Molecular Biology & Genomic
A living intersection of molecular biology, bioinformatics, and genomic data science—bridging wet-lab fundamentals with computational breeding, predictive genomics, and biostatistical modelling in poultry and beyond.
Self-Reflection
Can I propose that DNA isn’t just code—it’s the original blockchain? Think - its immutable, distributed and now can be analysed at scale? …Or am I mad for thinking that?
From Aircraft to Poultry? Let me explain.
After obtaining my aircraft engineering degree, I; as well as many, found it very difficult and demoralising due to the issue of “Experience and Degree” job requirement- How can one gain both at the same time?
So - I worked at a specialised glass manufacturing plant as the CAD/CAM specialist and Maintenance Engineer. Collecting all customer orders to maximise the output of the CNC Machine as well as following a routine maintenance or emergency repairs on the machine.
Simply put, working for slightly above minimum wage when I was earning close to double in hospitality with my ability to sell; I had to find another job.
This is where I started as a Lifetime Feed Consumption Ratio (LFCR) Technician then getting promoted to a Team leader; now supporting the running of the farm as well as all data collection.
Working in poultry genetics—where every bird is a data point in a long breeding program. Feed efficiency. Disease resistance. Growth rate. Egg quality. everything and anything is collected to calculate the efficiency of the farm as well as the efficiency of the production.
Due to my none-disclosures, I hope this provides enough information with how my curiosity sparked the potential possible results of data analytics
After completing the advanced data analytics, I asked myself as well as other teams “How does the company predict the perfect broiler?” with the generic answer “You need to ask a Geneticist”
And this is where I now have entered Genomic Data Science. Simply because: Nobody knows but a handful of people
Genomic Data Science Specialization
Now merging biology + code + stats:
- Category: Bioinformatics – Aligning billions of reads with BWA/GATK
- Category: Python/R – Pandas for VCFs, Bioconductor for differential expression
- Category: Unix/Linux –
awk,grep,bashpipelines on HPC clusters - Category: Biostatistics – FDR correction, logistic regression on GWAS hits
- Category: Data Management – From raw FASTQ to clean, annotated, reproducible datasets
Real-world application? Selective breeding via genomic selection (GS)— using DNA markers to predict phenotype before birth which makes me feel somewhat uncertain due to my moral implications
Can we Engineer Life with Data?
We’re not just raising chickens. We’re computationally evolving them. Typing this runs shivers down the back of my neck for some reason?
Lets break it down:
| Trait | Traditional Method | Genomic Method |
|---|---|---|
| Selection Accuracy | ~40% (pedigree) | ~85% (DNA) |
| Generation Interval | 2–3 years | <1 year |
| Genetic Gain | +1% per year | +3–5% per year |
This is AI + DNA = Agricultural Revolution.
The Road Ahead
This portfolio is alive and sequencing.
Next milestones:
- GWAS in Production Populations
- CRISPR-Cas9 Off-Target Prediction
- Metagenomics for Gut Health
- Single-Cell RNA Sequencing
- AlphaFold for Poultry Protein Design
- Digital Twin Breeder Models
I propose a question to you: Is the future of food grown or is it already designed?
We aren’t just in the coop. We are in the code.