[](https://swc-slack-invite.herokuapp.com/)
-This repository generates the corresponding lesson website from [The Carpentries](https://carpentries.org/) repertoire of lessons.
+This repository generates the corresponding lesson website from [The Carpentries](https://carpentries.org/) repertoire of lessons.
## Contributing
repository. For making your contribution, we use the GitHub flow, which is
nicely explained in the chapter [Contributing to a Project](http://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project) in Pro Git
by Scott Chacon.
-Look for the tag . This indicates that the maintainers will welcome a pull request fixing this issue.
+Look for the tag . This indicates that the maintainers will welcome a pull request fixing this issue.
## Maintainer(s)
-Current maintainers of this lesson are
-
-* FIXME
-* FIXME
-* FIXME
+Current maintainers of this lesson are
+* Peter Amstutz <peter.amstutz@curii.com>
## Authors
exercises: 0
questions:
- "What is CWL?"
-- "What are the requirements for this training?"
- "What is the goal of this training?"
objectives:
- "Understand how the training will be motivated by an example analysis."
keypoints:
- "Common Workflow Language is a standard for describing data analysis workflows"
-- "This training assumes some basic familiarity with editing text files, the Unix command line, and Unix shell scripts."
- "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."
---
suited for research collaboration, publishing, and high-throughput
production data analysis.
-# Introduction to this training
-
-The goal of this training is to walk the student through the
-development of a best-practices CWL workflow, starting from an
-existing shell script that performs a simple RNA-seq bioinformatics
-analysis. 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.
-
-This training assumes some basic familiarity with editing text files,
-the Unix command line, and Unix shell scripts.
-
-Specific knowledge of the biology of RNA-seq is *not* a prerequisite
-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.
-
-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.
-
# Introduction to the example analysis
-RNA-seq 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".
+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:
* Alignment (mapping)
* Counting reads associated with genes
-In this training, we are not attempting to develop the analysis from
-scratch, instead we we will be starting from an analysis already
-written in a shell script, which will be supplied in lesson 2.
+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.
{% include links.md %}
root: . # Is the only page that doesn't follow the pattern /:path/index.html
permalink: index.html # Is the only page that doesn't follow the pattern /:path/index.html
---
-FIXME: home page introduction
-<!-- this is an html comment -->
+This training will walk you through the development of a
+best-practices 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.
-{% comment %} This is a comment in Liquid {% endcomment %}
+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
>
-> FIXME
+> This training assumes some basic familiarity with editing text files,
+> the Unix command line, and Unix shell scripts.
+>
+> Specific knowledge of the biology of RNA-seq is *not* a prerequisite
+> 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.
+>
{: .prereq}
{% include links.md %}
projects / rnaseq-cwl-training.git / commitdiff