Research Resources on Computational Bioinformatics

Bioinformatics Resources

Mon, 01 Jan 0001 00:00:00 +0000

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

Play with code from W3’s Python Tutorial
Write code locally using Jupyter Interactive Python
Think Python by Allen B. Downey: Publisher Website
Python Programming Language
Online R programming

Articles

Getting started with Markdown

Tutorials

Tools and learning links

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

Mon, 01 Jan 0001 00:00:00 +0000

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

Mon, 01 Jan 0001 00:00:00 +0000

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)
- https://www.ebi.ac.uk/ena/
- Use for: raw sequencing data and assembled sequences.
Ensembl
- https://www.ensembl.org/
- Use for: genome browsers, annotations, comparative genomics.
UCSC Genome Browser
- https://genome.ucsc.edu/
- Use for: visualization, genome tracks, custom annotations.

Protein, structure, and function

UniProt
- https://www.uniprot.org/
- Use for: protein sequences, function, and cross references.
PDB (Protein Data Bank)
- https://www.rcsb.org/
- Use for: 3D protein and nucleic acid structures.
AlphaFold DB
- https://alphafold.ebi.ac.uk/
- Use for: predicted protein structures.
InterPro
- https://www.ebi.ac.uk/interpro/
- Use for: domains, families, and functional analysis.

Expression, variation, and pathways

GEO (Gene Expression Omnibus)
- https://www.ncbi.nlm.nih.gov/geo/
- Use for: microarray and RNA-seq expression datasets.
GTEx (Genotype-Tissue Expression)
- https://gtexportal.org/
- Use for: tissue-specific expression patterns.
ClinVar
- https://www.ncbi.nlm.nih.gov/clinvar/
- Use for: variant clinical significance.
KEGG
- https://www.kegg.jp/
- Use for: pathways, reactions, and pathway maps.
Reactome
- https://reactome.org/
- Use for: curated biological 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

Mon, 01 Jan 0001 00:00:00 +0000

Tools and Environments

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

Recommended software

Miniconda or Mamba for package management.
- https://docs.conda.io/en/latest/miniconda.html
- https://mamba.readthedocs.io/en/latest/installation.html
Python 3.11+
- Core language for notebooks and scripting.
Jupyter (Lab or Notebook)
- https://jupyter.org/
Git + GitHub
- https://git-scm.com/
- https://github.com/
BLAST
- https://blast.ncbi.nlm.nih.gov/Blast.cgi
Biopython
- https://biopython.org/

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

Mon, 01 Jan 0001 00:00:00 +0000

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

Maytum Center for Student Success: Writing and Speaking Consultants at Allegheny College’s Writing Center
University of Maryland: Online Guide to Writing