Resequencing Analysis

The data we will work with comes from the 1000 Genomes Project. Because whole human genomes are very difficult to work with, we will use only a small portion of the human genome, a little over a megabase from chromosome 17. Samtools have been used to extract the data from the 1000 Genomes ftp site for just this region from all of the individuals from the CEU (CEPH Europeans from Utah) population who were low coverage (2-4x average) whole genome shotgun sequenced. We have 81 low coverage Illumina sequences, plus 63 Illumina exomes, and 15 low coverage 454 samples. There are 55 of these samples that were done both ways.

We will walk through alignment, alignment processing and cleanup, quality recalibration, variant calling, and variant filtering.

In order to do these exercises, you will need to know a few things.

Book a node

We have reserved half a node for each student during this course. By now, you are probably already familiar with the procedure:

salloc -A g2015005 -t 08:00:00 -p core -n 8 --no-shell --reservation=g2015005_wed &

Make sure you ony do this once, otherwise you will take away resources from the other course participants!

Accessing Programs

First, we're going to run several programs that are installed under the module system. To access the bioinformatics modules you first need the bioinfo module:

module load bioinfo-tools

Throughout these exercises, that preformatted text, like above, will usually indicate something you can type into the command line.

Now you can load the individual programs we need:

module load bwa
module load samtools

We also will use Picard and GATK. There are modules for these, but they are java programs, which means we need to explicitly invoke java each time we run them, and we need to know where the code actually lives. The GATK module tells you this when you use it:

module load GATK
You can find all the GATK files in /sw/apps/bioinfo/GATK/1.5.21

The Picard module does not, but they are in a similar place. For various parts of this exercise, you will need to know:


For the other programs, you can just type the name of the program and it will run. You can even tab complete the name. This is what the module system does for you.

Accessing Data

You need to know where your input data are and where your output will go.

All of your input data for the first steps of these exercises will be in our course project:


Normally, if you were working on your own project, you would want to write your output into your project directory also. However, since we're all sharing the same data, we've made these file read-only so that no one accidentally deletes them or writes over the raw data or someone else's output.

Instead, you are going to write your output to the glob2 directory in your home directory. Remember that your home directory can be represented by the '~' character. This may save you a lot of typing. The glob2 space is not backed up and is occasionally deleted, and is meant to be used for temporary storage. (You could also write these files to your regular home directory space, but you may run out of space, and it is not good practice to keep large amounts of data in your home directory, so please do not do that.)

This creates some complexity, because your input data and your output data are not in the same place. This is a common data processing problem, and one you should get used to dealing with. It does mean that you'll need to type a lot. There are a few ways to deal with this.

  1. Remember where you are (your current working directory, [pwd]) and use relative or absolute paths as necessary to type the least. This is a quick but sloppy solution, and error prone, but if you are doing things one time by hand, it works. We all do it sometimes.
  2. Use the full paths to everything, regardless of where you are actually working. This is the most time consuming, and requires that you remember where everything is, but it is also the safest, because you always know that you are telling the computer exactly where you want to read and write. This method is not dependent on keeping track of your current directory, because there are no relative paths, and you are much less likely to write data out to the wrong place by mistake. Any time you get to the point of writing code or batch scripts to automate your data processing, you should do this. For purposes of these exercises, it does not really matter which of these you do. This is part of learning to work on the command line. For purposes of example, the full paths will be given, but there will be examples where only the general syntax will be given, and you will have to find your data.

Also, remember that tab completion can be very helpful in typing paths to files, not just because it saves keystrokes but also because it validates that you have typed a valid path (if the file is not there, tab completion will not work).

So that we don't clutter up the top level of our globs and get in the way of later exercises, we will make a subdirectory in there

mkdir ~/glob2/gatk

Throughout the exercises, we will us a common convention that "<parameter>" (or <inputfile>, <outputfile>, <your directory>, etc.) means "type in this space in the command the parameter (input file, output file, directory, etc.) that you will be using", never that you should literally type "<parameter>" into the computer. If you don't know what you should be replacing this with, ask. We do this for two reasons. First, as you all work, not everyone will create files with exactly the same names, so there is no way to make standard instructions for everyone. Second, you need to learn how to figure out what goes into these spaces.

That brings us to copying and pasting. It is possible to copy some of the commands out of this wiki and paste them into your terminal and make them work. This is not recommended. First, there can be formatting differences (especially how return characters are handled) between the browser and the terminal that make these commands not work properly. Second, and more important, when you are doing this on your own data, there will be no cutting and pasting. You will learn more by typing. Remember that tab completion will help you with this.

Running BWA

We will align our data to the reference using BWA, a popular aligner based on the Burrows-Wheeler transform.

Before we can run BWA at all, we need a reference genome, and we need to perform the Burrows-Wheeler transform on the reference and build the associated files. For our exercises, we'll use only human chromosome 17. You can copy this from the project directory to your workspace. (Normally copying references is a bad thing, but this is so that everyone can see the full BWA process.)

cp /proj/g2015005/labs/gatk/refs/human_17_v37.fasta ~/glob2/gatk

Check to see that this worked.

ls -l ~/glob2/gatk

should show you:

total 88812
-rw-r--r-- 1 mczody uppmax 82548517 Sep 23 21:44 human_17_v37.fasta

except with your username.

If your file is not there or if it's the wrong size, something went wrong with your copy and you need to figure out what before you move on. Checking the existence and size of files from each step in a process before performing the next step is a good practice that save a lot of time. A common mistake people make is to attempt to load input files that do not exist or create output files that they cannot write.

Now we need to build the Burrows-Wheeler transform

bwa index -a bwtsw ~/glob2/gatk/human_17_v37.fasta

BWA is a single program that takes a series of different commands as the first argument. That command says to index the specified reference and use the bwtsw algorithm (BWA also has another indexing method for small genomes that we will not use).

This command will take about 2 minutes to run and should create 5 new files in your gatk directory with the same base name as the reference and different extensions.

While we're doing this, we will also build a sequence dictionary for the reference, which It just lists the names and lengths of all the chromosomesother programs will need as input later. and is used to make sure the headers are correct.

samtools faidx ~/glob2/gatk/human_17_v37.fasta

step 2. Mapping - Making Single Read Alignments for each of the reads in the paired end data

Running BWA for paired end data is done in multiple steps. First we align each set of reads, then we combine the paired alignments together (which also includes a realignment step using a more sensitive algorithm for unplaced mates). Let's start with one chunk of whole genome shotgun data from individual NA06984.

bwa aln ~/glob2/gatk/human_17_v37.fasta /proj/g2015005/labs/gatk/fastq/wgs/NA06984.ILLUMINA.low_coverage.17q_1.fq >~/glob2/gatk/NA06984.ILLUMINA.low_coverage.17q_1.sai

Note that if you have to use a file redirect ( >) for your output. Many (but not all!) functions of BWA default to sending their output to stdout (i.e., your screen) if you do not define a specific outputfile using the -f option, which is great if you want to build pipelines that redirect these things but not so useful when you want to write them to disk. Forgetting the redirect can be very disappointing.

While that's running, take a minute to look at the input file path. This is a fastq file, so I put it in a directory called fastq. It is from whole genome shotgun sequencing, so it is in a subdirectory called wgs. The file name has 6 parts, separated by . or _:

  1. NA06984 - this is the individual name
  2. ILLUMINA - these reads came from the Illumina platform
  3. low_coverage - these are low coverage whole genome shotgun reads
  4. 17q - I have sampled these reads from one region of 17q
  5. 1 - these are the first reads in their paired sets
  6. fq - this is a fastq file
Now we need to do this again for the second read file. Everything is that same except with 2s instead of 1s. Don't forget to change your output file also!

Before we go on to the next step, take a minute and look at the fastq files. Use


to read one of those .fq files in the project directory.

step 3. Merging Alignments and Making SAM Files

The sai files are a binary format internal to BWA. We now need to process those into something we can use. For paired ends, this is done with the sampe function of BWA. (Note that if you ever forget the syntax for a function, you can just type

bwa <function>

and it will list the parameters and options. Run it for your files:

bwa sampe <ref> <sai1> <sai2> <fq1> <fq2> >~/glob2/gatk/<sample>.sam

The sampe function takes a lot of arguments. It needs the reference and the reads, because the sai files just have the definitions of the alignments, not the sequences. It needs the sai files to get the alignments. It outputs a SAM format file. I would suggest that you give it the same name prefix as the others, but if you are getting tired of typing that, pick something shorter. Retain the sample name and the fact that it is the 17q low coverage data.

step 4. Creating a BAM File

SAM files are nice, but bulky, so there is a compressed binary format, BAM. We want to convert our SAM into BAM for everything that comes downstream.

Typically the BAM has the same name as the SAM but with the .sam extension replaced with .bam.

We need to add something called read groups to our BAM file, because GATK is going to need this information. Normally, you would do this one sequencing run at a time, but because of the way I downloaded these data from 1000 Genomes, our data are pulled from multiple runs and merged. We will pretend that we just have one run for each sample, but on real data, you should not do this.

Now, we use the Picard package to add read group information. However, it turns out that Picard is a very smart program, and we can start with the sam file and ask it to simultaneously add read groups, sort the file, and spit it out as BAM. (It does, however, have a very awkward calling syntax.)

java -Xmx2g -jar /sw/apps/bioinfo/picard/1.69/kalkyl/AddOrReplaceReadGroups.jar INPUT=<sam file> OUTPUT=<bam file> SORT_ORDER=coordinate RGID=<sample>-id RGLB=<sample>-lib RGPL=ILLUMINA RGPU=<sample>-01 RGSM=<sample>

Note that the arguments to Picard tools are parsed (read by the computer) as single words, so it is important that there is no whitespace between the upper case keyword, the equals, and the value specified, and that you quote ('write like this') any arguments that contain whitespace.

We specify the input, the output (assumed to be BAM), the SORT_ORDER, meaning we want Picard to sort the reads according to their genome coordinates, and a lot of sample information. The sample names for each of these 1000 Genomes runs is the Coriell identifier, the two letters and five numbers at the start of the file names (e.g., NA11932). We're going to use this for all our read group information.

  • RGID is the group ID. This is usually derived from the combination of the sample id and run id, or the SRA/EBI id. We will just add -id to the sample name.
  • RGLB is the group library. This will come from your library construction process. You may have multiple read groups per library if you did multiple runs, but you should only have one library per read group. We will add -lib the sample name.
  • RGPL is the platform. It is a restricted vocabulary. These reads are ILLUMINA.
  • RGPU is the run identifier. It would normally be the barcode of your flowcell. You may have multiple read groups per run, but only one run per read group. We will just fake it as <sample>-01.
  • RGSM is the sample name. Use the actual sample name. You can have multiple read groups, libraries, runs, and even platforms per sample, but you can only have one sample per read group. (If you are pooling samples without barcoding, there is no way to separate them later, so you should just designate the pool itself as a sample, but downstream analyses like SNP calling will be blind to that knowledge.) One thing to note is that the SAM/BAM header contains a field SO for sort order. Picard modifies this field to coordinate when it sorts BAMs, but samtools actually doesn't (as of this writing). This can create problems, because Picard also validates that BAMs are sorted before performing operations that require a sorted file, while samtools doesn't. To get around this, Picard tools take an optional parameter ASSUME_SORTED which when set true tells Picard to proceed as if the file were sorted even though it does not say so.
Lastly, we need to index this BAM, so that programs can randomly access the sorted data without reading the whole file. This creates a file called <input bam>.bai, which contains the index. You do not have to specify this because the index file always has the exact same name as the BAM except that it has .bai instead of the .bam extension. This is how programs know to find the index associated with a BAM file. If you manually mix these things up (like you change a BAM without changing its name and do not reindex it), you can cause problems for programs that expect them to be in sync.

java -Xmx2g -jar /sw/apps/bioinfo/picard/1.69/kalkyl/BuildBamIndex.jar INPUT=<bam file>

step 5. Processing Reads with GATK

Now, we want to use the Genome Analysis Toolkit (GATK) to perform a couple of alignment and quality improvement steps, although on our data, they may not actually do much, due to the nature of the data and some of the shortcuts we have taken in identifying our read groups.

First, we'll realign locally around potential indels. This is done in two steps. First, we identify possible sites to realign:

java -Xmx2g -jar /sw/apps/bioinfo/GATK/1.5.21/GenomeAnalysisTK.jar -I <bam file> -R <ref file> -T RealignerTargetCreator -o <intervals file>

The <bam file> should be your sorted and indexed BAM with read groups added from before. Note that the option flag preceding the input bam is a capital I (as in Input), not a lower case l. The <ref file> is the reference you used for alignment, and the <intervals file> is an output text file that will contain the regions GATK thinks should be realigned. Give it the extension ".intervals". Note that there is an additional option we are not using, which is to specify a list of known indels that might be present in the data (i.e., are known from other sequencing experiments). Using this speeds up the process of identifying potential realignment sites, but because our data set is so small, we won't use that.

Now we feed our intervals file back into GATK with a different argument to actually do the realignments:

java -Xmx2g -jar /sw/apps/bioinfo/GATK/1.5.21/GenomeAnalysisTK.jar -I <input bam> -R <ref file> -T IndelRealigner -o <realigned bam> -targetIntervals <intervals file>

Note that we need to give it the intervals file we just made, and also specify a new output bam (<realigned bam>). GATK is also clever and automatically indexes that bam for us (you can type ls and look at the list of files to verify this).

Next, we're going to go back to Picard and mark duplicate reads:

java -Xmx2g -jar /sw/apps/bioinfo/picard/1.69/kalkyl/MarkDuplicates.jar INPUT=<input bam> OUTPUT=<marked bam> METRICS_FILE=<metrics file>

Note that you need to feed it an <input bam>, which should be your realigned BAM from before, and you need to specify an output, the <marked bam> which will be a new file used in the following steps. There is also a <metrics file>, which is a output text file. We will take a look at that now.

Picard do not automatically index the .bam file so you need to do that before proceeding.

java -Xmx2g -jar /sw/apps/bioinfo/picard/1.69/kalkyl/BuildBamIndex.jar INPUT=<bam file>

Now we can look at the duplicates we marked with Picard, using a filter on the bit flag. The mark for duplicates is the bit for 1024, so we can look at only duplicate marked reads with that.

samtools view -f 1024 <bam file> | less

If we just want a count of the marked reads, we can use the -c option.

samtools view -f 1024 -c <bam file>

Finally, we want to perform quality recalibration with GATK. We do this last, because we want all the data to be as clean as possible when we get here. This also happens in two steps. First, we compute all the covariation of quality with various other factors:

java -Xmx2g -jar /sw/apps/bioinfo/GATK/1.5.21/GenomeAnalysisTK.jar -T CountCovariates -I <input bam> -R <ref file> -knownSites /proj/g2014207/labs/gatk/ALL.chr17.phase1_integrated_calls.20101123.snps_indels_svs.genotypes.vcf -cov ReadGroupCovariate -cov CycleCovariate -cov DinucCovariate -cov QualityScoreCovariate -recalFile <calibration csv>

We need to feed it our bam file and our ref file. We also need a list of known sites. Otherwise, GATK will think all the real SNPs in our data are errors. We're using calls from 1000 Genomes, which is a good plan for human (although a bit circular in our case). If you are sequencing an organism with few known sites, you could try calling once and then using the most confident variants as known sites (which should remove most of the non-erroneous bases). Failure to remove real SNPs from the recalibration will result in globally lower quality scores. We also give it the name of a csv file we want it to write out containing the covariation data. We will take a look at this. It will be used in the next step:

java -Xmx2g -jar /sw/apps/bioinfo/GATK/1.5.21/GenomeAnalysisTK.jar -T TableRecalibration -I <input bam> -R <ref file> -recalFile <calibration csv> -o <output bam>

The <input bam> in this step is the same as the last step, because we haven't changed it yet, but the <output bam> is new and will have the recalibrated qualities. The <calibration csv> is the file we created in the previous step.

Now we are almost ready to call variants. First, though, go back and run at least one more set of data through this whole process on your own, then we will do one final step.

Merging BAMs

For variant calling, we want to merge the BAMs from multiple samples together. This makes them easier to handle and allows GATK to work on many samples at once. (We could also feed multiple BAMs, but it would potentially become unwieldy.) You can also use this feature if you have multiple runs of a single sample and want all of your data from that sample in one BAM.

java -Xmx2g -jar /sw/apps/bioinfo/picard/1.69/kalkyl/MergeSamFiles.jar INPUT=<input bam 1> [INPUT=<input bam 2> ... INPUT=<input bam N>] OUTPUT=<output bam>

Note that you can specify the INPUT option multiple times.

The inout should be sorted and you will need to reindex the new version with Picard.

step 6. Variant Calling

Now we'll run the GATK Unified Genotyper on our merged bams.

java -Xmx2g -jar /sw/apps/bioinfo/GATK/1.5.21/GenomeAnalysisTK.jar -T UnifiedGenotyper -R <ref file> -I <merged bam> -o <filename.vcf> -glm BOTH

The <ref file> is our old reference fasta again. The <merged bam> is what you just created. The output file is <filename.vcf>. It needs to have a .vcf extension because it is a vcf file. The beginning part should be identifiable as associated with your merged file name (like the name root you use before the .bam) so you can tell later which vcf file came from which BAM).

I have also generated some merged BAMs with all 55 samples that have low coverage data and exome data, one file each for low coverage and exome. These are in /proj/g2015005/labs/gatk/processed/MERGED* (remember that the * means every file that matches the rest of this string and then has any other text after that).

Run the unified genotyper on both the exome and the low coverage data. These jobs should each take ~ 20 minutes to run. Because they take a long time and you have 8 cores on your nodes, you should run them in parallel. To do this, put an ampersand (&) at the end of the command line before you hit return. This runs the job in the background, and you get your prompt back immediately. However, the output will still go to your screen. We don't really want that, so we can use the redirect to put the output in a file to read later (e.g., ... &>merged_exome_ug.out&). Remember to give different output file names to your exome job and your low coverage job, unless you're really sure you don't want to be able to figure out what happened (you can send them both to the same file, but the outputs will be mixed up with each other randomly).

Note: we are using the term "output" here for two different things. With the -o option to GATK, we specified the name of the output vcf file for the UnifiedGenotyper. The redirect has no effect on that, because the program isn't writing it to "stdout". However, while it is running GATK writes some information to stdout (usually equal to your screen) telling us what it is doing and whether anything went wrong. That is what we are capturing in a file with the redirect. (Advanced note: in cases where you really do not want to keep the output of a program, but you just do not want it on the screen, you can redirect to /dev/null, which is a special "output device" that is a valid target for writing, but does not exist. It is like sending your output directly and irrevocably into the trash incinerator.)

In practice, you would probably run jobs like this out to the cluster using slurm with the sbatch command, but we already have a whole 8 processors each reserved for our use, so it seems silly to then submit jobs out to the cluster and wait for them to get assigned to other machines. In reality, for applications like this where you are submitting multiple different jobs in parallel, it is usually faster and easier to use a job queueing system like slurm to manage your jobs instead of logging directly on to a multiprocessor machine and trying to manage the CPU usage yourself.

While those are running, we'll skip ahead and start IGV.

Filtering Variants

The last thing we will do is filter variants. We do not have enough data that the VQSR technique for training filter thresholds on our data are likely to work, so instead we're just going to use the "best practices" parameters suggested by the GATK team (

The parameters are slightly different for SNPs and indels, but we have called ours together. I would suggest trying both and seeing what you get. Why do you think that some of these parameters are different between the two types of variants?

An example command line is:

java -Xmx2g -jar /sw/apps/bioinfo/GATK/1.5.21/GenomeAnalysisTK.jar -T VariantFiltration -R <reference> -V <input vcf> -o <output vcf> --filterExpression "QD<2.0" --filterName QDfilter --filterExpression "MQ<40.0" --filterName MQfilter --filterExpression "FS>60.0" --filterName FSfilter --filterExpression "HaplotypeScore>13.0" --filterName HSfilter

Note two things about this. First, each filterName option has to immediately follow the filterExpression it matches. This is an exception to the rule that options can come in any order. However, the order of these pairs, or their placement relative to other arguments, can vary. Second, the arguments to filterExpression are in quotation marks ("). Why is that?

If you want to run the indel filtering version, you can look on the web page above and get those value and substitute them.

Once you have the filtered calls, open your filtered VCF with less and page through it. It has all the variant lines, still, but one of the fields that was blank before is now filled in, indicating that the variant on that line either passed filtering or was filtered out, with a list of the filters it failed. Note also that the filters that were run are described in the header section.

step 7. Looking at Your Data with IGV

Next, we want to know how to look at these data. For that, we will use IGV (Integrative Genomics Viewer). We will launch IGV from our desktops because it runs faster that way. Go to your browser window and Google search for IGV. Find the downloads page. You will be prompted for an email address. If you have not already downloaded IGV from that email address, it will prompt you to fill in some information and agree to a license. When you go back to your own lab, you can just type in your email and download the software again without agreeing to the license.

Now launch the viewer through webstart. The 1.2 Gb version should be sufficient for our data. It will take a minute or two to download IGV and start it up. While that's going on, we need to download some data to our local machines so the viewer can find it (IGV can also look at web hosted data, but we are not going to set that up for our course data). When it prompts you to save the IGV program, just save it in your home directory (normally we would put this in the Applications folder on a Mac, but we probably can't write to that on these machines).

Open a new terminal or xterm on your local machine (i.e., do not log in to uppmax again). You should be in your home directory. Now we're going to use the command scp (secure copy) to get some data copied down:

We will start with the merged bam files. We want to get both the bams and bais for the low coverage and exome data.

scp <username>\* ./

Because your uppmax user name is different than the user name on the local machine, you have to put your uppmax user name in front of the @ in the scp so that it knows you want to log in as your uppmax user, not as macuser. After the colon, we give the path to the files we want. The wildcard (*) character indicates that we want all the files that start with "MERGED.illumina". However, in this case, we need to add a backslash ('\') in front of the wildcard ('*'). This is known as "escaping", because ordinarily your local shell would try to expand the wildcard in your local directory, but we want it expanded on the remote machine. The './' means copy the files to your current directory.

It will prompt you for your uppmax password, then it should download four files.

We will also want to load the vcfs into IGV, so you can look at what calls got made.

scp <username>\* ./

By now, IGV should be launching. The first thing we want to do is make sure we have the right reference. In IGV, go to the popup menu in the upper left and set it to "Human 1kg (b37+decoy)". This is the latest build of the human genome (also known as GRCh37).

Now, go under the Tools menu and selection "Run igvtools..." Change the command to "Count" and then use the Browse button next to the Input File line to select the bams (not the bai) that you just downloaded. It will autofill the output file. Now hit the Run button. This generates a .tdf file for each bam. This allows us to see the coverage value for our BAM file even at zoomed at views. (We could also do this offline using a standalone version of igvtools.)

Now close the igvtools window and go back to the File menu, select "Load from File..." and select your bams (not the .bai or the .tdf). They should appear in the tracks window. Click on chromosome 17 to zoom in to there. You can now navigate with the browser to look at some actual read data. If you want to jump directly to the region we looked at, you can type MAPT in the text box at the top and hit return. This will jump you to one of the genes in the region.

Let's look at a few features of IGV.

Go under the View menu and select Preferences. Click on the Alignments tab. There are a number of things we can configure. Feel free to play with them. Two important ones for our data are near the top. Because we have multiple samples and the exome coverage is very deep, we want to turn off downsampling (upper left). However, this will cause us to load more reads, so we want to reduce the visible range threshold (top). I would suggest 5 kb.

Next, we want to look at some of the track features. If you control-click (or right click for PCs or multi-button mice on Macs) on the track name at the left, you will get a popup menu with several options. For example, click the gene track and play with the view (collapsed, squished, expanded). I would suggest squished for our purposes.

If you go to a read alignment track, you can control some useful features of the display. One is how you color the reads (by sample is an interesting one here). Another is the grouping. Group by sample is again useful (having grouped by sample, we could then use color for something else).

By now, our variant calls should be done. Let's finish working on those, then come back.

We can also load our variant calls into IGV. Use scp to copy your vcf files (and their idx indices) to your local machine and load them in also.

For the rest of the time, just scroll around in IGV and look at your variant calls. Compare filtered and unfiltered (IGV displays the filtered variant site in lighter shades, so you only need to load the filtered file). Compare calls from the exome versus the low coverage sequencing.

You can look at just the calls you made, or you can look at the calls from the full set, where you may see more of a difference between different types and depths of sequencing and between the calls with and without filtering. You can even load these data all together. Are there calls that were made using only one or two samples that were not made in the full data set or vice versa?

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Topic revision: r16 - 2015-02-11 - NgsIntro1502Teacher
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