Learning Objectives

The Dataset

The dataset we are using for this workshop is part of a larger study described in Kenny PJ et al., Cell Rep 2014. The authors are investigating interactions between various genes involved in Fragile X syndrome, a disease of aberrant protein production, which results in cognitive impairment and autistic-like features. The authors sought to show that RNA helicase MOV10 regulates the translation of RNAs involved in Fragile X syndrome.

Raw data

From this study we are using the RNA-seq data which is publicly available in the Sequence Read Archive (SRA).

NOTE: If you are interested in how to obtain publicly available sequence data from the SRA we have some materials on this linked here.

Metadata

In addition to the raw sequence data we also need to collect information about the data, also known as metadata. We are usually quick to want to begin analysis of the sequence data (FASTQ files), but how useful is it if we know nothing about the samples that this sequence data originated from?

Some relevant metadata for our dataset is provided below:

Implementing data management best practices

In a previous lesson we describe the data lifecycle and the different aspects to consider when working on your own projects. Here, we implement some of those strategies to get ourselves setup before we begin with any analysis.

Image acquired from the Harvard Biomedical Data Management Website

Planning and organization

For each experiment you work on and analyze data for, it is considered best practice to get organized by creating a planned storage space (directory structure). We will start by creating a directory that we can use for the rest of the workshop. First, make sure that you are in your home directory.

$ cd
$ pwd

This should return /home/rc_training. Create the directory rnaseq and move into it.

$ mkdir rnaseq
$ cd rnaseq

Next, we will create a project directory and set up the following structure to keep our files organized.

rnaseq
  ├── logs
  ├── meta
  ├── raw_data  
  ├── results
  └── scripts

This is a generic structure and can be tweaked based on personal preference and the analysis workflow.

$ mkdir logs meta raw_data results scripts

File naming conventions

Another aspect of staying organized is making sure that all the directories and filenames for an analysis are as consistent as possible. You want to avoid names like alignment1.bam, and rather have names like 20170823_kd_rep1_gmap-1.4.bam which provide a basic level of information about the file. This link and this slideshow have some good guidelines for file naming dos and don’ts.

Documentation

In your lab notebook, you likely keep track of the different reagents and kits used for a specific protocol. Similarly, recording information about the tools used in the workflow is important for documenting your computational experiments.

README files

After setting up the directory structure it is useful to have a README file within your project directory. This is a plain text file containing a short summary about the project and a description of the files/directories found within it. An example README is shown below. It can also be helpful to include a README within each sub-directory with any information pertaining to the analysis.

## README ##
## This directory contains data generated during the Introduction to RNA-seq workshop
## Date: 

There are five subdirectories in this directory:

raw_data : contains raw data
meta:  contains...
logs:
results:
scripts:

Exercise

  1. Take a moment to create a README for the rnaseq/ folder (hint: use vim to create the file). Give a short description of the project and brief descriptions of the types of files you will be storing within each of the sub-directories.

Obtaining data

Let’s populate the rnaseq/ project with some data. The FASTQ files are located on the O2 cluster in the /n/groups space. Copy them over from the path shown below, into your raw_data directory:

$ cp /n/groups/hbctraining/unix_lesson/raw_fastq/*.fq ~/rnaseq/raw_data/

NOTE: When obtaining data from your sequencing facility, the data will not be stored on O2 and so a simple copy command (cp) will not suffice. The raw sequence data will likely be located on another remote computer/server that is hosted by the sequencing facility and you will be given login credentials to access it. To copy it over you can use commands like rsync, wget or scp. These are all commands that can help securely copy the data over to the appropriate location on O2. We have some information linked here if you would like to learn more.

Now the structure of rnaseq/ should look like this:

rnaseq
  ├── logs
  ├── meta
  ├── raw_data
  │   ├── Irrel_kd_1.subset.fq
  │   ├── Irrel_kd_2.subset.fq
  │   ├── Irrel_kd_3.subset.fq
  │   ├── Mov10_oe_1.subset.fq
  │   ├── Mov10_oe_2.subset.fq
  │   └── Mov10_oe_3.subset.fq
  ├── README.txt
  ├── results
  └── scripts

Okay, we are all set to begin the analysis!

This lesson has been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.