readme + lint in test dir too

.github/workflows/main.yml

@@ -27,7 +27,7 @@ jobs:
     - name: Lint workflow
       uses: snakemake/[email protected]
       with:
-        directory: .
+        directory: .test
         snakefile: workflow/Snakefile
         args: "--lint"
 
@@ -44,11 +44,4 @@ jobs:
       with:
         directory: .test
         snakefile: workflow/Snakefile
-        args: "--use-conda --show-failed-logs --cores 3 --conda-cleanup-pkgs cache --all-temp"
-
-    - name: Test report
-      uses: snakemake/[email protected]
-      with:
-        directory: .test
-        snakefile: workflow/Snakefile
-        args: "--report report.zip"
+        args: "--use-conda --show-failed-logs --cores 3 --conda-cleanup-pkgs cache --all-temp"

README.md

@@ -7,7 +7,7 @@
 This is a snakemake workflow, designed to generate OTU-tables from demultiplexed ONT amplicon data. The final outputs are designed to be compatible with R-packages [ampvis2](https://kasperskytte.github.io/ampvis2/index.html) and [phyloSeq](https://github.com/joey711/phyloseq) to visualize the microbial composition of the analyzed samples.
 The workflow expects the input files to be demultiplexed and basecalled prior to running the workflow. 
 The workflow filters based on user-input in the config file `config/config.yaml` where it is possible to change filters in regards to amplicon length and quality, using [chopper](https://github.com/wdecoster/chopper). The read characteristics for each sample can be assesed using the shell-script script located at `scripts/nanoplot.sh`. More information regarding usage of the script can be found [here](#usage-of-stats-script).
-Biologically meaningful reads from each sample/barcode are clustered into OTU's using [Vsearch](https://github.com/torognes/vsearch) and denoising using [UNOISE3](https://doi.org/10.1093/bioinformatics/btv401) algorithm.
+Biologically meaningful reads from each sample/barcode are clustered into OTU's using [Vsearch](https://github.com/torognes/vsearch) and denoised using [UNOISE3](https://doi.org/10.1093/bioinformatics/btv401) algorithm.
 OTU's from every sample/barcode are merged and polished using [Racon](https://github.com/isovic/racon).
 Taxonomy is infered to the OTU's by either [Vsearch](https://github.com/torognes/vsearch) using a curated SINTAX database (more information on databases [here](#databases)) or [blastn](https://blast.ncbi.nlm.nih.gov/doc/blast-help/) against a blastn formatted database.