diff --git a/conf/modules.config b/conf/modules.config
index 0abff92..5d8398e 100644
--- a/conf/modules.config
+++ b/conf/modules.config
@@ -55,7 +55,7 @@ process {
             params.shortread_clipmerge_adapter1 ? "--adapter_sequence ${params.shortread_clipmerge_adapter1}" : "",
             // filtering options
             "--length_required ${params.shortread_clipmerge_minlength}",
-            params.perform_shortread_complexityfilter && params.shortread_complexityfilter_tool == 'fastp' ? "--low_complexity_filter --complexity_threshold ${params.shortread_complexityfilter_fastp_threshold}" : ''
+            (params.perform_shortread_complexityfilter && params.shortread_complexityfilter_tool == 'fastp') ? "--low_complexity_filter --complexity_threshold ${params.shortread_complexityfilter_fastp_threshold}" : ''
         ].join(' ').trim()
         ext.prefix = { "${meta.id}_${meta.run_accession}" }
         publishDir = [
diff --git a/docs/usage.md b/docs/usage.md
index 47ac952..54ffce0 100644
--- a/docs/usage.md
+++ b/docs/usage.md
@@ -185,7 +185,7 @@ Complexity filtering is primarily a run-time optimisation step. It is not necess
 
 There are currently three options for short-read complexity filtering: [`bbduk`](https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/bbduk-guide/), [`prinseq++`](https://github.com/Adrian-Cantu/PRINSEQ-plus-plus), and [`fastp`](https://github.com/OpenGene/fastp#low-complexity-filter).
 
-The tools offer different algorithms and parameters for removing low complexity reads. We therefore recommend reviewing the pipeline's [parameter documentation](https://nf-co.re/taxprofiler/parameters) and the documentation of both tools (see links above) to decide on optimal methods and parameters for your dataset.
+The tools offer different algorithms and parameters for removing low complexity reads. We therefore recommend reviewing the pipeline's [parameter documentation](https://nf-co.re/taxprofiler/parameters) and the documentation of the tools (see links above) to decide on optimal methods and parameters for your dataset.
 
 You can optionally save the FASTQ output of the run merging with the `--save_complexityfiltered_reads`. If running with `fastp`, complexity filtering happens inclusively within the earlier shortread preprocessing step. Therefore there will not be an independent pipeline step for complexity filtering, and no independent FASTQ file (i.e. `--save_complexityfiltered_reads` will be ignored) - your complexity filtered reads will also be in the `fastp/` folder in the same file(s) as the preprocessed read.