Short read quality and trimming


Reminder: if you’re on Windows, you should install mobaxterm.

Log into the HPC with SSH; use your MSU NetID and log into the machine ‘’. There copy/paste:

module load powertools
getexample RNAseq-semimodel

This will put all of the example files for today in your home directory under the directory ‘RNAseq-semimodel’.

0. Getting the data

Note that each sample has two replicates, and each replicate has two files.

Don’t download them, but if you were downloading these yourself, you would want the “Fastq files (ftp)”, both File 1 and File 2. (They take a few hours to download!)

We’ve already loaded the data onto the MSU HPC, and you’ve loaded it with ‘module load powertools’.

To log into a compute node, type:


If this doesn’t work, do:

ssh dev-intel14-phi

Now do:

ls -l ~/RNAseq-semimodel/data/

You’ll see something like

-r--r--r-- 1 mscholz common-data 6781517200 Dec  9 09:46 SRR534005_1.fastq.gz
-r--r--r-- 1 mscholz common-data 7023515467 Dec  9 09:50 SRR534005_2.fastq.gz
-r--r--r-- 1 mscholz common-data 7285848617 Dec  9 09:41 SRR534006_1.fastq.gz
-r--r--r-- 1 mscholz common-data 7542383700 Dec  9 09:43 SRR534006_2.fastq.gz
-r--r--r-- 1 mscholz common-data 7219923066 Dec  9 09:47 SRR536786_1.fastq.gz
-r--r--r-- 1 mscholz common-data 7467116873 Dec  9 09:49 SRR536786_2.fastq.gz
-r--r--r-- 1 mscholz common-data 7694614208 Dec  9 09:40 SRR536787_1.fastq.gz
-r--r--r-- 1 mscholz common-data 7944043814 Dec  9 09:44 SRR536787_2.fastq.gz

These files are each approximately 7-8 GB in size!

1. Copying in some data to work with.

First, make a directory:

mkdir ~/rnaseq
cd ~/rnaseq

Copy in a subset of the data (100,000 reads):

gunzip -c ~/RNAseq-semimodel/data/SRR534005_1.fastq.gz | head -400000 | gzip > female_repl1_R1.fq.gz
gunzip -c ~/RNAseq-semimodel/data/SRR534005_2.fastq.gz | head -400000 | gzip > female_repl1_R2.fq.gz

These are FASTQ files – let’s take a look:

less female_repl1_R1.fq.gz

(type ‘q’ to exit less)


  • why are some files named SRR*?
  • why are some files named female*?
  • why are there R1 and R2 in the name?


2. FastQC

We’re going to use FastQC to summarize the data.

First, we need to load the FastQC software into our account:

module load FastQC/0.11.2

(You have to do this each time you log in and want to use FastQC.)

Now, run FastQC on both of the female files:

fastqc female_repl1_R1.fq.gz
fastqc female_repl1_R2.fq.gz

Now type ‘ls’:


and you will see


Copy these to your laptop and open them in a browser. If you’re on a Mac or Linux machine, you can type:

scp*fastqc.* /tmp

and then open the html files in your browser. For Windows, if you’re using mobaxterm, most of you should have a file transfer window on the left. Click ‘refresh’ (green circle icon fourth from the left) and then navigate into the ‘rnaseq’ folder; you should see the ‘female_repl...’ files there. Drag and drop those onto your Windows machine.

You can also view my versions: female_repl1_R1_fastqc.html and female_repl1_R2_fastqc.html


  • What should you pay attention to in the FastQC report?
  • Which is “better”, R1 or R2?


3. Trimmomatic

Now we’re going to do some trimming! We’ll be using Trimmomatic. For a discussion of optimal RNAseq trimming strategies, see MacManes, 2014.

First, load the Trimmomatic software:

module load Trimmomatic/0.32

Next, run Trimmomatic:

java -jar $TRIM/trimmomatic PE female_repl1_R1.fq.gz female_repl1_R2.fq.gz\
     female_repl1_R1.qc.fq.gz s1_se female_repl1_R2.qc.fq.gz s2_se \
     ILLUMINACLIP:$TRIM/adapters/TruSeq3-PE.fa:2:40:15 \

You should see output that looks like this:

Quality encoding detected as phred33
Input Read Pairs: 100000 Both Surviving: 95583 (95.58%) Forward Only Surviving: 4262 (4.26%) Reverse Only Surviving: 86 (0.09%) Dropped: 69 (0.07%)


  • How do you figure out what the parameters mean?
  • How do you figure out what parameters to use?
  • What adapters do you use?
  • What version of Trimmomatic are we using here? (And FastQC?)
  • Are parameters different for RNAseq and genomic?
  • What’s with these annoyingly long and complicated filenames?
  • What do we do with the single-ended files (s1_se and s2_se?)


4. FastQC again

Run FastQC again:

fastqc female_repl1_R1.qc.fq.gz
fastqc female_repl1_R2.qc.fq.gz

(Note that you don’t need to load the module again.)

Copy them to your laptop and open them, OR you can view mine: female_repl1_R1.qc_fastqc.html and female_repl1_R2.qc_fastqc.html

Let’s take a look at the output files:

less female_repl1_R1.qc.fq.gz

(again, use ‘q’ to exit less).


  • Why are some of the reads shorter than others?
  • is the quality trimmed data “better” than before?
  • Does it matter that you still have adapters!?

5. Subset and trim the rest of the sequences

Copy and paste all of the below at once:

gunzip -c ~/RNAseq-semimodel/data/SRR534006_1.fastq.gz | head -400000 | gzip > female_repl2_R1.fq.gz
gunzip -c ~/RNAseq-semimodel/data/SRR534006_2.fastq.gz | head -400000 | gzip > female_repl2_R2.fq.gz

gunzip -c ~/RNAseq-semimodel/data/SRR536786_1.fastq.gz | head -400000 | gzip > male_repl1_R1.fq.gz
gunzip -c ~/RNAseq-semimodel/data/SRR536786_2.fastq.gz | head -400000 | gzip > male_repl1_R2.fq.gz

gunzip -c ~/RNAseq-semimodel/data/SRR536787_1.fastq.gz | head -400000 | gzip > male_repl2_R1.fq.gz
gunzip -c ~/RNAseq-semimodel/data/SRR536787_2.fastq.gz | head -400000 | gzip > male_repl2_R2.fq.gz

java -jar $TRIM/trimmomatic PE female_repl2_R1.fq.gz female_repl2_R2.fq.gz\
     female_repl2_R1.qc.fq.gz s1_se female_repl2_R2.qc.fq.gz s2_se \
     ILLUMINACLIP:$TRIM/adapters/TruSeq3-PE.fa:2:40:15 \

java -jar $TRIM/trimmomatic PE male_repl1_R1.fq.gz male_repl1_R2.fq.gz\
     male_repl1_R1.qc.fq.gz s1_se male_repl1_R2.qc.fq.gz s2_se \
     ILLUMINACLIP:$TRIM/adapters/TruSeq3-PE.fa:2:40:15 \

java -jar $TRIM/trimmomatic PE male_repl2_R1.fq.gz male_repl2_R2.fq.gz\
     male_repl2_R1.qc.fq.gz s1_se male_repl2_R2.qc.fq.gz s2_se \
     ILLUMINACLIP:$TRIM/adapters/TruSeq3-PE.fa:2:40:15 \

Next: Building a new reference transcriptome

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