From b1611d9971baf76c21a011bcfcf05c1119757fd1 Mon Sep 17 00:00:00 2001 From: npappas <n.pappas@uu.nl> Date: Wed, 9 Sep 2020 11:15:50 +0200 Subject: [PATCH] include full link to git clone, edit some lists --- README.md | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index de82629..0c7a43d 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ ``` # Clone this repo -$ git clone this_repo pvogs_function +$ git clone https://git.science.uu.nl/n.pappas/pvogs_function.git # Get in there $ cd pvogs_function @@ -74,7 +74,8 @@ Currently, this workflow was built and tested on a local machine with an X serve `cd` into the root directory of this repo. -- Dry run: +- Dry run + Always a good idea before launching the whole worfklow ``` $ snakemake --use-conda -j16 -np @@ -87,6 +88,7 @@ If the dry run completed with no errors you can execute the worfklow by removing $ snakemake --use-conda -j16 -p ``` - Speed up environment creation with mamba + If `mamba` is available in your snakemake environment, or if you created a new environment with the `environment.yml` provided here: ``` @@ -94,6 +96,7 @@ $ snakemake --use-conda -j16 --conda-frontend mamba ``` - Jupyter integration + A central notebook is used for all visualization and machine learning (model search) purposes. Its main output is the `results/RF/best_model.pkl` file. @@ -118,9 +121,10 @@ Something along the [guidelines from snakemake](https://snakemake.readthedocs.io ## Output -The output of the whole workflow is produced and stored within a `results` directory. This looks like below. +The output of the whole workflow is produced and stored within a `results` directory. +This has the structure shown below. (several directories and files have been omitted) -Th most prominent ones are marked with a short description: +The most prominent ones are marked with a short description: ``` # Skipping several thousands of intermediate files with the -I option $ tree -n -I '*NC*.fasta|*_genes.*|*.gff|*.log' results -- GitLab