The trimmomatic workflow should be used to initially process sequence data. You should not use the trimmomatic workflow on low-coverage NIL or RIL data.

Docker Image

The trimmomatic pipeline is very simple and only requires trimmomatic and multiqc to be installed.

The wild-isolate docker file can be used to run the trimmomatic pipeline.



New sequence data should be stored in the appropriate path on the cluster in the raw directory. For example, when adding new sequence data for wild isolates, data should be added to a new folder here:



All FASTQs should end with a _1.fq.gz or a _2.fq.gz. To rename FASTQs you can use:

rename --dry-run --subst .fastq.gz .fq.gz --subst _R1_001 _1 --subst _R2_001 *.fastq.gz

Outside of these simple changes to the filenames, no further changes should be made. The original filenames are potentially useful when tracing issues. Downstream steps use a file to connect the filenames with the appropriate strain or isotype.

To begin running the pipeline you will cd to the directory containing the raw FASTQs and run the pipeline:

# cd to directory of fastqs
nextflow run Andersenlab/trimmomatic-nf

The resulting trimmed FASTQs will be output in the processed directory located up one level from the current directory. For example:

FASTQs are deposited in this directory


You run the pipeline while sitting in the same directory:


And results are output in the following directory: