This lesson has passed peer-review! See the publication in JOSE.

Trimming and Filtering


Teaching: 30 min
Exercises: 25 min
  • How can we get rid of sequence data that does not meet our quality standards?

  • Clean FASTQ reads using Trimmomatic.

  • Select and set multiple options for command line bioinformatic tools.

  • Write for loops with two variables.

Cleaning reads

In the last episode, we took a high-level look at the quality of each of our samples using FastQC. We visualized per-base quality graphs showing the distribution of the quality at each base across all the reads from our sample. This information helps us to determine the quality threshold we will accept, and thus, we saw information about which samples fail which quality checks. Some of our samples failed quite a few quality metrics used by FastQC. However, this does not mean that our samples should be thrown out! It is common to have some quality metrics fail, which may or may not be a problem for your downstream application. For our workflow, we will remove some low-quality sequences to reduce our false-positive rate due to sequencing errors.

To accomplish this, we will use a program called Trimmomatic. This useful tool filters poor quality reads and trims poor quality bases from the specified samples.

Trimmomatic options

Trimmomatic has a variety of options to accomplish its task. If we run the following command, we can see some of its options:

$ trimmomatic

Which will give you the following output:

       PE [-version] [-threads <threads>] [-phred33|-phred64] [-trimlog <trimLogFile>] [-summary <statsSummaryFile>] [-quiet] [-validatePairs] [-basein <inputBase> | <inputFile1> <inputFile2>] [-baseout <outputBase> | <outputFile1P> <outputFile1U> <outputFile2P> <outputFile2U>] <trimmer1>...
       SE [-version] [-threads <threads>] [-phred33|-phred64] [-trimlog <trimLogFile>] [-summary <statsSummaryFile>] [-quiet] <inputFile> <outputFile> <trimmer1>...

This output shows that we must first specify whether we have paired-end (PE) or single-end (SE) reads. Next, we will specify with which flags we want to run Trimmomatic. For example, you can specify threads to indicate the number of processors on your computer that you want Trimmomatic to use. In most cases, using multiple threads(processors) can help to run the trimming faster. These flags are unnecessary, but they can give you more control over the command. The flags are followed by positional arguments, meaning the order in which you specify them is essential. In paired-end mode, Trimmomatic expects the two input files and then the names of the output files. These files are described below. While in single-end mode, Trimmomatic will expect one file as input, after which you can enter the optional settings and, lastly, the name of the output file.

Option Meaning
<inputFile1> input forward reads to be trimmed. Typically the file name will contain an _1 or _R1 in the name.
<inputFile2> Input reverse reads to be trimmed. Typically the file name will contain an _2 or _R2 in the name.
<outputFile1P> Output file that contains surviving pairs from the _1 file.
<outputFile1U> Output file that contains orphaned reads from the _1 file.
<outputFile2P> Output file that contains surviving pairs from the _2 file.
<outputFile2U> Output file that contains orphaned reads from the _2 file.

The last thing Trimmomatic expects to see is the trimming parameters:

step meaning
ILLUMINACLIP Perform adapter removal.
SLIDINGWINDOW Perform sliding window trimming, cutting once the average quality within the window falls below a threshold.
LEADING Cut bases off the start of a read if below a threshold quality.
TRAILING Cut bases off the end of a read if below a threshold quality.
CROP Cut the read to a specified length.
HEADCROP Cut the specified number of bases from the start of the read.
MINLEN Drop an entire read if it is below a specified length.
TOPHRED33 Convert quality scores to Phred-33.
TOPHRED64 Convert quality scores to Phred-64.

Understanding the steps you are using to clean your data is essential. We will use only a few options and trimming steps in our analysis. For more information about the Trimmomatic arguments and options, see the Trimmomatic manual.

Diagram showing the parts of the sequence that are reviewed by each parameter and the parts that are maintained or discarded at the end of the process. The Illuminaclip parameter removes the adapters, and the SlidingWindow scans the read by sections and removes a part of the read below the quality threshold. We remain with a trimmed read that has a valid quality.

However, a complete command for Trimmomatic will look something like the command below. This command is an example and will not work, as we do not have the files it refers to:

$ trimmomatic PE -threads 4 SRR_1056_1.fastq SRR_1056_2.fastq \
              SRR_1056_1.trimmed.fastq SRR_1056_1un.trimmed.fastq \
              SRR_1056_2.trimmed.fastq SRR_1056_2un.trimmed.fastq \
              ILLUMINACLIP:SRR_adapters.fa SLIDINGWINDOW:4:20

In this example, we have told Trimmomatic:

code meaning
PE that it will be taking a paired-end file as input
-threads 4 to use four computing threads to run (this will speed up our run)
SRR_1056_1.fastq the first input file name. Forward
SRR_1056_2.fastq the second input file name. Reverse
SRR_1056_1.trimmed.fastq the output file for surviving pairs from the _1 file
SRR_1056_1un.trimmed.fastq the output file for orphaned reads from the _1 file
SRR_1056_2.trimmed.fastq the output file for surviving pairs from the _2 file
SRR_1056_2un.trimmed.fastq the output file for orphaned reads from the _2 file
ILLUMINACLIP:SRR_adapters.fa to clip the Illumina adapters from the input file using the adapter sequences listed in SRR_adapters.fa
SLIDINGWINDOW:4:20 to use a sliding window of size 4 that will remove bases if their Phred score is below 20

Multi-line for long commands

Some of the commands we ran in this lesson are long! To separate code chunks onto separate lines When typing into your terminal one command with long input or many modifying parameters, you can use the \ character to make your code more readable. For example, let us use multi lines with the echo command. With \ it is possible to use several lines to print “hello world” on your terminal.

$ echo he\
$ llo\
$ world
$ hello world

Note: Some terminals only wait a few seconds for you to keep typing. In that case, you may write down the full command in a text file and then copy it to your terminal.

Running Trimmomatic

Now, we will run Trimmomatic on our data. Navigate to your untrimmed_fastq data directory and verify that you are located in the untrimmed_fastq/ directory:

$ cd ~/dc_workshop/data/untrimmed_fastq
$ pwd
$ /home/dcuser/dc_workshop/data/untrimmed_fastq

You should have only four files in this directory. Those files correspond to the files of forward and reverse reads from samples JC1A and JP4D.

$ ls
$ JC1A_R1.fastq.gz  JC1A_R2.fastq.gz  JP4D_R1.fastq  JP4D_R2.fastq.gz  TruSeq3-PE.fa   

We are going to run Trimmomatic on one of our paired-end samples. While using FastQC, we saw that Universal adapters were present in our samples. The adapter sequences came with the installation of Trimmomatic and it is located in our current directory in the file TruSeq3-PE.fa.

We will also use a sliding window of size 4 that will remove bases if their Phred score is below 20 (like in our example above). We will also discard any reads that do not have at least 25 bases remaining after this trimming step. This command will take a few minutes to run.

Before, we unzipped one of our files to work with it. Let us compress the file corresponding to the sample JP4D again before we run Trimmomatic.

gzip JP4D_R1.fastq
$ trimmomatic PE JP4D_R1.fastq.gz JP4D_R2.fastq.gz \
JP4D_R1.trim.fastq.gz  JP4D_R1un.trim.fastq.gz \
JP4D_R2.trim.fastq.gz  JP4D_R2un.trim.fastq.gz \
TrimmomaticPE: Started with arguments:
 JP4D_R1.fastq.gz JP4D_R2.fastq.gz JP4D_R1.trim.fastq.gz JP4D_R1un.trim.fastq.gz JP4D_R2.trim.fastq.gz JP4D_R2un.trim.fastq.gz SLIDINGWINDOW:4:20 MINLEN:35 ILLUMINACLIP:TruSeq3-PE.fa:2:40:15
Multiple cores found: Using 2 threads
ILLUMINACLIP: Using 1 prefix pairs, 0 forward/reverse sequences, 0 forward only sequences, 0 reverse only sequences
Quality encoding detected as phred33
Input Read Pairs: 1123987 Both Surviving: 751427 (66.85%) Forward Only Surviving: 341434 (30.38%) Reverse Only Surviving: 11303 (1.01%) Dropped: 19823 (1.76%)
TrimmomaticPE: Completed successfully

Exercise 1: What did Trimmomatic do?

Use the output from your Trimmomatic command to answer the following questions.

1) What percent of reads did we discard from our sample?
2) What percent of reads did we keep both pairs?


1) 1.76%
2) 66.85%

You may have noticed that Trimmomatic automatically detected the quality encoding of our sample. It is always a good idea to double-check this or manually enter the quality encoding.

We can confirm that we have our output files:

$ ls JP4D*
JP4D_R1.fastq.gz       JP4D_R1un.trim.fastq.gz	JP4D_R2.trim.fastq.gz
JP4D_R1.trim.fastq.gz  JP4D_R2.fastq.gz		JP4D_R2un.trim.fastq.gz

The output files are also FASTQ files. It should be smaller than our input file because we have removed reads. We can confirm this with this command:

$ ls JP4D* -l -h
-rw-r--r-- 1 dcuser dcuser 179M Nov 26 12:44 JP4D_R1.fastq.gz
-rw-rw-r-- 1 dcuser dcuser 107M Mar 11 23:05 JP4D_R1.trim.fastq.gz
-rw-rw-r-- 1 dcuser dcuser  43M Mar 11 23:05 JP4D_R1un.trim.fastq.gz
-rw-r--r-- 1 dcuser dcuser 203M Nov 26 12:51 JP4D_R2.fastq.gz
-rw-rw-r-- 1 dcuser dcuser 109M Mar 11 23:05 JP4D_R2.trim.fastq.gz
-rw-rw-r-- 1 dcuser dcuser 1.3M Mar 11 23:05 JP4D_R2un.trim.fastq.gz

We have just successfully run Trimmomatic on one of our FASTQ files! However, there is some bad news. Trimmomatic can only operate on one sample at a time, and we have more than one sample. The good news is that we can use a for loop to iterate through our sample files quickly!

$ for infile in *_R1.fastq.gz
base=$(basename ${infile} _R1.fastq.gz)
trimmomatic PE ${infile} ${base}_R2.fastq.gz \
${base}_R1.trim.fastq.gz ${base}_R1un.trim.fastq.gz \
${base}_R2.trim.fastq.gz ${base}_R2un.trim.fastq.gz \

Go ahead and run the for loop. It should take a few minutes for Trimmomatic to run for each of our four input files. Once it is done, take a look at your directory contents. You will notice that even though we ran Trimmomatic on file JP4D before running the for loop, there is only one set of files for it. Because we matched the ending _R1.fastq.gz, we re-ran Trimmomatic on this file, overwriting our first results. That is ok, but it is good to be aware that it happened.

$ ls
JC1A_R1.fastq.gz                     JP4D_R1.fastq.gz                                    
JC1A_R1.trim.fastq.gz                JP4D_R1.trim.fastq.gz                                
JC1A_R1un.trim.fastq.gz              JP4D_R1un.trim.fastq.gz                                
JC1A_R2.fastq.gz                     JP4D_R2.fastq.gz                                 
JC1A_R2.trim.fastq.gz                JP4D_R2.trim.fastq.gz                                 
JC1A_R2un.trim.fastq.gz              JP4D_R2un.trim.fastq.gz                                 

We have completed the trimming and filtering steps of our quality control process! Before we move on, let us move our trimmed FASTQ files to a new subdirectory within our data/ directory.

$ cd ~/dc_workshop/data/untrimmed_fastq
$ mkdir ../trimmed_fastq
$ mv *.trim* ../trimmed_fastq
$ cd ../trimmed_fastq
$ ls
JC1A_R1.trim.fastq.gz    JP4D_R1.trim.fastq.gz                
JC1A_R1un.trim.fastq.gz  JP4D_R1un.trim.fastq.gz              
JC1A_R2.trim.fastq.gz    JP4D_R2.trim.fastq.gz                
JC1A_R2un.trim.fastq.gz  JP4D_R2un.trim.fastq.gz   

Bonus Exercise (Advanced): Quality test after trimming

Now that our samples have gone through quality control, they should perform better on the quality tests run by FastQC.

Sort the following chunks of code to re-run FastQC on your trimmed FASTQ files and visualize the HTML files to see whether your per base sequence quality is higher after trimming.

$ scp*.html ~/Desktop/fastqc_html/trimmed
$ fastqc ~/dc_workshop/data/trimmed_fastq/*.fastq*
$ mkdir ~/Desktop/fastqc_html/trimmed


In your AWS terminal window, do the following:

$ fastqc ~/dc_workshop/data/trimmed_fastq/*.fastq*

In a terminal standing on your local computer, do:

$ mkdir ~/Desktop/fastqc_html/trimmed
$ scp*.html ~/Desktop/fastqc_html/trimmed

Then take a look at the html files in your browser.

Remember to replace everything between the @ and : in your scp command with your AWS instance number.

After trimming and filtering, our overall quality is much higher, we have a distribution of sequence lengths, and more samples pass adapter content. However, quality trimming is not perfect, and some programs are better at removing some sequences than others. Trimmomatic did pretty well, though, and its performance is good enough for our workflow.

Key Points

  • The options you set for the command-line tools you use are important!

  • Data cleaning is essential at the beginning of metagenomics workflows.

  • Use Trimmomatic to get rid of adapters and low-quality bases or reads.

  • Carefully fill in the parameters and options required to call a function in the bash shell.

  • Automate repetitive workflows using for loops.