Updated CTEHR Training (markdown)

CTEHR-Training.md

@@ -1,3 +1,7 @@
+All contents are adapted from [chapkinlab](https://github.com/chapkinlab/sequencing-pipeline/wiki)  
+
+This is a guide to get you started quickly with beginning to use our sequencing pipeline for analyzing your RNA-seq datasets. 
+
 DMSO is the control group and TCDD is the treatment group. We will be using data related to only these groups. There are six samples in total. Ignore the rest. Sequencing data is from two lanes, and these have to be concatenated before processing.
 
 #1. Installation and Setup  
@@ -6,7 +10,13 @@ Log in VPN
 Open terminal on mac  
 Type `ssh -p 22 [email protected]`  
 Type password  
+All the pipeline programs are on Github. These files might need some editing to accommodate project-specific configuration of the pipeline. If you are setting up your pipeline new, navigate to your desired installation directory, and issue the following command.  
 Under folder `Assignment`, exicute `git clone https://github.com/chapkinlab/sequencing-pipeline.git`  
+The pipeline makes use of several external open source tools that you'll need to have installed on your computer. These include:
++The STAR RNA-Seq aligner  
++FastQC  
++Python (with the Pandas library)  
+You'll also need a reference genome and annotation. You can obtain that from [Ensembl](http://useast.ensembl.org/info/data/ftp/index.html).  
 `wget ftp://ftp.ensembl.org/pub/release-84/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna_sm.primary_assembly.fa.gz`  
 `wget ftp://ftp.ensembl.org/pub/release-84/gtf/homo_sapiens/Homo_sapiens.GRCh38.84.gtf.gz`     
 ##20160311
@@ -16,6 +26,7 @@ To verify that the pipeline is installed and up-to-date, navigate to the `sequen
 `cp -r /mnt/nas/Organoid-data-04242014/FastqFile ./home/candice/Assignment/FastqFile`  
 
 #3. Quality Check  
+If the sequencing facility has not done a quality check on the sequencing data, you can run `preqc.sh` to check on the quality of the sequencing.  
 ##20160312
 `mkdir FastqFiles_fastq`
 gunzip every fastq.gz files
@@ -39,6 +50,8 @@ samplelist=(\
 From the sequencing pipeline directory, run `main-scripts/preqc.sh lists/candice_list`  
 
 #4. Dataset Pre processing and List files  
+If your sequences were run on multiple lanes, you will first need to concatenate your files so each sample has only one file. To easily do this, use the `main-scripts/concat.sh` script available in the sequencing-pipeline repository.  
+The `concat.sh` script automatically finds separate `fastq.gz` files from a single experiment (split across several sequencing lanes) and then combines them together into one file that you can then use in the mapping step.   
 `cd sequencing-pipeline/main-scripts`  
 `vi concat.sh`
 modifying the cut commmand on [this line](https://github.com/chapkinlab/sequencing-pipeline/blob/80061158cfebb1dda2c7806779b53466573cc337/main-scripts/concat.sh#L34) from 1-4 to 1-5 in `concat.sh`.   
@@ -48,6 +61,8 @@ remove the # commenting character) [this line](https://github.com/chapkinlab/seq
 (sample list is optional)  
 
 #5. Mapping reads to the genome  
+Now that you know that the sequences have passed QC, it is time to map them against a reference genome. In order to make the processing of our datafiles easier, we need to make an "experiment list" file which describes the location of the samples to be operated on, and the reference genome that they should be mapped against. You will need to make an experiment list file `candice_list` for your samples. The genome that you will reference against (e.g. `grch38-human`) should be the first line in your `candice_list`. `map.sh` is the script to handle mapping your samples against the reference genome. This script is also usually run from the sequencing pipeline directory.   
+##20160313
 Edit `candice_list`:  
 species="grch38-human"  
 samplelist=(\  
@@ -67,4 +82,3 @@ main-scripts/map.sh lists/candice_list 2> err.log | tee out.log
 
 #6. Analysis Summarization  
 #7. Gene Differential Expression Analysis  
-