Introductory Bioinformatics for Neuroscience

Last Updated: 2026-08-10
Last Delivered: 2026-08-11

Check out the recording of all MSI tutorials on our YouTube channel:
https://www.youtube.com/@UofMMSI/videos

1: Introduction

1.1 Goals

In this tutorial we will cover the following topics:

• An introduction to MSI and ITN/Neuro dedicated bioinformatics support
• An overview of bulk RNA-sequencing
• An introduction to single cell RNA-sequencing, including guidelines for experimental design
• A hands-on guide to using the Allen Brain Cell Atlas
• A hands-on guide to analyzing single cell/nucleus RNA-seq data in R through Open On Demand

1.2 Intro to the MSI

The Minnesota Supercomputing Institute (MSI) provides compute resources to labs and researchers at the UMN.

1.3 ITN/Neuro Dedicated Bioinformatics Support

Dr. Ying Zhang is a RI bioinformatics analyst who is dedicated to working with ITN/Neuroscience department. If you have bioinformatics or experimental design questions, you can contact Dr. Zhang at zhan2142@umn.edu.

1.4 Bulk RNA-sequencing

Here we provide an overview of bulk RNA sequencing and data generation/analysis.

1.5 Single Cell RNA-Sequencing

Here we discuss how single cell/nuclei transcriptomes are measured. Additional details on single cell bioinformatic analysis methods are provided in our Introduction to Single Cell Analysis Tutorial.

1.6: Single Cell Experimental Design

MSI Bioinformatics analysts developed this Single Cell Experimental Design template to help you consider important aspects of sample preparation and sequencing.

Some key points from that document include:

• What organism are you studying? Probe-based technologies (ie. 10X Flex) are available only for a subset of organisms.
• Can you get samples at the same time? How are you going to arrange the labwork to avoid confounding factors?
• Morphology of cells: can you actually capture the cells inside a droplet? If not, consider single nuclei or combinatorial labeling approaches.
• Ease of dissociation/stickyness: can you isolate “single” cells? You might need protocol optimizations.
• How abundant are the cell types of interest in your tissue? If rare, you might need enrichment approaches like cell sorting before library preparation.
• Are you planning to do statistical analysis? Then we strongly recommend biological replication!

We created this table to compare the 3 current types of single cell kits that are used by researchers at UMN. You may find it helpful when you are planning your experiments:

Another important consideration in experimental design is minimizing batch effects. Here we provide an example of how a batch effect can be introduced:

Remember, you can always reach out to MSI RI Bioinformatics for a free of charge consultation to help you with planning your sequencing experiments.

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2: Getting Started with the Allen Brain Cell (ABC) Atlas

Here we provide a brief overview of how single-cell atlases are used. Then, we will take a look at the Allen Brain Cell atlas website and explore their single-cell/nucleus browser.

Human ABC single-cell atlas walkthrough

3: Single Cell RNA-seq analysis on MSI

3.1 Open On Demand: files, terminal, interactive apps

Open on Demand (OOD) is web-browser based interface that allows access to MSI resources. We can see the files stored in the system and access computing nodes through a terminal or through graphical user interfaces and interactive apps.

3.2 Hands-on Single-cell Analysis

In this section I’ll briefly show you how to connect to RStudio through OOD, the interface and how to create your first Seurat object. We will explore the structure of the data, walk through the main analysis steps and learn how to generate common plots. The software needed and data is preinstalled. We will follow this step by step tutorial showing you all the commands you will need, you can download it and keep it for your records.

Click here to open the tutorial

4: Resources

We hope this tutorial facilitates your introduction to the world of single cell analysis, but there is so much more to explore! The links here point to useful information along the experiment and analysis journey:

Experimental design

• Single Cell Experimental Design and Planning: https://umn-msi-ri.github.io/bioinfo-training/sc_expt_design_template

Using MSI Resources

• Getting access: https://msi.umn.edu/getting-started/getting-started-and-access
• Batch jobs: https://msi.umn.edu/computing/slurm-job-submission-and-scheduling
• Interactive computing: https://msi.umn.edu/computing/interactive-hpc/open-ondemand
• Tutorials page: https://msi.umn.edu/getting-started/help/tutorials
• Youtube channel: https://www.youtube.com/@UofMMSI/videos
• Biological databases: https://msi.umn.edu/consulting-and-research/bioinformatics-guides/bioref

Single cell analysis

• Seurat package: https://satijalab.org/seurat/
• MSI’s Single-cell tutorial: https://z.umn.edu/single_cell

FEEDBACK CONTACT

Thank you for reading this document, we hope it helps your research! This tutorial was prepared by Natalia Calixto Mancipe, Marissa Macchietto, and Sarah Munro, in the Core Bioinformatics group at MSI. We’d love to hear your feedback, please email us at ribhelp@msi.umn.edu.

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Glossary

This section describes the specialized terms that we use in this tutorial. Synonyms for some terms are also included.

Molecular Biology (Wet Bench) Terms

• (Sequencing) Adapter: Short nucleotide oligomers that are ligated to the 5′ and/or 3′ ends of a fragment in a sequencing library. These are non-biological, artificial sequences.
• Library: A collection of nucleic acid molecules that represents the sample organism that you wish to study. For RNAseq, it is usually a collection of cDNA (complementary DNA) sequences synthesized from RNA molecules.
• Library Preparation: The protocol that converts an extracted sample of nucleic acid into a sequencing library, read to run on the instrument. Various library preparation protocols exist, so it is important to know the details of the protocols your experiments are using. The details of the protocol do affect how your data should be handled.
• Sequencing Read: A string of nucleotide bases identified by the sequencing instrument. The nucleotide bases are derived from the sequence of the inserts of the sequencing library.

Bioinformatic Terms

• Depth: The number of times a particular nucleotide position has been seen in sequencing reads. Higher depth means the nucleotide has been read more times and thus typically has less uncertainty. Note that some people use “coverage” or “fold coverage” to refer to this definition of “depth.”
• Gene Annotation: (Noun) The assignment of regions of a reference genome to gene models. The assignments are stored in “annotation files” with formats such as GTF or GFF3. A gene annotation file must only be used with its accompanying reference genome file.
• Cell Annotation: process of identifying and labeling the biological classification of cells or clusters of cells.
• Mapping: A process by which short sequencing reads are compared to a reference genome to identify where they originated. This is the process that is used in inferring the relative abundance of transcripts in a RNA extraction. I distinguish mapping from alignment in that alignment is used to identify homologous sequences (rather than simply identifying a genomic position) and uses different algorithms from mapping. Some people use “mapping” and “alignment” interchangeably.
• Mapping Quality: A numerical value that describes the confidence with which the mapping position is known. Higher values indicate higher confidence. Each mapped read will have a mapping quality value associated with it.
• Normalization: A technique for adjusting the relative gene expression values across samples to account for variation in library size.
• (Raw) Counts Matrix: A matrix of integers describing the assignment of mapped reads to annotated gene models across multiple cells.

• Reference Genome: A string of nucleotides that describes the genome of a given species. Sequencing reads are mapped against a reference genome, and gene expression is quantified using the accompanying gene annotation file.

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