From d66f8b66382b5ac28f877984b164f77934122fb7 Mon Sep 17 00:00:00 2001 From: apca Date: Mon, 4 Nov 2024 14:45:42 +0100 Subject: [PATCH] Moved ABout info to README and created a new About --- README.md | 82 +++++++++++-------------------------------------------- about.md | 35 +++++++----------------- 2 files changed, 26 insertions(+), 91 deletions(-) diff --git a/README.md b/README.md index e7d83c0..e18d453 100644 --- a/README.md +++ b/README.md @@ -1,81 +1,31 @@ -# Template for a static website using Sphinx and GitHub Pages +#**Nextflow fundamentals training** -## Instructions +Is your code reusable? Are your results reproducible? Tired of reinventing the wheel? The thing is that reproducibility and even repeatability is challenging even if you are very careful about keeping the same software parameters and versions. Do you want to learn how to write reusable, reproducible, and scalable pipelines? -### 1. Create new repository based on this template +**Nextflow** is an advanced workflow management system designed to streamline data-driven processes, especially in the bioinformatics and computational biology fields. It allows users to develop, to execute, and to scale complex workflows across various environments (i.e local machine, Cloud - Azure, AWS, etc... - or HPC). This system is simple and flexible and supports different programming languages (i.e. Python, R, Bash) and containers (e.g Docker, Singularity, etc...). -Create a template based on -[this repository](https://github.com/enryH/notes_template) -by clicking on the "Use this template" button, -see instructions -[here](https://docs.github.com/en/repositories/creating-and-managing-repositories/creating-a-repository-from-a-template#creating-a-repository-from-a-template) +Let us introduce you to Nextflow and unlock you a door to a vibrant community building and maintaining standardized reproducible bioinformatics analyses, trainings, hackathons and resources to make your data-driven analysis scalable an reproducible. -- now you are already publish the site which looks identical to the template site - (see it here) -- jump to step 5 to do that directly. +## Objectives -### 2. Open in GitHub Codespaces (or locally) +In this course you will learn: -- go to [github.com/codespaces](https://github.com/codespaces) -- create a new codespace using the forked repository +- What is Nextflow? -> If you are done, remember to delete the codespace to not see your free credit or money -> wasted. Also inactive (stopped) codespaces use storage for the last 30 days. +- Why is needed? -### 3. Edit files +- Nextflow concepts (e.g. channels, processes and operators, parallellism, reentrancy, reusability) -You will need to know the Markdown to format your text. See -[this overview on GitHub](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) -or [this cheatsheet](https://www.markdownguide.org/cheat-sheet/) to get started. +- Core features (e.g Portability, Scalability, Reproducibility, Modularity) -- update in `conf.py` at least the author, project and copyright information at the top - - also update two urls to your repository: - ```json - "github_url": "https://github.com/enryh/", - "repository_url": "https://github.com/enryh/notes_template", - ``` -- write something about you in `about.md` -- write articles in `folder_topic/article_topic.md` -- update the `index.md` file to include new files -- use [pandoc](https://pandoc.org/try/) to convert your previous files into markdown or - reStructuredText +- Installing Nextflow (requirements, you will use a prepared dev environment) -Troubleshooting: - - don't forget to add new files to the `index.md` file - - each document should have a title (`# title`) using a main heading and otherwise - nested headlines (subheadings followed by sub-subheadings) +- Write and run your first Nextflow script (nextflow script, config file, results, working directory) -### 4. Build the site locally +- Run a nextflow pipeline in a Docker image (run for example a short version of an rnaseq pipeline) -Sphinx uses the configuration file `conf.py` to set up the site. The `requirements.txt` file -contain extensions and themes that are used additionally to sphinx to build the site. -The layout of the website is defined in the `index.md` file. +- Seqera platform (monitoring your pipeline execution) -> Have look at `.github/workflows/build_website.yaml` to see how the site is built -> if you are interested. +- nf-core community (113 bioinformatics standardized workflows) -- Open a terminal (GitHub Codespaces) -- install required packages from [`requirements.txt`](requirements.txt): - ```bash - pip install -r requirements.txt - ``` -- build the site (you could set an alias if you want): - ```bash - sphinx-build -n -W --keep-going -b html ./ ./_build/ - ``` - in case the command is not found, try: - ```bash - python -m sphinx -n -W --keep-going -b html ./ ./_build/ - ``` -- open the site in a browser: - - install ["Live Preview" extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode.live-server) in Visual Studio Code - - open the `_build/index.html` file in the browser (right-click, "Show Preview") - -### 5. Publish the site - -Follow -[these instructions](https://docs.github.com/en/pages/getting-started-with-github-pages/configuring-a-publishing-source-for-your-github-pages-site) -to publish the website using GitHub Pages. - -- Select the `gh-pages` branch as the source for the GitHub Pages site (currently step 7) -- add the deployed url to your "About" on the right sight of the repository +- Resources to keep training \ No newline at end of file diff --git a/about.md b/about.md index 9ee12b9..7a4df07 100644 --- a/about.md +++ b/about.md @@ -1,31 +1,16 @@ # About -**Nextflow fundamentals training** -Is your code reusable? Are your results reproducible? Tired of reinventing the wheel? The thing is that reproducibility and even repeatability is challenging even if you are very careful about keeping the same software parameters and versions. Do you want to learn how to write reusable, reproducible, and scalable pipelines? +## The course +At the Data Science platform we are integrating with the research data management team and we are also building pipelines for our researchers at DTU - Biosustain. To that end we are building and deploying in Azure scalable, portable and reproducible bioinformatics workflows using Nextflow. Nextflow is a workflow orchestration tool designed for developing and executing reproducible and scalable data analysis pipelines. It is a scripting language to develop the workflows based on Groovy, allowing for easy integration with existing software and tools. Nextflow supports parallelization and can run on multiple computing environments, including local systems, high-performance clusters, and cloud platforms. For these characteristics it has been our workflow building language of choice and would like to teach it to our fellows at DTU-Biosustain. -**Nextflow** is an advanced workflow management system designed to streamline data-driven processes, especially in the bioinformatics and computational biology fields. It allows users to develop, to execute, and to scale complex workflows across various environments (i.e local machine, Cloud - Azure, AWS, etc... - or HPC). This system is simple and flexible and supports different programming languages (i.e. Python, R, Bash) and containers (e.g Docker, Singularity, etc...). +## [Data Science platform](https://www.biosustain.dtu.dk/technologies/informatics/data-science-platform) -Let us introduce you to Nextflow and unlock you a door to a vibrant community building and maintaining standardized reproducible bioinformatics analyses, trainings, hackathons and resources to make your data-driven analysis scalable an reproducible. +Data Science has become an essential piece both in academia and industry to accelerate gaining insights into the generated data. As a strategy to integrate high-level analytics we created a centralized Data Science platform (DSP) that provides support to our researchers while promoting standardized data and data processes. -# In this course you will learn: +The DSP team aims to make data science more accessible and inclusive at DTU Biosustain and across the DTU Community. The platform follows a data-centric approach that focuses on data infrastructure, processes, and outputs as ongoing, evolving products rather than one-time projects. Each data product is designed as a multidisciplinary collaboration involving the entire data lifecycle and pursuing standardization and automation, and with data usage in mind. - -- What is Nextflow? - -- Why is needed? - -- Nextflow concepts (e.g. channels, processes and operators, parallellism, reentrancy, reusability) - -- Core features (e.g Portability, Scalability, Reproducibility, Modularity) - -- Installing Nextflow (requirements, you will use a prepared dev environment) - -- Write and run your first Nextflow script (nextflow script, config file, results, working directory) - -- Run a nextflow pipeline in a Docker image (run for example a short version of an rnaseq pipeline) - -- Seqera platform (monitoring your pipeline execution) - -- nf-core community (113 bioinformatics standardized workflows) - -- Resources to keep training +The DSP is based on four pillars: +– Support +– Education +– Innovation +– Tooling