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Tutorial Collection

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;

  1. A list of tutorials presented at recent ISMB meetings
  2. A list of tutorials submitted by ISCB members.

Tutorials and Special Sessions Presented at Recent ISMB Meetings

Session Title Session Details
Recent Advances in Statistical Methods and Computational Algorithms for Single-Cell Omics Analysis ISMB/ECCB 2019 Tutorial AM2
Biomarker discovery and machine learning in large pharmacogenomics datasets - Part 2 ISMB/ECCB 2019 Tutorial PM5
Single cell RNA-seq toolkit - Part 4 ISMB 2018 Tutorial AM1
Integrated network analysis: Cytoscape automation using R and Python ISMB 2018 Tutorial AM3
Computational methods for comparative regulatory genomics ISMB 2018 Tutorial AM4
Ontologies in computational biology - Part 4 ISMB 2018 Tutorial PM8
Text Mining for Biology and Healthcare IMSB/ECCB 2019 Special Session 01
Omics Data Formats, Compression and Storage: Present and Future - Part 2D ISMB/ECCB 2019 Special Session 05
3D Genomics: Computational approaches for analyzing the role of three-dimensional chromatin organization in gene regulation - Part 2 ISMB 2018 Special Session 01
Omics Data Compression and Storage: Present and Future - Part 4 ISMB 2018 Special Session 03
SCANGEN: Single-cell cancer genomics - Part 3 ISMB 2018 Special Session 05


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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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