---
title: "Introduction"
-teaching: 0
+teaching: 10
exercises: 0
questions:
-- "Key question (FIXME)"
+- "What is CWL?"
+- "What is the goal of this training?"
objectives:
-- "First learning objective. (FIXME)"
+- "Gain a high level understanding of the example analysis."
keypoints:
-- "First key point. Brief Answer to questions. (FIXME)"
+- "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
-
-The goal of this training is to walk through the development of a
-best-practices CWL workflow by translating an existing bioinformatics
-shell script into CWL. Specific knowledge of the biology of RNA-seq
-is *not* a prerequisite for these lessons.
-
-These lessons are based on "Introduction to RNA-seq using
-high-performance computing (HPC)" 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 here:
-
-https://github.com/hbctraining/Intro-to-rnaseq-hpc-O2
-
-## Background
-
-RNA-seq is the process of sequencing RNA in a biological sample. From
-the sequence reads, we want to measure the relative number of RNA
-molecules appearing in the sample that were produced by particular
-genes. This analysis is called "differential gene expression".
+# 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:
-
+{: height="400px"}
For this training, we are only concerned with the middle analytical
steps (skipping adapter trimming).
* Alignment (mapping)
* Counting reads associated with genes
-## Analysis shell script
-
-This analysis is already available as a Unix shell script, which we
-will refer to in order to build the workflow.
-
-Some of the reasons to use CWL over a plain shell script: portability,
-scalability, ability to run on platforms that are not traditional HPC.
-
-rnaseq_analysis_on_input_file.sh
-
-```
-#!/bin/bash
-
-# Based on
-# https://hbctraining.github.io/Intro-to-rnaseq-hpc-O2/lessons/07_automating_workflow.html
-#
-
-# This script takes a fastq file of RNA-Seq data, runs FastQC and outputs a counts file for it.
-# USAGE: sh rnaseq_analysis_on_input_file.sh <name of fastq file>
-
-set -e
-
-# initialize a variable with an intuitive name to store the name of the input fastq file
-fq=$1
-
-# grab base of filename for naming outputs
-base=`basename $fq .subset.fq`
-echo "Sample name is $base"
-
-# specify the number of cores to use
-cores=4
-
-# directory with genome reference FASTA and index files + name of the gene annotation file
-genome=rnaseq/reference_data
-gtf=rnaseq/reference_data/chr1-hg19_genes.gtf
-
-# make all of the output directories
-# The -p option means mkdir will create the whole path if it
-# does not exist and refrain from complaining if it does exist
-mkdir -p rnaseq/results/fastqc
-mkdir -p rnaseq/results/STAR
-mkdir -p rnaseq/results/counts
-
-# set up output filenames and locations
-fastqc_out=rnaseq/results/fastqc
-align_out=rnaseq/results/STAR/${base}_
-counts_input_bam=rnaseq/results/STAR/${base}_Aligned.sortedByCoord.out.bam
-counts=rnaseq/results/counts/${base}_featurecounts.txt
-
-echo "Processing file $fq"
-
-# Run FastQC and move output to the appropriate folder
-fastqc $fq
-
-# Run STAR
-STAR --runThreadN $cores --genomeDir $genome --readFilesIn $fq --outFileNamePrefix $align_out --outSAMtype BAM SortedByCoordinate --outSAMunmapped Within --outSAMattributes Standard
-
-# Create BAM index
-samtools index $counts_input_bam
-
-# Count mapped reads
-featureCounts -T $cores -s 2 -a $gtf -o $counts $counts_input_bam
-```
-
+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 %}
projects / rnaseq-cwl-training.git / blobdiff