ISCB COVID-19 Tutorial Collection
Below is a list of tutorials on general computational biology issues for use by computational biologists and bench scienists in this time of added digital education activities. The are two lists;
- A list of tutorials presented at recent ISMB meetings
- A list of tutorials submitted by ISCB members.
Tutorials and Special Sessions Presented at Recent ISMB Meetings
Tutorials Submitted by ISCB Members
To submit a tutorial to be added to the collection use the submission form.
| Tutorial Title | Suggested By |
| Description and Reason for Suggesting | |
| Bioinformatics for Benched Biologists | Lior Pachter |
| This workshop demonstrates an analysis of a single-cell RNA-seq dataset starting from the reads. It was designed for an online 1-2 hour Zoom workshop and uses a python notebook that runs in about 30 minutes on Google Colab. | |
| Bioinformatics for Benched Biologists II (R Edition) | Lior Pachter |
| This workshop demonstrates an analysis of a single-cell RNA-seq dataset starting from the reads. It was designed for an online 1-2 hour Zoom workshop and uses an R notebook that runs on Google Colab. | |
| GPU-Accelerated Single-Cell Analysis with RAPIDS | Avantika Lal |
| This tutorial demonstrates how to accelerate single-cell RNA-seq analysis using GPUs (Graphics Processing Units). RAPIDS, a free and open-source software suite for GPU-accelerated data science, is used to perform preprocessing, clustering, visualization and differential expression analysis of single cells, 5-100x faster than on CPUs. A demonstration is provided in the form of a Jupyter notebook. Single-cell RNA-seq is essential for Covid-19 research, e.g. identifying susceptible cells. Analysis requires speed and interactivity but tools are slow. This GPU pipeline performs scRNA-seq analysis 4-90 times faster than standard tools. |
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| A Practical Introduction to Reproducible Computational Workflows | Peter W. Rose |
| This hands-on tutorial teaches participants the key requirements and practical skills to setup a reproducible and reusable computational research environment. The tutorial is intended for Python users and anyone interested in using Jupyter Notebooks. We also cover collaborative development practices. After working through this tutorial, participants should be able to set up their own projects by applying the principles and techniques learned and publish reproducible research protocols. Computational notebooks offer new opportunities to communicate computational analyses. This tutorial presents step-by-step instructions following our: "Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks" paper. |
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