Recipes for computational biology

Here you can find installation commands, scripts and configurations to run genetic software. This is in order to keep everything tidy.

Starting to use Git/GitHub

Some useful commands are:

Command	Description	Usage
git clone	This command clones repositories from github	git clone https://github.com/gaow/lab-wiki
git status	This command displays the state of your working directory and the staging area	git status
git diff	Let you see differences between the working tree and the index or a tree
git add	This serves to add the file to the repo. Every time you change a file add it to the repo using this command and commiting it	git add filename.md
git commit	Commit refers to saved changes. It will save the changes to file and add the description. The -m command creates a description of the file to help you and others understand the change in the code	git commit -am "Add example"
git push	To upload your local changes into your online repository	git push origin master
git pull	If you have cloned a previous copy to your computer before editing update to the current version that might have been changed by other since you last cloned	git pull
git checkout		git checkout <filename>
git reset		git reset --hard
git log	Keeps track of any change to any file	git log
git blame	Keeps track of changes more specifically. It allows you to easily see the history of modifications of a file, or restore the file to any time point in the history	git blame
git branch BRANCHNAME	to create a branch in someone else's repository	git branch BRANCHNAME
git checkout -b BRANCHNAME	another way of creating branches	git checkout -b ld-module
git push -u origin BRANCHNAME	push commits in the branch and then ask for pull request in github	git push -u origin BRANCHNAME

First GitHub project

1. Create a GitHub repository online going to https://github.com/dianacornejo'
2. Under repositories click new repository and call it compbio-recipes. When creating the repository select "Include a README.md file".
3. Download or "clone" the repo to your local computer $ git clone https://github.com/dianacornejo/compbio-recipes
4. Add files to the repository:
   $ echo "# Recipes for software configurations" > software-config.md $ git add software-config.md $ git commit -am "Add a software configuration file with markdown
5. Push your change to repo into GitHub. This is an important step to upload your changes to the online repository git push
6. Update the local clone of repositories before editing and commiting new information git pull

Iphyton notebook and Jupyterlab

This is to document clearly what you do in research

###SoS installation

###SoS workflow and sos-notebook installation with Conda

• conda install sos sos-pbs -c conda-forge
• conda install sos-notebook jupyterlab-sos sos-papermill -c conda-forge
• conda install sos-r sos-python sos-bash -c conda-forge
• conda install sos-r -c conda-forge
• conda install sos-bash -c conda-forge

Create conda environments and install kernels specifically in one environment

conda create --name py37 python=3.7
conda source activate py37
conda install -n py37 markdown-kernel -c conda-forge #installation of markdown kernel
conda install -n py37 sos-bash -c conda-forge #installation of bash kernel
conda install -n py37 sos-r -c conda-forge #installation of R kernel
conda install -n py37 notebook jupyterlab jupyter_contrib_nbextensions -c conda-forge #installation of jupyterlab and extensions
conda install -n py37 jupyter-docx-bundler -c conda-forge #to save ipynb to docx
jupyter bundlerextension enable --py jupyter_docx_bundler --sys-prefix
conda install -n py37 nbdime -c conda-forge
conda install -n py37 bash_kernel --no-cache-dir -c conda-forge
conda install -n py37 sos-notebook jupyterlab-sos sos-papermill -c conda-forge

Available kernels should look like this in my computer

Available kernels:
  bash            /Users/dianacornejo/miniconda3/envs/py37/share/jupyter/kernels/bash
  calysto_bash    /Users/dianacornejo/miniconda3/envs/py37/share/jupyter/kernels/calysto_bash
  ir              /Users/dianacornejo/miniconda3/envs/py37/share/jupyter/kernels/ir
  markdown        /Users/dianacornejo/miniconda3/envs/py37/share/jupyter/kernels/markdown
  python3         /Users/dianacornejo/miniconda3/envs/py37/share/jupyter/kernels/python3
  sos             /Users/dianacornejo/miniconda3/envs/py37/share/jupyter/kernels/sos

###To see the installed kernels in jupyter

jupyter kernelspec list

How to access you SoS notebook using docker

• $ docker run -it mdabioinfo/sos:latest /bin/bash
• $ docker run -d -p 9999:8888 mdabioinfo/sos-notebook
• $ docker ps Finds the of the instances (look at the last column of the output, for example mine is: priceless_archimedes)
• $ docker logs priceless_archimedes

Now enter the URL into a browser. Tip: change the text within ( ) to your ip

http://(10.124.143.97):8888/?token=2fcb6e87003ac99f26b7a1c021327d7dd31595e0e4b60173

• $ docker run -d -p 8888:8888 -v $HOME:/Users/dmcs2245/work mdabioinfo/sos-notebook

If you get a message stating that the port is already allocated use -p 9999:8888 instead

Making the WGS example for SoS from Gao

1. Using docker access the SoS notebook
2. Clone the repository to you local folder git clone https://github.com/vatlab/sos-docs/
3. Prepare the data as explained in Gao's tutorial
4. Run sos run WGS_Call.ipynb --samples k9-test/test_samples.manifest -j2

A directory containing the results will be created