Condensed instructions and conda install

README.md

@@ -1,106 +1,49 @@
 # longread-UMI-pipeline
 A collection of scripts for processing longread UMI data.
-Tested on Linux 3.10.0
 
-<b> Citation: </b> \
+<b> Pipeline version reference: </b> \
 Karst, S. M., Ziels, R. M., Kirkegaard, R. H., & Albertsen, M. (2019). Enabling high-accuracy long-read amplicon sequences using unique molecular identifiers and Nanopore sequencing. bioRxiv, 645903.
 https://www.biorxiv.org/content/10.1101/645903v2
 
-## Installing the Easy Way -- via Conda
-### Requirements/Dependencies 
-Conda or Miniconda3 installed  
-`usearch` version 10 or higher
-
-### Clone from github
-1. Go to desired installation directory, open a terminal and run:  \
-   `git clone https://github.com/ziels/longread-UMI-pipeline`
-
-2. Go to scripts directory: \
-   `cd longread-UMI-pipeline/scripts`  
-
-3. Modify `dependencies.sh` with path to `usearch`  
-Change line `export USEARCH=usearch_path` to give your exact file path to the `usearch` executable file (instead of `usearch_path`). 
-
-### Install conda environment 
-   `conda env create -f environment.yaml`
-
-1. Check that Conda env is installed \
-   `conda info —-envs` 
-
-Make sure you get something like: 
-
-   > `# conda environments:`\
-   > `#`\
-   >  ` longread-UMI       <path to conda envs>/longread-UMI ` 
-   
-Note the installation path of the `longread-UMI` environment (for next steps)
-
-2. Activate conda environment \
-   `conda activate longread-UMI` \
-   Or, depending on your conda version: `source activate longread-UMI`
-
-### Editing porechop adapters with custom primers
-1. Find path of conda environments from command \
-   `conda info —-envs` 
-
-2. Check the `porechop` path works: \
-   `ls < path to conda environments >/longread-UMI/lib/python3.6/site-packages/porechop` 
-
-Make sure you see an `adapters.py` returned from the above command.
-
-3. Back-up and replace `adapters.py`\
-   `mv <path_to_conda_environments>/longread-UMI/lib/python3.6/site-packages/porechop/adapters.py <path_to_conda_environments>/longread-UMI/lib/python3.6/site-packages/porechop/adapters_old.py`
-
-(From within `longread-UMI-pipeline/scripts` directory):\
-   `cp ./adapters.py <path_to_conda_environments>/longread-UMI/lib/python3.6/site-packages/porechop/adapters.py`
-
-### Test longread-UMI-pipeline on test data:  
-   Go to `/path/to/longread-UMI-pipeline/test_data`   
-   Open a terminal in the directory and run \
-   `../longread_UMI_pipeline.sh -d test_reads.fq -s 10 -c 30 -t 1`
-
-
-## Installing the Hard Way -- Manual installation
-### Requirements/Dependencies
-
-
-### Clone from github
-1. Go to desired installation directory, open a terminal and run:  
-   `git clone https://github.com/SorenKarst/longread-UMI-pipeline`
-
-### Make bash scripts executable
-1. Go to longread-UMI-pipeline directory, open a terminal and run:  
-   `find . -name "*.sh" -exec chmod +x {} \;`
-
-### Create symlink to run longread-UMI-pipeline and longread-UMI-mockanalysis from terminal
-1. Create symlink in ~/bin by opening a terminal and run:  
-   `mkdir -p ~/bin`  
-   `ln -s /path/to/longread-UMI-pipeline/longread_UMI_pipeline.sh ~/bin/longread-UMI-pipeline`  
-   `ln -s /path/to/longread-UMI-pipeline/longread_UMI_mockanalysis.sh ~/bin/longread-UMI-mockanalysis`
-
-### Change paths to dependencies
-1. Open /path/to/longread-UMI-pipeline/scripts/dependencies.sh in a texteditor.
-2. Change all paths under "Program paths" to reflect installation paths on your system.
-3. If unsure of the paths try to type `which <function>` in the terminal. I.e. `which racon`.
-4. Install any missing dependencies.
-
-### Customize porechop adaptors.py to be able to detect custom primers
-1. We recommend to make a seperate installation of porechop to use with the longread-UMI-pipeline.
-2. Go to path/to/porechop/porechop/
-3. Backup current adapters.py.
-4. Replace current adapters.py with path/to/longread-UMI-pipeline/scripts/adapters.py.
-
-### Test scripts
+## Conda installation
+
+1. Requirements/Dependencies
+   OS tested (Linux 3.10.0, Ubuntu 14.04, Ubuntu 16.04)
+  `usearch` >=10
+2. Download installer script from terminal \
+   `wget https://raw.githubusercontent.com/SorenKarst/longread-UMI-pipeline/v0.2/scripts/install_conda.sh`
+3. Run installation script from terminal and follow instructions \
+   `bash ./install_conda.sh` 
+
+## Manual install
+
+1. Requirements/Dependencies \
+   OS tested (Linux 3.10.0, Ubuntu 14.04, Ubuntu 16.04) \
+   See `scripts/longread-UMI-pipeline_version_dump.txt`
+2. Clone from github in terminal \
+   `git clone https://github.com/SorenKarst/longread-UMI-pipeline/tree/v0.2`
+3. Make bash scripts executable \
+   `find ./longread-UMI-pipeline -name "*.sh" -exec chmod +x {} \;`
+4. Install dependencies \
+   See `./longread-UMI-pipeline/scripts/install_dependencies.sh` for inspiration.
+5. Change paths to dependencies \
+   Modify `./longread-UMI-pipeline/scripts/dependencies.sh` in a texteditor.
+6. Customize porechop adaptors.py to be able to detect custom primers \
+   Replace current `adapters.py` with `./longread-UMI-pipeline/scripts/adapters.py`
+
+## Usage
+
+### Test data
 1. Open a terminal anywhere and run:  
   `longread-UMI-pipeline -h` or `/path/to/longread-UMI-pipeline -h`
 2. Test longread-UMI-pipeline on test data:  
    Go to /path/to/longread-UMI-pipeline/test_data  
    Open a terminal in the directory and run `longread-UMI-pipeline -d test_reads.fq -s 10 -c 30 -t 1`
 
-## Run pipeline on Zymo mock data
-1. Create a working directory, open a terminal, download the Zymo mock fastq data and decompress:  
+### Nanopore R9.4.1 data from Zymo mock
+1. Create a working directory and download the Zymo mock fastq data from terminal:  
    `wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR333/003/ERR3336963/ERR3336963_1.fastq.gz; gunzip -c ERR3336963_1.fastq.gz > reads.fq`  
-2. Open a terminal in the directory and run:  
+2. Run pipeline:  
   `longread-UMI-pipeline -d reads.fq -s 1000000 -c 30 -t <Number-of-threads>`
-3. Open a terminal in the directory and run:  
+3. Run data QC:  
   `longread-UMI-mockanalysis <Number-of-threads>`