Data Science in Omics Introduction

An OMICS Course Part 1: Shell, Organizing and Genomics

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Project Organization

Notes

Questions:

Objectives:

Getting your project started

Project organization is one of the most important parts of a sequencing project, and yet is often overlooked amidst the excitement of getting a first look at new data. You can always reorganize your project when (for example) you decide to publish the data, but it’s much better to get yourself organized from the beginning.

You should approach your sequencing project similar to any biological experiment, which ideally begins with good experimental design.

We’re going to assume that you’ve already designed a beautiful sequencing experiment to address your biological hypothesis, collected appropriate samples, are using appropriate controls, and that you have enough statistical power to answer the questions you’re interested in asking… RIGHT ?!?!? These steps are all incredibly important, but beyond the scope of these lessons.

For all of those steps (collecting specimens, extracting DNA, prepping your samples, etc.) you’ve likely kept a lab notebook that details how and why you did each step. However, the process of documentation doesn’t start or stop at the sequencer!

Genomics projects can quickly accumulate hundreds of files across dozens of folders. Every computational analysis you perform over the course of your project is going to create many files, which becomes a problem when you’ll inevitably want to run some of those analyses again, or document how you performed the analyses. Try to think in terms of your “past-self”, “present-self” and “future self”. For instance, your present-self might have made significant headway into your project, but then has to remember the PCR conditions your past-self used to create your sequencing library months prior.

Examples of just a few “future-self” questions that might arise:

The only way to prevent these from being problems is by good documentation! It’s worthwhile to consider your future-self as an entirely separate collaborator. The better your documentation is, the more this ‘collaborator’ will thank you!

Luckily enough, recording your computational experiments is even easier than recording lab data. Copy/Paste will become your friend, sensible file names will make your analysis understandable (by you and your collaborators), and writing the methods section for your next paper should be easy! With this in mind, let’s have a look at the best practices for documenting your genomics project.

Options for this course

Here is where the course and the workshops start to diverge into options.
Option A is to use an online cloud computing resource (currently AWS) as is described in the Data Carpentry Genomics Workshop Setup pages.
Note that AWS costs money and we cannot use this option at this time. BUT we will be imitating this process using the Cowboy computer!

Option B is on the same page as Option A but is “Using the lessons on your local machine”. For Windows users, this works, mostly, using the Ubuntu for Windows 10 Bash shell. For Mac or Linux machines, you might want to go for it!!!

But we couldn’t possibly install all those software on all our differentl computers and operating systems, and expect them to work. Imagine if this class had 100 students!

Option C might be available where you work using the University’s supercomputers. This has now changed as we are migrating to the “Pete” supercomputer, so we are going to work with the next option.

Option D is a great option. Clicking this link will open a NEW Lesson on the Cyverse “Atmosphere” if the cloud instance remains available.

Option A: Example of using AWS

AWS: Follow the instructions on the Data Carpentry Genomics Workshop Setup pages.

While logged into your AWS instance (or from inside of a terminal window)
We start by creating a directory that we can use for the rest of the workshop/lesson. First navigate to your AWS home directory. Use cd Enter, and confirm that you are in the correct directory using the pwd command.

$ cd
$ pwd

You should see the following as output:

/home/dcuser

In-class Exercise

Use the mkdir command to make the following directories:

  • dc_workshop
  • dc_workshop/docs
  • dc_workshop/data
  • dc_workshop/results

Solution

$ mkdir dc_workshop
$ mkdir dc_workshop/docs
$ mkdir dc_workshop/data
$ mkdir dc_workshop/results

Use ls -R to verify that you have created these directories. The -R option for ls stands for recursive. This option causes ls to return the contents of each subdirectory within the directory iteratively.

$ ls -R dc_workshop

You should see the following output:

dc_workshop/:
data  docs  results

dc_workshop/data:

dc_workshop/docs:

dc_workshop/results:

Organizing your files

Before beginning any analysis, it’s important to save a copy of your raw data. The raw data should never be changed. Regardless of how sure you are that you want to carry out a particular data cleaning step, there’s always the chance that you’ll change your mind later or that there will be an error in carrying out the data cleaning and you’ll need to go back a step in the process. Having a raw copy of your data that you never modify guarantees that you will always be able to start over if something goes wrong with your analysis. When starting any analysis, you can make a copy of your raw data file and do your manipulations on that file, rather than the raw version. We learned in the READINGS for today’s lesson how to prevent overwriting our raw data files by setting restrictive file permissions.

NOTE: We previously used the chmod command in the Bash shell to make files executable, but this didn’t work in the Windows GitBash terminal. This is a good time to review file permissions while we are using a “real” linux environment.

We will store any results generated from our analysis in the results folder. This guarantees that we won’t confuse results file and data files in six months or two years when your future self is looking back through your files in preparation for publishing your study.

The docs folder is the place to store notes about how your analyses were carried out, any written contextual analysis of your results, and documents related to your eventual publication.

Documenting your activity on the project

When carrying out wet-lab analyses, most scientists work from a written protocol and keep a hard copy of written notes in their lab notebook. Daily notes include any things they did differently from the written protocol. This detailed record-keeping process is just as important when doing computational analyses. Luckily, it’s easier to record the steps you’ve carried out computationally than it is when working at the bench.

The history command is a convenient way to document all the commands you have used while analyzing and manipulating your project files. Let’s document the work we have done on our project so far.

View the commands that you have used so far during this session using history:

$ history

You should see that history contains all your entered shell commands. There are probably more commands than you have used for the current project. To view the last n lines of your history (where n = approximately the last few lines you think relevant) we can use tail. For our example, to view the last 7 shell commands:

$ history | tail -n 7

Using your knowledge of the shell, use the append redirect >> to create a file called dc_workshop_log_XXXX_XX_XX.sh (Use the four-digit year, two-digit month, and two digit day, e.g. dc_workshop_log_2020_10_27.sh)

You may have noticed that your history contains the history command itself. To remove this redundancy from our log, let’s use the nano text editor to fix the file:

$ nano dc_workshop_log_2020_10_27.sh

(Remember to replace the 2020_10_27 with your actual lesson or workshop date.)

From the nano screen, you can use your cursor to navigate, type, and delete line numbers, or any redundant lines.

We know nano is useful and not too complicated, but it can be frustrating to edit documents, because you can’t use your mouse to navigate to the part of the document you would like to edit. Here are some useful keyboard shortcuts for moving around within a text document in nano. You can find more information by typing Ctrl-G within nano.

key action
Ctrl-Space to move forward one word
Alt-Space to move back one word
Ctrl-A to move to the beginning of the current line
Ctrl-E to move to the end of the current line
Ctrl-W to search

Add a date line and comment to the line where you have created the directory, for example:

# 2020_10_27   
# Created sample directories for the Data Carpentry workshop

The bash shell treats the # character as a comment character.
Any text on a line after a # character is ignored by bash when evaluating the text as code with one exception.

Next, remove any lines of the history that are not relevant by navigating to those lines and using your delete key. Save your file and close nano.

Your file should look something like this:

# 2020_10_27
# Created sample directories for the Data Carpentry workshop

mkdir dc_workshop
mkdir dc_workshop/docs
mkdir dc_workshop/data
mkdir dc_workshop/results

If you keep this file up to date, you can use it to re-do your work on your project if something happens to your results files. To demonstrate how this works, first delete your dc_workshop directory and all of its subdirectories. Look at your directory contents to verify the directory is gone.

$ rm -r dc_workshop
$ ls
shell_data	dc_workshop_log_2020_10_27.sh

Then run your workshop log file as a bash script. You should see the dc_workshop directory and all of its subdirectories reappear.

$ bash dc_workshop_log_2020_10_27.sh
$ ls
shell_data	dc_workshop dc_workshop_log_2020_10_27.sh

It’s important that we keep our workshop log file outside of our dc_workshop directory if we want to use it to recreate our work. It’s also important for us to keep it up to date by regularly updating with the commands that we used to generate our results files.

Congratulations! You’ve finished your introduction to using the shell for genomics projects. You now know how to navigate your file system, create, copy, move, and remove files and directories, and automate repetitive tasks using scripts and wildcards. With this solid foundation, you’re ready to move on to apply all of these new skills to carrying out more sophisticated bioinformatics analysis work. Don’t worry if everything doesn’t feel perfectly comfortable yet. We’re going to have many more opportunities for practice as we move forward on our bioinformatics journey!

References

A Quick Guide to Organizing Computational Biology Projects

Keypoints: