---
This training will walk you through the development of a
-best-practices CWL workflow. At the conclusion of this training, you
+best-practices Common Workflow Language (CWL) workflow. At the conclusion of this training, you
should have a grasp of the essential components of a workflow, and
have a basis for learning more.
-These lessons are based on [Introduction to RNA-seq using
-high-performance computing
-(HPC)](https://github.com/hbctraining/Intro-to-rnaseq-hpc-O2) lessons
-developed by members of the teaching team at the Harvard Chan
-Bioinformatics Core (HBC). The original training, which includes
-additional lectures about the biology of RNA-seq, can be found at that
-link.
-
> ## Prerequisites
>
> This training assumes some basic familiarity with editing text files,
> for these lessons. Although orignally developed to solve big data
> problems in genomics, CWL is not domain specific to bioinformatics,
> and is used in a number of other fields including medical imaging,
-> astronomy, geospatial, and machine learning. We hope that you will
-> find this training useful regardless of your area of research.
+> astronomy, geospatial imaging, and machine learning. We hope that
+> you will find this training useful regardless of your area of
+> research.
>
{: .prereq}
+These lessons are based on [Introduction to RNA-seq using
+high-performance computing
+(HPC)](https://github.com/hbctraining/Intro-to-rnaseq-hpc-O2) lessons
+developed by members of the teaching team at the Harvard Chan
+Bioinformatics Core (HBC). The original training, which includes
+additional lectures about the biology of RNA-seq, can be found at that
+link.
+
{% include links.md %}
projects / rnaseq-cwl-training.git / blobdiff