HPC-Pipes (November 2024)

HP-Pipes logo

A 2-day course on best practices for setting up, running, and sharing reproducible bioinformatics pipelines and workflows


Join us to learn about the general process of building a robust pipeline (regardless of data type) using workflow languages, environment/package managers, optimized HPC resources, and FAIRly managed data and tools. On course completion, participants will be able to use this knowledge to design their own custom pipelines with tools appropriate for their individual analysis needs.

 

The course will provide guidance on how to automate data analysis using common workflow languages such as Snakemake or Nextflow. Subsequently, we will delve into ensuring the reproducibility of pipelines and explore available options. Participants will learn how to share their data analysis and software with the research community. We will also delve into different strategies for managing the produced research data. This includes addressing the challenges posed by large volumes of data and exploring computational approaches that aid in data organization, documentation, processing, analysis, storing, sharing, and preservation. These discussions will encompass the reasons behind the increasing popularity of Docker and other containers, along with demonstrations on how to effectively utilize package and environment managers like Conda to control the software environment within a workflow. Finally, participants will learn how to manage and optimize their pipeline projects on HPC platforms, using compute resources efficiently.

 

Requirements
The workshop is for researchers, professors, and PhD students at SUND who seek to acquire skills in effectively managing data and analyses in bioinformatics. Knowledge of R/Python and bash is required, as well as basic understanding of an omics analysis pipeline.

We strongly recommend taking this course after completing the course HPC-Launch, a single day course which covers theoretical concepts for HPC and RDM in health data science.

 

Interested?

➡️ If you are an employee, sign up here

➡️ If you are a PhD student, sign up here

 

Course Disclaimers: 
      • The course is free of charge and intended for all KU researchers, and especially PhD students, at SUND
      • A no-show free will be implemented for registrants who do not attend the course
      • If you are a PhD student and you would like to receive ECTS credits for participating in this course, you must enroll through the PhD school school system
      • If you are not a PhD student and sign up via the PhD school registration link, you will be charged for the course
      • Please be advised that course participation via the PhD school happens under the PhD school's terms and conditions which HeaDS has no influence over