Perform adapter and quality trimming on sequencing reads with reporting


Name (Type)

meta (map)

Metadata about the specific run.
I guess this can be used if you are running many different images in a single nextflow pass?
I have no clue.
Groovy Map containing sample information
e.g. [ id:‘test’, single_end


image (file)

Obtained from cellpose documentation: Inputs
You can use tiffs or PNGs or JPEGs. We use the image loader from scikit-image.
Single plane images can read into data as nY x nX x channels or channels x nY x nX.
Then the channels settings will take care of reshaping the input appropriately for the network.
Note the model also rescales the input for each channel so that 0 = 1st percentile of image values and 1 = 99th percentile.

image_metadata (file)

This file will let Cellpose know which channels to use for segmentation.
The default will be channel 0.

Model_to_use (string)

This will define which model to use.
The default will be nuclear.
All models from the model zoo should be usable, the exact string has to be passed.


Name (Type)

meta (map)

Groovy Map containing sample information
e.g. [ id:‘test’, single_end


versions (file)

File containing software versions


reads (file)

The trimmed/modified fastq reads


reads_fail (file)

Reads that failed the preprocessing (Optional with —discard args setting)


reads_unpaired (file)

Reads without matching mates in paired-end files (Optional)


stats (file)

trimming/qc text stats file


txt (file)

trimming/qc text txt files from —debug option


statspdf (file)

trimming/qc pdf report file


log (file)

fastq log file



GPLv3 License

FaQCs combines several features of currently available applications into a single, user-friendly process, and includes additional unique capabilities such as filtering the PhiX control sequences, conversion of FASTQ formats, and multi-threading. The original data and trimmed summaries are reported within a variety of graphics and reports, providing a simple way to do data quality control and assurance.