Forum:What Are The Best Bioinformatics Course Materials [2020]
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3.4 years ago

Hi everyone,

I'm gathering course materials for bioinformatics. These can be online pages, ebooks, videos, etc...

Older ressources are listed in this 2010 post: What Are The Best Bioinformatics Course Materials And Videos (Available Online)?

I'll start with the obvious:

Thanks !

course teaching • 2.0k views
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Off the top of my head:

  • For RNAseq, I'd probably mention the edgeR and DESeq2 manuals, and the RNASeq123 vignette over at Bioconductor.
  • In my opinion, beginner-friendly Nextflow tutorials are hard to come by (someone can correct me if I am wrong), and in this regard, I found this tutorial at sequeralabs helpful.
  • And I guess also the comprehensive Trinity RNASeq wiki for de novo transcriptome assembly.
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3.4 years ago
GenoMax 142k

A lot of material was added to YouTube by many enterprising fellow researchers to keep up with training during COVID lockdown. An example is Dr Simon Cockell's Lockdown learning series.

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ATpoint 82k

Specific to single-cell RNA-seq but an absolute must-read: Orchestrating Single-Cell Analysis with Bioconductor

Another really good resource with lots of example code and nice illustrations is the GitHub of the Harvard Chan Bioinformatocs Core, see https://github.com/hbctraining. As a teaser see e.g. their ChIP-seq introduction: https://hbctraining.github.io/Intro-to-ChIPseq/lessons/05_peak_calling_macs.html Some of their code is infrastructure-bound to their HPCs and environments, but one gets a good idea on the concepts.

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3.4 years ago

In the frame of ELIXIR there is also a lot of online material and courses available (slides, e-learning, ...) on a wide range of topics :

https://tess.elixir-europe.org/

The bioinformatics core facility of VIB also offers some online material & courses:

https://www.bits.vib.be/

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3.4 years ago

There are a few "awesome" lists floating around github that are useful and collate resources all in one place. This one has some that are generally useful for teaching/learning.

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I'd say that there are many "awesome" lists on github but few that are awesome.

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