X-Git-Url: https://git.arvados.org/rnaseq-cwl-training.git/blobdiff_plain/17e18678b6fbcc09220f597e55e7574dc9cae60a..3fbeb188954cdc09529323816e0cbb25ec1daa19:/_episodes/01-introduction.md

diff --git a/_episodes/01-introduction.md b/_episodes/01-introduction.md
index 81819f9..fe005cc 100644
--- a/_episodes/01-introduction.md
+++ b/_episodes/01-introduction.md
@@ -4,13 +4,11 @@ teaching: 10
 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."
 ---
@@ -28,40 +26,16 @@ standard with multiple implementations, CWL is particularly well
 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:
 
@@ -74,8 +48,8 @@ steps (skipping adapter trimming).
 * 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.
 
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