Trimming reads with Trimmomatic

Note: the trimmomatic manual is the go-to-guide for parameters and commands.

E. coli trimming

Trim the E. coli data lightly:

cd ~/notebooks
cd ecoli

java -jar /home/Trimmomatic-0.36/trimmomatic-0.36.jar PE \
 ecoli-R1.fq.gz ecoli-R2.fq.gz \
 ecoli-R1-pe.fq ecoli-R1-orphans.fq ecoli-R2-pe.fq ecoli-R2-orphans.fq \
  ILLUMINACLIP:/home/Trimmomatic-0.36/adapters/TruSeq3-PE.fa:2:40:15 \
  LEADING:2 TRAILING:2 \
  SLIDINGWINDOW:4:2 \
  MINLEN:50

You should see:

Input Read Pairs: 100000 Both Surviving: 99802 (99.80%) Forward Only Surviving: 186 (0.19%) Reverse Only Surviving: 12 (0.01%) Dropped: 0 (0.00%)

Trim the E. coli data stringently:

java -jar /home/Trimmomatic-0.36/trimmomatic-0.36.jar PE \
 ecoli-R1.fq.gz ecoli-R2.fq.gz \
 ecoli-R1-pe.fq ecoli-R1-orphans.fq ecoli-R2-pe.fq ecoli-R2-orphans.fq \
  ILLUMINACLIP:/home/Trimmomatic-0.36/adapters/TruSeq3-PE.fa:2:40:15 \
  LEADING:2 TRAILING:2 \
  SLIDINGWINDOW:4:20 \
  MINLEN:50

You should see:

Input Read Pairs: 100000 Both Surviving: 83439 (83.44%) Forward Only Surviving: 8960 (8.96%) Reverse Only Surviving: 4821 (4.82%) Dropped: 2780 (2.78%)

You can use khmer’s readstats.py to evaluate the loss of sequence –

readstats.py ecoli-R?.fq.gz
readstats.py ecoli-R?-*.fq

Now, run fastqc on the trimmed data:

fastqc ecoli-R1-pe.fq
fastqc ecoli-R2-pe.fq

Trimming the yeast RNAseq data

Next, try building your own commands to trim the yeast RNAseq data - stringently, and lightly.

Next: Evaluating read mismatch statistics: mapping and de novo

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