team-repo-template

❗ This top heading should be the name of your project i.e. BiocSwirl or SNVariome. Anything between 2 exclamation marks is intended to be deleted. Any content that isn't a heading or an optional heading can be deleted as well. The structure of this readme is open to any creative changes, but the main components of Background/Data/Usage/Team Members should remain. You're free to add images and get creative about how you want your readme to look. ❗

❗ The configs and notebooks directories are also optional. We recommend taking a look at cookiecutter for datascience or cookiecutter for computational biology to get ideas on structuring your projects. Also, use a .gitignore that fits the main programming language of your project. ❗

Table of Contents

• Template
  • Background
  • Data
  • Usage
    • Installation
    • Requirements Can be named Dependencies as well
    • Activate conda environment Optional
    • Steps to run Optional depending on project
      • Step-1
      • Step-2
  • Results Optional depending on project
  • Team Members

Background

❗ Include background on the project, project description, and significance. This will be converted to your team's abstract by the end of the hackathon. This should be updated by Monday, August 1st to include feedback given. ❗

Data

❗ Discuss the data you used and how it can be accessed. ❗

Usage

❗ How will someone not involved in your project be able to run the code or use it. ❗

Installation

❗ If installation is required, please mention how to do so here. ❗

Installation simply requires fetching the source code. Following are required:

• Git

To fetch source code, change in to directory of your choice and run:

git clone -b main \
    [email protected]:u-brite/team-repo-template.git

Requirements

❗ Note any software used (including Python or R packages), operating system requirements, etc. and its version so that your project is reproducible. It does not have to be in the below format ❗

OS:

Currently works only in Linux OS. Docker versions may need to be explored later to make it useable in Mac (and potentially Windows).

Tools:

• Anaconda3
  • Tested with version: 2020.02

Activate conda environment

❗ Optional: Depends on project. ❗

Change in to root directory and run the commands below:

# create conda environment. Needed only the first time.
conda env create --file configs/environment.yaml

# if you need to update existing environment
conda env update --file configs/environment.yaml

# activate conda environment
conda activate testing

Steps to run

❗ Optional: Depends on project. ❗

Step 1

python src/data_prep.py -i path/to/file.tsv -O path/to/output_directory

Step 2

python src/model.py -i path/to/parsed_file.tsv -O path/to/output_directory

Output from this step includes -

output_directory/
├── parsed_file.tsv               <--- used for model
├── plot.pdf- Plot to visualize data
└── columns.csv - columns before and after filtering step

Note: The is an example note with a link.

Results

❗ If your project yielded or intends to yield some novel analysis, please include them in your readme. It can be named something other than results as well. ❗

Team Members

Tarun Mamidi | [email protected] | Team Leader
Shaurita Hutchins | [email protected] | Co-leader