Published February 15, 2024 | Version release-2024.2.0
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zaneveld/full_spectrum_bioinformatics: Full Spectrum Bioinformatics Development Release 2024.2.0


Full Spectrum Bioinformatics Development Release 2024.2.0

What's Changed

New Content

Quickly introduce command line software

The new 'Duck vs Yeast' exercise lets students who have just learned how to navigate the command line immediately apply those skills to identify homologs of duck delta crystallin protein in yeast. This is intended to provide an immediate example of how one might use command line software with a few valued parameters in a bioinformatic analysis.

Quickly learn to make graphs and run basic statistics in python

This new release shifts teaching approach for new students who have just learned python. Instead of starting with more traditional python topics, the text instead now introduces how to accomplish some useful tasks with just a little bit of python first. After a brief introduction to strings, ints, lists and calling functions, we immediately establish some 'quick wins' by using python to graph (matplotlib boxplots or scatterplots) and statistically analyze (T-test or Pearson regression) hard-coded data that we enter by hand using lists. We then immediately build on this in the 'Another Quick Win' chapter by discussing the Tidy data format, and how to load tabular Tidy-format data into python. One reason for this change is to allow learners to see how to organize data from their final bioinformatic analyses so it is easy to graph and analyze before they start writing analytical code.

  • Added Quick Wins in Python section by @zaneveld in
  • Updated 'another quick win' and associated resources by @zaneveld in

Added discussion of scientific writing for bioinformatics

This release included a guide to installing and using Zotero for reference management contributed by Dr. Mushtaq Bilal. The goal is to make citation less time-consuming so that it is easier to appropriately reference papers that establish the background for our bioinformatics projects.

  • Initial commit of Dr. Bilal's Zotero tutorial as jupyter notebook for… by @zaneveld in
  • Zotero by @zaneveld in

The release also includes updates to guidance for writing about the literature.

  • Lit review by @zaneveld in

Added discussion of merging tables in pandas

A new chapter covers merging tables using Pandas. This is important for many projects that draw data from multiple sources. Examples might include annotating gene functional categories in a table of gene ids, compiling demographic characteristics of cities, states or countries from government sources, etc.

  • Adding data files for table merging chapter by @zaneveld in
  • Adding the merging tables chapter by @zaneveld in
  • Updated the merging tables section to include information on avoiding… by @zaneveld in


This release includes several edits to improve chapters

  • Additional edits to the command line chapter by @zaneveld in
  • Update exercise_little_brother_is_missing.ipynb by @zaneveld in
  • Update to DataFrame chapter for data downloads and series math explanations by @zaneveld in
  • Edited the analyzing tabular omic data in python chapter by @zaneveld in
  • Updated text and the graph in the sequencing depth chapter by @zaneveld in
  • Updated reading response link in merging and filtering data in pandas… by @zaneveld in
  • Updated fasta file reading chapter text and added an exercise by @zaneveld in
  • Add Git bash install instructions to Command Line interfaces chapter by @zaneveld in


This release includes fixes to some bugs and dead links

  • Fasta parser bug by @zaneveld in
  • Fixed broken image link by @zaneveld in
  • Fixing capitalization of FASTA_banner.png image link by @zaneveld in
  • Correct column name to patient1 in examples; fix minor typos. by @yeemey in

Development of Future Chapters

This release includes some behind-the-scenes work drafting sections that are not yet ready for use. This includes some preliminary text and graphics for a chapter introducing the use of GitHub, as well as a case study demonstrating how to remap column names if they are encoded with ids, as is common in some US government datasets, using the NHANES data.

  • Added a draft version of the chapter on using GitHub and supporting images by @zaneveld in
  • Update to github draft chapter by @zaneveld in
  • Initial commit of NHANES sleep data tutorial in python by @zaneveld in

New Contributors

  • @yeemey made their first contribution in
  • Dr. Mushtaq Bilal contributed a chapter on Zotero

Full Changelog:



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