---
title: "Introduction"
teaching: 10
exercises: 0
questions:
- "What is CWL?"
- "What is the goal of this training?"
objectives:
- "Gain a high level understanding of the example analysis."
keypoints:
- "Common Workflow Language is a standard for describing data analysis workflows"
- "We will use an bioinformatics RNA-seq analysis as an example workflow, but does not require in-depth knowledge of biology."
- "After completing this training, you should be able to begin writing workflows for your own analysis, and know where to learn more."
---

# Introduction to Common Worklow Language

The Common Workflow Language (CWL) is an open standard for describing
automated, batch data analysis workflows.  Unlike many programming
languages, CWL is a declarative language.  This means it describes
_what_ should happen, but not _how_ it should happen.  This enables
workflows written in CWL to be portable and scalable across a variety
of software and hardware environments, from workstations to cluster,
cloud, and high performance computing (HPC) environments.  As a
standard with multiple implementations, CWL is particularly well
suited for research collaboration, publishing, and high-throughput
production data analysis.

# Introduction to the example analysis

This training uses a bioinformatics RNA-seq analysis as a motivating
example.  However, specific knowledge of the biology of RNA-seq is
*not* required for these lessons.  For those unfamiliar with RNA-seq,
it is the process of sequencing RNA present in a biological sample.
From the sequence reads, we want to measure the relative numbers of
different RNA molecules appearing in the sample that were produced by
particular genes.  This analysis is called "differential gene
expression".

The entire process looks like this:

![]({{ relative_root_path }}/assets/img/RNAseqWorkflow.png){: height="400px"}

For this training, we are only concerned with the middle analytical
steps (skipping adapter trimming).

* Quality control (FASTQC)
* Alignment (mapping)
* Counting reads associated with genes

In this training, we do not develop the analysis from first
principals, instead we we will be starting from an analysis already
written as a shell script, which will be presented in lesson 2.

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