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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).
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The adapter file listed here is likely the correct one to use for standard library preps that have been generated in the last few years, but may not be appropriate for all library preps (such as single end sequencing adapters, or nextera based preps). look to both the trimmomatic documentation and your experimental procedures at the bench to figure out if the adapter file is sufficient or if you need to create your own |
Learning objectives:
Install trimmomatic
Set up a small script to work around the annoying java invocation
Remove adapter sequences from some plasmids and evaluate effect on read quality, or assembly.
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Breaking down the parts of the above command:
| Part | Purpose | replace with/note |
|---|---|---|
| trimmomatic | tell the computer you are using the bash script/command you just made | |
| PE | tell trimmomatic program you are using the paired end mode | |
| <READ1> | fastq file read1 you are trying to trim | actual name of fastq file |
| <READ2> | fastq file read2 you are trying to trim | actual name of paired fastq file |
| -baseout | add a prefix to the resulting trimmed files | |
| Trimreads/<basename> | put all trimmed reads in a new folder. This is very good practice | typically you will replace with the first part of the fastq file name (before the Snumber or Lnumber depending on the file usually) |
ILLUMINACLIP | tell trimmomatic program how you want to trim the adapters | |
<FASTAadapterFILE> | name of file you containing your adapters | good practice to copy the fasta file you want to use to the current directory |
:4:30:10 | allow 4 mismatches require 30 bp of overlap between R1 and R2 to identify the fragment as being less than the read length Require 10bp of sequence to match before removing anything | |
MINLEN:30 | discard any reads that are less than 30bp after trimming |
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
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mkdir -p $SCRATCH/GVA_trimmomatic_test1sample/Trim_Reads cd $SCRATCH/GVA_trimmomatic_test1sample 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.)
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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:
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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.
Useful command line for loop
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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
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mkdir -p $SCRATCH/GVA_trimmomatic_multiqcSamples/Trim_Reads
cd $SCRATCH/GVA_trimmomatic_multiqcSamples
cp -r $BI/gva_course/plasmid_qc/ Raw_Reads/
cp $WORK/src/Trimmomatic-0.39/adapters/TruSeq3-PE-2.fa . |
The ls command will now show 2 directories, and a fasta file.
Trim the fastq files
Just as we used a for loop to set up a set of FastQC commands in the multiqc tutorial, we can use a similar for loop to generate a single file with 272 trim commands for the 544 total files.
The following command will pair all R1 reads in the Raw_Reads folder with its R2 pair, determine the base name, and generate a command to trim the file
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for r1 in Raw_Reads/*_R1_*.fastq.gz; do r2=$(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.
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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
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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:
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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:
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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:
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- Consider moving back over to the multiqc tutorial
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- use multiqc to determine
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- well the trimming worked. \
- The reads could then further be assembled in the Spades tutorial as they are all plasmid samples.