Overview

As mentioned in the introduction tutorial as well as the read processing tutorial, read processing can make a huge impact on downstream work. While cutadapt which was introduced in the read processing tutorial is great for quick evaluation or dealing with a single bad sample, it is not as robust as some other trimmers in particular when it comes to removing sequence that you know shouldn't be present but may exist in odd orientations (such as adapter sequences from the library preparation).

Learning objectives:

1. Install trimmomatic

2. Set up a small script to work around the annoying java invocation

3. Remove adapter sequences from some plasmids and evaluate effect on read quality, or assembly.

Installing trimmomatic

Trimmomatic's home page can be found at this link which includes links to the paper discussing the program, and a user manual. Trimmomatic is far above average for as far as programs go, most will not have a user manual, may not have been updated since originally published, etc. This is what makes it such a good tool.

Checking for installation

java -jar $HOME/local/bin/trimmomatic-0.39.jar

If the above command works, jump down to the section on making a bash script. Otherwise continue with the next section to install the program

Installing using wget

In a new web browser window/tab, navigate to the trimmomatic home page. In the Downloading Trimmomatic section; right click on the 'binary' link for version 0.39 and copy that link address.

...

unzip Trimmomatic-0.39.zip
cdcp Trimmomatic-0.39
cp /trimmomatic-0.39.jar $HOME/local/bin

If you don't see the zip file or are unable to cd into the 0.39 directory after unzipping it let the instructor know.

java -jar $HOME/local/bin/trimmomatic-0.39.jar

When you compare how wordy and complicated that is to the other programs you have encountered in the course, it makes sense that we would want a simpler way of accessing the program which is exactly what we will do next.

Anchor
bashScript bashScript

Creating bash script to launch trimmomatic 

The goal here will be to create an executable file with 2 lines. The first line will specify that it is a bash script. The second line will be the command we want to run, followed by information needed by bash to know to put the rest of the command you type after the executable file name.

1. Launch nano with the command: nano $HOME/local/bin/trimmomatic

2. In the nano window enter the following 2 lines exactly as they are typed

   No Format
   #!/bin/bash java -jar $HOME/local/bin/trimmomatic-0.39.jar $@

3. write and exit nano with control+o and control+x

4. make the trimmomatic file you just created executable 

   Code Block
   language bash title making file executable
   chmod +x $HOME/local/bin/trimmomatic

Verify that you have successfully made your bash script by typing 'trimmomatic' (try using the tab key in the middle as further sign things are working) and then press enter. You should see the same help that you saw above.

Trimming adapter sequences

Example generic command

trimmomatic PE <READ1> <READ2> -baseout Trim_Reads/<basename> ILLUMINACLIP:<FASTAadapterFILE>:4:30:10 MINLEN:30

Breaking down the parts of the above command:

ILLUMINACLIP

<FASTAadapterFILE>

:4:30:10

 MINLEN:30

All of the above has been put together using the trimmomatic manual and experience in our lab given the adapters we use and the library kits we prepare the samples with.

What does this look like in practice you may ask? Read on for some examples of trimming a single sample, or trimming a large number of samples.

Trimming a single sample

Get some data

mkdir -p $SCRATCH/GVA_trimmomatic_1sample/Trim_Reads
cd $SCRATCH/GVA_trimmomatic_1sample
cp $BI/gva_course/plasmid_qc/E1-7* .

cp $WORK/src/Trimmomatic-0.39/adapters/TruSeq3-PE-2.fa .

The ls command should show you 2 gzipped fastq files and a fasta file

Trim the fastq files

The following command can be run on the head node. Like with FastQC if we are dealing with less than say 1-2Million reads, it is reasonable to run the command on the head node unless we have 100s of samples in which case submitting to the queue will be faster as the files can be trimmed all at once rather than 1 at a time. Use what you have learned in the class to determine if you think this command should be run on the head node. (this was covered in more detail in the first part of the evaluating and processing read quality tutorial.)

zgrep -c "^+$" *.fastq.gz

trimmomatic PE E1-7_S187_L001_R1_001.fastq.gz E1-7_S187_L001_R2_001.fastq.gz -baseout Trim_Reads/E1-7.fastq.gz ILLUMINACLIP:TruSeq3-PE-2.fa:4:30:10 MINLEN:30

Evaluating the output

The last 2 lines of text you get should read:

Input Read Pairs: 6891 Both Surviving: 2391 (34.70%) Forward Only Surviving: 1844 (26.76%) Reverse Only Surviving: 2644 (38.37%) Dropped: 12 (0.17%)
TrimmomaticPE: Completed successfully

From the last line we know things worked correctly.

2nd to last line tells us:

1. we had 6891 total reads
2. 34.7% of reads both R1 and R2 were long enough to be kept after trimming
3. 26.76% of reads and 38.37% of reads only 1 of the reads were long enough and/or not a complete duplicate of the other read
4. only 0.17% of reads were discarded  for both R1 and R2. This is a rough estimate of adapter dimer being present in the sample.

When we interrogate the Trim_Reads folder with ls we see 4 files:

1. _1P
2. _2P
3. _1U
4. _2U

1/2 represent read 1 and read2 ... P/U represent paired and unpaired reads. They are kept separate as many programs require R1 and R2 files to be the same length when being used as input.

Trim all the samples from the multiqc tutorial

As mentioned above, if you have already done the multiqc tutorial, you can use your new trimmomatic command to remove the adatper sequences from all 544 samples.

Get some data

mkdir -p $SCRATCH/GVA_trimmomatic_multiqcSamples/Trim_Reads
cd $SCRATCH/GVA_trimmomatic_multiqcSamples
cp -r $BI/gva_course/plasmid_qc/ Raw_Reads/
cp $WORK/src/Trimmomatic-0.39/adapters/TruSeq3-PE-2.fa .

 The ls command will now show 2 directories, and a fasta file.

Trim the fastq files

Just as we used a for loop to set up a set of FastQC commands in the multiqc tutorial, we can use a similar for loop to generate a single file with 272 trim commands for the 544 total files.

The following command will pair all R1 reads in the Raw_Reads folder with its R2 pair, determine the base name, and generate a command to trim the file 

for r1 in Raw_Reads/*_R1_*.fastq.gz; do r2=$(echo $r1|sed 's/_R1_/_R2_/'); name=$(echo $r1|sed 's/_R1_001.fastq.gz//'|sed 's/Raw_Reads\///'); echo "trimmomatic PE $r1 $r2 -baseout Trim_Reads/$name.fastq.gz ILLUMINACLIP:TruSeq3-PE-2.fa:4:30:10 MINLEN:30";done > trim_commands

Use the head and wc -l to see what the output is and how much there is of it respectively.

Again as we discussed in the multiqc tutorial, running this number of commands is kind of a boarder line case, there are not a lot of total reads, but there are a large number of samples so we are likely to benefit from not being run on an idev node. 

launcher_creator.py -N 1 -w 48 -n "trimmomatic" -t 00:10:00 -a "UT-2015-05-18" -j trim_commands -m "module load launcher/3.0.3"
sbatch trimmomatic.slurm

The job should take less than 5 minutes once it starts, the showq -u command can be used to check for the job finishing.

Evaluating the output

ls Trim_Reads | wc -l
grep -c "TrimmomaticPE: Completed successfully" trimmomatic.e*

The above 2 commands are expected to show 1088 and 272 respectively, if you see other answers it suggests that something went wrong with the trimming command itself. If so remember I'm on zoom if you need help looking at whats going on.

Beyond the job finishing successfully, the best way to evaluate this data would actually be to move back to the multiqc tutorial and repeat the analysis there that was done on the raw files for the trimmed files here.

Personal experience

The for loop above focuses just on generating the trim commands. In my experience that is only half of the job, the other half is capturing the individual outputs so you can evaluate how many reads were trimmed in what way for each sample. Perhaps you will find this command helpful in your own work: 

mkdir trimLogs; mkdir Trim_Reads; for r1 in Raw_Reads/*_R1_*.fastq.gz; do r2=$(echo $r1|sed 's/_R1_/_R2_/'); name=$(echo $r1|sed 's/_R1_001.fastq.gz//'|sed 's/Raw_Reads\///'); echo "trimmomatic PE $r1 $r2 -baseout Trim_Reads/$name.fastq.gz ILLUMINACLIP:TruSeq3-PE-2.fa:4:30:10 MINLEN:30 >& trimLogs/$name.log"; done > trim_commands

After the job completes, the following command is useful for evaluating its success:

echo -e "\nTotal read pairs:";wc -l trim_commands;echo "Successful trimmings:"; tail -n 1 trimLogs/*|grep -c "TrimmomaticPE: Completed successfully"

This gives me a quick readout of if all of the individual commands finished correctly.

Further, if you were to use this for loop rather than the one listed in the tutorial above, you would see each sample generates its own log file in the the trimLogs directory that when investigated with cat/more/less/tail/nano is identical to the output you saw when you trimmed a single sentence allowing you to figure out how different samples are being processed.

Optional next steps:

1. Consider moving back over to the multiqc tutorial use multiqc to determine well the trimming worked. \
2. The reads could then further be assembled in the Spades tutorial as they are all plasmid samples.

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