A comprehensive 12-month journey from biomedical science to computational biology mastery
Transitioning from a Master's in Biomedical Science to computational biology expertise through projects, real-world datasets, and bioinformatics techniques. This repository documents my 12-month intensive learning pathway toward PhD applications in computational biology.
Background: Master Biology and Health, Morocco | US State Department Student Leaders Program Alumni
- Month 1: Linux & Command-Line Mastery
- Month 2: Python Programming for Bioinformatics
- Month 3: Data Science with Python & R
- Month 4: Genomics & Sequence Analysis
- Month 5: Next-Generation Sequencing Analysis
- Month 6: Advanced Genomics & Functional Analysis
- Month 7: Machine Learning in Bioinformatics
- Month 8: Multi-Omics & Systems Biology
- Month 9: Cutting-Edge Technologies
- Month 10: Advanced Statistical Methods
- Month 11: Reproducible Research & Best Practices
- Month 12: Independent Research Project
Objectives:
- Master filesystem navigation
- Set up organized bioinformatics workspace
- Create project directory structure
Daily Progress:
- Day 1: Linux installation and basic commands
- Day 2: File system exploration and navigation
- Day 3: Directory management and organization
- Day 4: Practice with real genomic file structures
- Day 5: Create automated setup scripts
- Day 6-7: Review and mini-project
Resources Used:
Programming Languages:
- Python (Biopython, pandas, NumPy, scikit-learn)
- R (Bioconductor, tidyverse, ggplot2)
- Bash scripting
- SQL
Bioinformatics Tools:
- BLAST, samtools, bedtools
- GATK, BWA, bowtie2
- Nextflow/Snakemake
Machine Learning:
- TensorFlow/Keras
- scikit-learn
- Deep learning for genomics
Development Tools:
- Git/GitHub
- Docker
- Jupyter notebooks
- VS Code
Projects will be added as I progress through the roadmap
- Week 1: Genomic file organization and automation scripts
- Month 1: Automated bioinformatics workspace setup
- Month 2: Multi-sequence analysis toolkit
- Month 3: RNA-seq differential expression pipeline
- Month 6: Machine learning for genomic variant classification
- Month 12: Independent research project
- Linux/Unix: ⭐☆☆☆☆☆(Newbie)
- Python: ⭐⭐☆☆☆ (Learning)
- R: ⭐⭐⭐☆☆ (Intermediate)
- Bioinformatics Tools: ⭐⭐⭐☆☆ (Learning)
- Machine Learning: ⭐☆☆☆☆ (Beginner)
- Statistical Analysis: ⭐⭐⭐☆☆(Advanced)
- Molecular Biology: ⭐⭐⭐⭐⭐ (Expert - Master's degree)
- Genomics: ⭐⭐⭐⭐☆ (Advanced)
- Proteomics: ⭐⭐⭐☆☆ (Intermediate)
- Systems Biology: ⭐⭐☆☆☆ (Learning)
Immediate (6 months):
- Complete core bioinformatics skill development
- Build portfolio of 5+ substantial projects
- Begin PhD application preparation
Medium-term (12 months):
- Submit PhD applications to top programs in US and Europe
- Complete independent research project
- Present work at conferences/workshops
Long-term (3-5 years):
- PhD in Computational Biology/Bioinformatics
- Research focus on [Your specific interest - e.g., cancer genomics, personalized medicine, etc.]
- Contribute to open-source bioinformatics community
Focus: Linux Fundamentals and Workspace Setup
What I Learned:
- [To be updated daily]
Challenges:
- [To be updated]
Next Week Goals:
- [To be planned]
I'm always interested in connecting with fellow researchers, PhD students, and bioinformatics professionals. Feel free to:
- Discuss projects: Open to collaborations and feedback
- Share resources: Happy to share datasets and learning materials
- Network: Connecting with the global bioinformatics community
Contact:
- 📧 Email: abdellah.chaaibi.edu@gmail.com
- 💼 LinkedIn: I'm on LinkedIn
- 🐧 X: @flagflieger
- Inspiration from the global bioinformatics community
- Resources from NCBI, EBI, and other public databases
- Open-source tools and libraries that make this journey possible
Last Updated: September 2, 2025 Current Status: Week 1, Day 1 - Beginning the journey!