Computational Bioinformatics

Research Resources

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Welcome Statement

Hello! Welcome to research!

Of research, a wise person once wrote that, one never completes a research project in isolation of others. This bright quote signifies that finding and correctly harnessing appropriate resources for research is what makes for a successful conclusion.

On this page, I will be adding links and short discussions for some of the resources that I think might be helpful to you in your adventures in your investigative work in science.

Data Sources

Trusted databases for sequences, structures, expression, and pathways.

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Career Paths

Common roles, skills, and job boards for bioinformatics careers.

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Tools and Environments

Recommended software, setup steps, and reproducibility tips.

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Bioinformatics Resources

Tutorials, tools, and useful links for research workflows.

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Writing Resources

Guides for research writing, citations, and academic style.

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Bioinformatics Resources

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.

Career Paths

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

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

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.

Writing Resources

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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