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Class Overview

Keeping track of data analysis steps is as important as laboratory experiment procedures. These processes ensure that researchers can retrace steps during trouble shooting, enable scientists to share procedures with their peers and promote reproducibility of results in research. Open source bioinformatics software require the use of code or command line. For instance, RNA sequencing analysis packages such as DESeq2 and edgeR require knowledge of R. Biopython, enables tasks such as sequence manipulation and querying of NCBI databases requires knowledge of Python. This class will introduce participants to Jupyter Lab, an open source tool that allows investigators to keep analysis steps, code, and output in one place. It is compatible with languages used in bioinformatics such as R, Python, and Bash but can also be used for other languages such as Julia and C/C++.

Disclaimer

This class will not make participants experts in using Jupyter Lab or scripting. Also, this class is a demo, experience using or installation onto personal computer of Jupyter Lab is not required to participate. BTEP staff will be happy to meet with participants to discuss installing or accessing Jupyter Lab after the class, as they are many options. Just email us at ncibtep@nih.gov if you need help! Further, do not worry about the coding part of this class but focus more on what Jupyter Lab can do in terms of organizing analysis steps.

Learning Objectives

After this class, participants will be able to

  • Have obtained an appreciation for Jupyter Lab as an one-stop shop for organizing analysis steps, code, and output.
  • Be aware of or able to describe the steps involved in using Jupyter Lab for documenting data analysis including:
    • Initiating a new Jupyter Lab session.
    • Navigating through and transferring data into the file explorer.
    • Starting a language specific notebook.
    • Writing formatted text and code.