified file

15 Create a commands file called bt2_vibcho.cmds with this task definition then generate and submit a batch job for it (time 1 hour, development queue). What's going on? Use nano to create the bt2_vibcho.cmds file. Then: Local bowti2 alignment of mirna data launcher_creator.py -n bt2_vibcho -j bt2_vibcho.cmds -t 01:00:00 -A UT sbatch bt2_vibcho.slurm; showq -u When the job is complete you should have a cholera_rnaseq.sam file that you can examine using whatever commands you like. Exercise #3: Bowtie2 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 another test microrna-seq dataset for you to experiment with (not the same one you used cutadapt on). 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 can impede alignment and we don't know where! We need an aligner that can find subsections of the read that do align, and discard (or "soft-clip") the rest away an aligner with a local alignment mode. Bowtie2 is just such an aligner. Overview mirna alignment workflow with bowtie2 If the adapter structure were known, the normal workflow would be to first remove the adapter sequences with cutadapt. Since we can't do that, we will instead perform a local alignment of the single-end mirna sequences using bowtie2. This workflow has the following steps: Prepare the mirbase v20 reference index for bowtie2 (one time) using bowtie2-build Perform local alignment of the R1 reads with bowtie2, producing a SAM file directly Convert the SAM file to a BAM file ( samtools view) Sort the BAM file by genomic location ( samtools sort) Index the BAM file ( samtools index) Gather simple alignment statistics ( samtools flagstat and samtools idxstat) This looks so much simpler than bwa only one alignment step instead of three! We'll see the price for this "simplicity" in a moment... As before, we will just do the alignment steps leave samtools for the next section. Mirbase is a collection of all known micrornas in all species (and many speculative mirnas). 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 likely containing almost all sequences likely to be detected in a mirna sequencing run. If it's simpler and faster, would one ever want to align a mirna dataset to hg19 rather than mirbase? If so, why? 1. 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. 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. This is a major complication involved when determining, for example, whether a potential mirna is produced from a repetitive region. 2. If we suspect our library contained other RNA species, we may want to quantify the level of "contamination". Aligning to the human genome will allow rrna, trna, snorna, etc to align. We can then use programs such as bedtools, coupled with appropriate genome annotation files, to quantify these "off-target" hits. This is particularly plausible if, after a mirna sequencing run, the alignment rate to mirbase is very low. These are the four reference genomes we will be using today, with some information about them (and here is information about many more genomes): Building the bowtie2 mirbase index Before the alignment, of course, we've got to build a mirbase index using bowtie2-build (go ahead and check out its options). Unlike for the aligner itself, we only need to worry about a few things here:

16 bowtie2-build <reference_in> <bt2_index_base> reference_in file is just the FASTA file containing mirbase v20 sequences bt2_index_base is the prefix of where we want the files to go Following what we did earlier for BWA indexing, namely move our FASTA into place, create the index directory, and establish our symbolic links. Prepare Bowtie2 index directory for mirbase mkdir -p $WORK/core_ngs/references/bt2/mirbase.v20 mv $WORK/core_ngs/references/hairpin_cDNA_hsa.fa $WORK/core_ngs/references/fasta cd $WORK/core_ngs/references/bt2/mirbase.v20 ln -s -f../../fasta/hairpin_cdna_hsa.fa ls -la module load perl module load bowtie/2.2.0 Now build the mirbase index with bowtie2-build l ike we did for the V. cholerae index: Prepare Bowtie2 index directory for mirbase bowtie2-build hairpin_cdna_hsa.fa hairpin_cdna_hsa.fa 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). You should see the following files: hairpin_cdna_hsa.fa hairpin_cdna_hsa.fa.1.bt2 hairpin_cdna_hsa.fa.2.bt2 hairpin_cdna_hsa.fa.3.bt2 hairpin_cdna_hsa.fa.4.bt2 hairpin_cdna_hsa.fa.rev.1.bt2 hairpin_cdna_hsa.fa.rev.2.bt2 bowtie2 index files for mirnas Performing the bowtie2 local alignment 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. Some of the important options for bowtie2 are: Option --end-to-end or --local Effect Controls whether the entire read must align to the reference, or whether soft-clipping the ends is allowed to find internal alignments. Default --end-to-end -L Controls the length of seed substrings generated from each read (default = 22) -N Controls the number of mismatches allowable in the seed of each alignment (default = 0)

17 -i Interval between extracted seeds. Default is a function of read length and alignment mode. --score-min Minimum alignment score for reporting alignments. Default is a function of read length and alignment mode. To decide how we want to go about doing our alignment, check out the file we're aligning with less: cd $SCRATCH/core_ngs/alignment less fastq/human_mirnaseq.fastq.gz Examine mirna sequences to be aligned Lots of 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 smaller than the expected insert size. Here, we are interested in finding any sections of any reads that align well to a microrna, which are between 16 and 24 bases long, with most So an acceptable alignment should have at least 16 matching bases, but could have more. If we're also interested in detecting mirna SNPs, we might want to allow a mismatch in the seed. So, a good set of options might look something like this: -N 1 -L 16 --local If our reads were longer Because these are short reads we do not have to adjust parameters like inter-seed distance (-i) or minimum alignment score (--min-score) that are a function of read length. If we were processing longer reads, we might need to use parameters like this, to force bowtie2 to "pretend" the read is a short, constant length: -i C,1,0 --score-min=c,32,0 Yes, that looks complicated, and it kind of is. It's basically saying "slide the seed down the read one base at a time", and "report alignments as long as they have a minimum alignment score of 32 (16 matching bases x 2 points per match, minimum). See the bowtie2 manual (after you have had a good stiff drink) for a full explanation. Let's make a link to the mirbase index directory to make our command line simpler: Link to mirbase index for bowtie2 cd $SCRATCH/core_ngs/alignment ln -s -f $WORK/core_ngs/references/bt2/mirbase.v20 mb20 Putting this all together we have a command line that looks like this. Local bowti2 alignment of mirna data bowtie2 --local -N 1 -L 16 -x mb20/hairpin_cdna_hsa.fa -U fastq/human_mirnaseq.fastq.gz -S human_mirnaseq.sam What's going on? Parameters are: --local local alignment mode

18 -L 16 seed length 16 -N 1 allow 1 mismatch in the seed -x mb20/hairpin_cdna_hsa.fa prefix path of index files -U fastq/human_mirnaseq.fastq.gz FASTQ file for single-end ( Unpaired) alignment -S human_mirnaseq.sam tells bowtie2 to report alignments in SAM format to the specified file Create a commands file called bt2.cmds with this task definition then generate and submit a batch job for it (time 1 hour, development queue). Use nano to create the bt2.cmds file. Then: What's going on? Local bowti2 alignment of mirna data launcher_creator.py -n bt2 -j bt2.cmds -t 01:00:00 -A UT sbatch bt2.slurm; showq -u #copy from Amelia's scratch: cp /scratch/01786/awh394/core_ngs.test/alignment/human_mirnaseq.sam. When the job is complete you should have a human_mirnaseq.sam file that you can examine using whatever commands you like. An example alignment looks like this. Example mirna alignment record TUPAC_0037_FC62EE7AAXX:2:1:2607:1430#0/1 0 hsa-mir-302b S20M13S * 0 0 TACGTGCTTCCATGTTTTANTAGAAAAAAAAAAAAG ZZFQV]Z[\IacaWc]RZIBVGSHL_b[XQQcXQcc AS:i:37 XN:i:0 XM:i:1 XO:i:0 XG:i:0 NM:i:1 MD:Z:16G3 YT:Z:UU Notes: This is one alignment record, although it has been broken up below for readability. This read mapped to the mature microrna sequence hsa-mir-302b, starting at base 50 in that contig. Notice the CIGAR string is 3S20M13S, meaning that 3 bases were soft clipped from one end ( 3S), and 13 from the other ( 13S). If we did the same alignment using either bowtie2 --end-to-end mode, or using bwa aln as in Exercise #1, very little of this file would have aligned. The 20M part of the CIGAR string says there was a block of 20 read bases that mapped to the reference. 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 this one that only matched for 20 bases. Such is the nature of bowtie2 it 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 take quite a few trials to figure out. Exercise: About how many records in human_mirnaseq.sam represent aligned reads? Solution We can use our cut / grep trick from Exercise #1, but on the human_mirnaseq.sam file. Since all read names in this file start with TUPAC, we'll use that pattern to select non-header lines. Count aligned SAM records grep -P -v '^@' human_mirnaseq.sam cut -f 3 grep -v '*' wc -l This expressions returns Use sort and uniq to create a histogram of mapping qualities The mapping quality score is in field 5 of the human_mirnaseq.sam file. We can do this to pull out only that field:

19 Cut mapping quality SAM field grep -P -v human_mirnaseq.sam cut -f 5 head We will use the uniq create a histogram of these values. The first part of the --help for uniq says: What uniq does Usage: uniq [OPTION]... [INPUT [OUTPUT]] Filter adjacent matching lines from INPUT (or standard input), writing to OUTPUT (or standard output). With no options, matching lines are merged to the first occurrence. Mandatory arguments to long options are mandatory for short options too. -c, --count prefix lines by the number of occurrences To create a histogram, we want to organize all equal mapping quality score lines into an adjacent block, then use uniq -c option to count them. The sort -n command does the sorting into blocks (-n means numerical sort). So putting it all together, and piping the output to a pager just in case, we get: Cut mapping quality SAM field grep -P -v '^@' human_mirnaseq.sam cut -f 5 sort -n uniq -c more Exercise: What is the flaw in this "program"? Answer We are looking at mapping quality values for both aligned and un-aligned records, but mapping quality only makes sense for aligned reads. This expression does not distinguish between mapping quality = 0 because the read mapped to multiple locations, and mapping quality = 0 because the sequence did not align. The proper solution will await the use of samtools to filter out unmapped reads. Exercise #4: BWA-MEM - Human mrna-seq After bowtie2 came out with a local alignment option, it wasn't long before bwa developed its own local alignment algorithm called BWA-MEM (for Maximal Exact Matches), implemented by the bwa mem command. bwa mem has the following advantages: It incorporates a lot of the simplicity of using bwa with the complexities of local alignment, enabling straightforward alignment of datasets like the mirbase data we just examined It can align different portions of a read to different locations on the genome 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 was made available when we loaded the bwa module, so take a look at its usage information. The most important parameters, similar to those we've manipulated in the past two sections, are the following: Option Effect -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 is reported as one long alignment with a long gap in the middle (default = 100)

20 -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 ones 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. Go ahead and take a look at it, and find out how many reads are in the file. Hint: cd $SCRATCH/core_ngs/alignment ls fastq gunzip -c fastq/human_rnaseq.fastq.gz echo $((`wc -l`/4)) A word about real splice-aware aligners Using BWA mem for RNA-seq alignment is sort of a "poor man's" RNA-seq alignment method. Real splice-aware aligners like tophat2 or star have more complex algorithms (as shown below) and take a lot more time!

21 RNA-seq alignment with bwa aln Now, try aligning it with bwa aln like we did in Example #1, but first link to the hg19 bwa index directory. In this case, due to the size of the hg19 index, we are linking to Anna's scratch area INSTEAD of our own work area containing indexes that we built ourselves. Link to BWA hg19 index directory cd $SCRATCH/core_ngs/alignment ln -s -f /scratch/01063/abattenh/ref_genome/bwa/bwtsw/hg19 ls hg19 You should see a set of files analogous to the yeast files we created earlier, except that their universal prefix is hg19.fa. Go ahead and try to do a single-end alignment of the file to the human genome using bwa aln like we did in Exercise #1, saving intermediate files with the prefix human_rnaseq_bwa. Go ahead and just execute on the command line. Commands to bwa aln RNA-seq data bwa aln hg19/hg19.fa fastq/human_rnaseq.fastq.gz > human_rnaseq_bwa.sai bwa samse hg19/hg19.fa human_rnaseq_bwa.sai fastq/human_rnaseq.fastq.gz > human_rnaseq_bwa.sam Once this is complete use less to take a look at the contents of the SAM file, using the space bar to leaf through them. You'll notice a lot of alignments look basically like this: HWI-ST1097:228:C21WMACXX:8:1316:10989: * 0 0 * * 0 0 AAATTGCTTCCTGTCCTCATCCTTCCTGTCAGCCATCTTCCTTCGTTTGATCTCAGGGAAGTTCAGGTCTTC CAGCCGCTCTTTGCCACTGATCTCCAGCT CCCFFFFFHHHHHIJJJJIJJJJIJJJJHJJJJJJJJJJJJJJIIIJJJIGHHIJIJIJIJHBHIJJIIHIE GHIIHGFFDDEEEDDCDDD@CDEDDDCDD Notice that the contig name (field 3) is just an asterisk ( * ) and the alignment flags value is a 4 (field 2), meaning the read did not align (decimal 4 = hex 0x4 = read did not map). Essentially, nothing (with a few exceptions) aligned. Why? Answer Because this file was generated exclusively from reads in a larger dataset that cross at least one splice junction. The sequences as they exists in most of the reads do not correspond to a single location in the genome. However subsections of each read do exist somewhere in the genome. So, we need an aligner that is capable aligning different parts of the read to different genomic loci. RNA-seq alignment with bwa mem Exercise: use bwa mem to align the same data Based on the following syntax and the above reference path, use bwa mem to align the same file, saving output files with the prefix human_rnas eq_mem. Go ahead and just execute on the command line. bwa mem <ref.fa> <reads.fq> > outfile.sam Hint: