Computational Bioinformatics

Assignments

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Assignments at a Glance

All assignments are hosted in the course GitHub organization. Use the cards below to jump to each repository.

Assignment 1

Researching and reporting on bioinformatics research tools (I).

Repository

Objectives

Use JupyterLite to pull, complete, and submit homework.
Use GitHub and git to manage submissions.
Make a basic edit to a Python program.
Research and report on bioinformatics tools.

Assignment 2

Central Dogma of Biology and Bioinformatics.

Repository

Objectives

Connect the Central Dogma of Biology to bioinformatics practice.
Explore Python debugging and problem-solving strategies.
Run sequence experiments using starter code.

Assignment 3

Researching and reporting on bioinformatics research tools (II).

Repository

Objectives

Identify tools for specific research tasks.
Present and explain a tool to peers.

Assignment 4

What Is This Sequence?

Repository

Objectives

Investigate sequence data using online and local analysis tools.
Interpret software output to identify sequence origins.

Assignment 5

Investigating Online Tools.

Repository

Objectives

Read literature and summarize relevant science.
Install and apply software tools to datasets.

Assignment 6

Ethical Topics in Bioinformatics.

Repository

Objectives

Read literature and gather supporting evidence.
Identify ethical dilemmas in bioinformatics research.
Evaluate outcomes in terms of fairness and accountability.

Bioinformatics Resources

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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

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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

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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.

Policy

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Policies

Grading

The grade that a student receives in this class will be based on the following categories. All percentages are approximate and may change during the academic semester.

Category	Percentage	Assessment metric
Class Participation	10%	check mark grade
Exams	20%	letter grade
Lab Assignments	40%	letter grade
Final Project	30%	letter grade
Total	100%

Grading Scale

Letter	Range	Letter	Range	Letter	Range
A	96 - 100	A-	90 - 95.9
B+	87 - 89.9	B	83 - 86.9	B-	80 - 82.9
C+	77 - 79.9	C	73 - 76.9	C-	70 - 72.9
D+	67 - 69.9	D	63 - 66.9	F	59.9 and below

Definitions of Grading Categories

Class Participation: Active participation during class sessions, including questions, exercises, presentations, and leading discussions.
Exams: Cover material in their associated module(s). Dates are announced at least one week in advance.
Laboratory Assignments: Explore concepts, tools, and techniques in bioinformatics. Assignments may be completed in teams and may require presentations or demonstrations.
Final Project: A complete bioinformatics solution to a specific problem. Details are provided approximately two months before finals.

Assignment Submission

We will use GitHub Classroom to collect assignments. It is expected that you can use git to submit your work. Ask peers, Technology Leaders, or the instructor if you need help.

All assignments have a due date and are submitted at the beginning of the lab session. Late submissions are accepted for up to one week with a 15% penalty and must be submitted at the beginning of the session one week after the due date.

Extensions

No assignments are accepted after the late deadline without prior arrangements. If you request an extension, email the instructor before the due date with a valid reason. Extensions are 24 hours and are granted at the instructor’s discretion.

The submission of homework includes the Honor Code pledge. Group assignments require a one-page reflection describing each member’s contribution.

Extenuating Circumstances

If circumstances interfere with your ability to prepare for class or complete assignments, contact the instructor immediately. You may also find these campus resources helpful:

Communication

We will use email, Discord, and the GitHub issue tracker. Install the Discord app to receive timely updates.

The course website stores the syllabus, schedule, and information about the classDocs/ repository. Grades are communicated through a Gradebook GitHub repository.

Bring Your Own Computer

The Department of Computer Science does not provide student machines. Bring a wifi-ready device to class. If the class meets online, make sure your system supports Zoom and other required services.

You will be asked to install software during the semester, including:

Special Needs and Disability Services

The Americans with Disabilities Act (ADA) requires reasonable accommodations. Students who need accommodations are encouraged to contact Disability Services at 332-2898. Disability Services is part of the Learning Commons in Pelletier Library.

Honor Code

The Academic Honor Program applies to all work submitted for academic credit and includes examinations, laboratory assignments, and the final project. Each student acknowledges the Honor Code pledge:

I hereby recognize and pledge to fulfill my responsibilities, as defined in the Honor Code, and to maintain the integrity of both myself and the College community as a whole.

Discussing work with classmates is encouraged, but submitting work identical to others is a violation. More information is available at the Maytum Center for Student Success .

Schedule

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Schedule

Below is a tentative schedule of covered topics and associated activities. The schedule is subject to change with the addition of guest appearances.

Week 1

Lecture: Introduction to the course and careers.
Lab: No lab.
Reading: Read the syllabus.
Learn more: Career Paths

Week 2

Lecture: DNA basics, Central Dogma, replication, Activity 01.
Lab: Software tools.
Reading: Chapter 1.
Learn more: Tools and Environments

Week 3

Lecture: BioPython basics, Activity 02.
Lab: BioPython and silent mutations.
Reading: Chapter 1.
Learn more: Assignments

Week 4

Lecture: Reading frames, mutations, sequence alignment, Activity 03.
Lab: Tool intro and presentation.
Reading: Chapters 2 and 3.
Learn more: Data Sources

Week 5

Lecture: Sequence alignment and Needleman-Wunsch (global alignment).
Lab: No new assignment.
Reading: Chapter 3.
Learn more: Bioinformatics Resources

Week 6

Lecture: Biopython and sequence alignment, Activity 04.
Lab: Sequence analysis.
Reading: Chapters 3 and 4.
Learn more: Assignments

Week 7

Lecture: Fall break, no class.
Lab: No lab.
Activity: Activity 05 BuildADatabase.
Learn more: Assignments

Week 8

Lecture: BLAST and protein BLAST.
Lab: Article and tool.
Reading: Chapters 4 and 5.
Learn more: Bioinformatics Resources

Week 9

Lecture: Substitution matrices (PAM, BLOSUM). Test week: Activity 06 (video).
Lab: See last week’s lab.
Learn more: Assignments

Week 10

Lecture: Gene assembly, Activity 07 .
Lab: Ethics in Bioinformatics Research .
Reading: Chapters 8 and 9.
Learn more: Writing Resources

Week 11

Lecture: Protein research websites, Activity 08.
Lab: Hand out.
Learn more: Data Sources

Week 12

Lecture: Final project assignment.
Lab: Hand out.
Learn more: Assignments

Week 13

Lecture: Thanksgiving.
Learn more: Research Resources

Week 14

Lecture: Activity 09 and Activity 10.
Lab: Hand out.
Learn more: Assignments

Week 15

Lecture: BeagleTM.
Learn more: Bioinformatics Resources

Week 16

Lecture: Course summary.
Presentations.
Learn more: Writing Resources

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Tools and Environments

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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

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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

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