Computational Bioinformatics

Computational Bioinformatics

An interdisciplinary, hands-on course that builds research-ready bioinformatics skills.

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Introduction

This site represents a class in a box that you are welcome to use to develop and /or apply to your own class of a similar theme.

Here, you will find a wealth of objectives, notes, assignments, ideas and systems of structure which may help to strengthen your approach to pedagogy, for seemingly any level of teaching.

These materials comprise a course that continues to be developed by Oliver Bonham-Carter, PhD. at Allegheny College in Meadville, Pennsylvania, USA.

If you find these materials helpful, I invite you to use them to the benefit of your students and to enrich your class!

I look forward to hearing from you if you would like to get in-touch.

  • Oliver Bonham-Carter
  • obonhamcarter (a) allegheny (dot) edu

Interdisciplinary focus

Bridge biology, computing, and data science while building practical analysis skills.

Project-driven labs

Apply algorithms and tools in weekly labs, then report results clearly and ethically.

Research-ready outcomes

Finish with a final project that synthesizes data, methods, and presentation.

Description

An introduction to the development and application of methods, from the computational and information sciences, for the investigation of biological phenomena. In this interdisciplinary course, students integrate computational techniques with biological knowledge to develop and use analytical tools for extracting, organizing, and interpreting information from genetic sequence data. Often participating in team-based and hands-on activities, students implement and apply useful bioinformatics algorithms. During a weekly laboratory session students employ cutting-edge software tools and programming environments to complete projects, reporting on their results through both written documents and oral presentations. Students are invited to use their own departmentally approved laptop in this course; a limited number of laptops are available for use during class and lab sessions.

Course Objectives

Students successfully completing this class will have developed:

  • A “big-picture” view of bioinformatics.
  • An understanding of the objectives and limitations of bioinformatics.
  • An understanding of the biological foundations of bioinformatics (genes and genomes, gene expression, etc.).
  • An understanding of the computational foundations of bioinformatics (programming, databases, etc.).
  • An understanding of how genetic information is obtained and processed.
  • The ability to use basic bioinformatics software tools to study genetic information.

Throughout the semester students also will enhance their ability to write and present ideas about bioinformatics in a clear and compelling fashion. Students will gain practical experience in the design, implementation, and analysis of bioinformatics research during laboratory sessions and a final project. Finally, students will develop a richer understanding of the fascinating connections between biological systems, analysis and automation.

An Ethical Interest

Throughout the semester students will be exposed to famous dilemmas in technology which will arrive with discussions to encourage positive thinking in ethics. For example, the course will introduce students to ethically inclined concepts in the generation of technology. Such terms include liability, ethics, responsibility, privacy, information governance, data security and others.

TextBooks

  • Exploring Bioinformatics: A Project-based Approach by Caroline St. Clair and Jonathan E. Visick.
  • Think Python by Allen B. Downey.
  • Along with reading the required books, you will be asked to study many additional articles from a wide variety of conference proceedings, journals, and the popular press.

Other Useful Textbooks

  • Think Python by Allen B. Downey.

  • BUGS in Writing: A Guide to Debugging Your Prose by Lyn Dupr'e. Addison-Wesley Professional. ISBN-10: 020137921X and ISBN-13: 978-0201379211, 704 pages, 1998. References to the textbook are abbreviated as “BIW”.

  • Writing for Computer Science (Second Edition). Justin Zobel. Springer ISBN-10: 1852338024 and ISBN-13:978-1852338022, 270 pages, 2004. References to the textbook are abbreviated as “WFCS”.

  • On Being a Scientist: A Guide to Responsible Conduct in Research (Third Edition). Committee on Science, Engineering, and Public Policy, National Academy of Sciences, National Academy of Engineering, and Institute of Medicine. ISBN: 0309119715, 82 pages, 2009. References to the textbook are abbreviated as “OBAS”.

Welcome to a resources page for bioinformatics research. Here you will find links for data, tools, tutorials, and related resources that may be helpful to your work.

Software and installations

Python programming resources

Articles

Tutorials

Research organizations


Note

If you find a good link for bioinformatics research that you believe would fit nicely here, please let me know.

Bioinformatics Career Paths

This page gives a quick overview of roles, skills, and entry points in computational biology and bioinformatics.

Roles you might see

  • Bioinformatics Analyst
    • Focus: data cleaning, pipelines, and reports.
    • Typical tools: Python, R, Bash, SQL, workflow tools.
  • Computational Biologist
    • Focus: algorithm development, research modeling, and analysis.
    • Typical tools: Python, R, statistics, machine learning basics.
  • Genomics Data Scientist
    • Focus: high-throughput sequencing, ML, and reproducible analytics.
    • Typical tools: Python, R, ML libraries, cloud platforms.
  • Bioinformatics Software Engineer
    • Focus: production software, APIs, and data systems.
    • Typical tools: Python, Go, Java, Docker, CI/CD.
  • Clinical Genomics Specialist
    • Focus: variant interpretation, regulated pipelines, clinical reporting.
    • Typical tools: standards (HGVS), ClinVar, pipelines, QC.

Core skills to build in this course

  • Sequence alignment, assembly, and annotation basics.
  • Data handling and reproducible analysis with notebooks.
  • Clear reporting: methods, results, and limitations.
  • Ethical data use, privacy, and bias awareness.

How to prepare

  • Build a small portfolio: 2 to 3 notebooks or reports.
  • Practice writing short analysis summaries for non-technical readers.
  • Learn the command line and Git basics.

Where to look for roles

  • Academic labs and research institutes.
  • Biotech and pharma companies.
  • Hospitals and clinical genomics labs.
  • Government or public health agencies.

Example job boards

Suggested class activity

  • Find one job posting and map the required skills to course topics.

Data Sources for Bioinformatics

This page lists reliable datasets and databases for sequence, structure, expression, and pathway analysis. Each resource includes common use cases and citation guidance.

Core sequence and genome resources

  • NCBI (GenBank, RefSeq, GEO, SRA)
    • https://www.ncbi.nlm.nih.gov/
    • Use for: general sequence records, curated reference genomes, gene expression data, and raw reads.
    • Cite: follow the dataset accession page; many provide a preferred citation.
  • ENA (European Nucleotide Archive)
  • Ensembl
  • UCSC Genome Browser

Protein, structure, and function

Expression, variation, and pathways

Citation tips

  • Prefer accession numbers over informal dataset names.
  • Check the dataset page for a citation or DOI.
  • Include database name and version when available.

Suggested class activities

  • Pick one database and write a short guide: what it stores, how to search it, and how to cite it.
  • Compare two sources for the same gene or protein and note differences in annotation.

Tools and Environments

This page describes a recommended setup for course work and a checklist for getting started.

Suggested environment checklist

  • Install Python and a package manager.
  • Create a new environment for this course.
  • Install core libraries: biopython, pandas, numpy, matplotlib.
  • Open a notebook and run a short test script.

Example conda workflow

conda create -n bioinfo python=3.11
conda activate bioinfo
conda install biopython pandas numpy matplotlib
pip install jupyterlab

Reproducibility tips

  • Keep environment files (e.g., environment.yml).
  • Record software versions in reports.
  • Use clear file naming and folder structure.

Suggested class activity

  • Set up a new environment and run a short sequence parsing script.

writer logo

How can I write better?

When completing any assignment in research or in your classes, the quality of your writing is very important. Below are resources that may help you improve clarity, structure, and academic style.


Online resources