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djosergenomics.github.io
Djoser Genomics
@djosergenomics.github.io
1.2K followers 8.1K following 120 posts
Exploring #SyntheticBiology & #Bioinformatics + Documenting my learning journey | Built by @noureldenrihan.bsky.social | 🌐 djosergenomics.github.io
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djosergenomics.github.io
Part of HTGAA Week 4 is to write a proposal on an In-silico Bacteriophage Engineering.

My proposal: 'ArmoredPhage' 🛡️

My goal: Engineer a Thermostable MS2 Bacteriophage Using Protein Design Techniques.

Check it out:
djosergenomics.github.io/Armored-Phag...

#HTGAA #synbio #AlphaFold #proteindesign
A Schematic Showing a Brief Overview of the Proposed Pipeline PyMOL Visualization showing the full icosahedral 'shell' of the MS2 bacteriophage (PDB: 2MS2). PyMOL Visualization showing a single 'monomer' or 'building block' of the MS2 capsid protein. (Yellow is Beta Sheets, Red is Alpha helices, Green is in between loops)
November 11, 2025 at 5:21 PM
djosergenomics.github.io
Week 4 of HTGAA (Protein Design) is done & I'm amazed!

I was always intimidated by 3D protein models in papers & never thought I'd understand them one day!

Today, I loaded a PDB file into PyMOL, saw the alpha helices & beta sheets of my Tyrosinase enzyme.

This is so cool. 🚀
#HTGAA #proteindesign
PyMOL Visualization of Structures of Tyrosinase Enzyme (Red Alpha Helices, Yellow Beta Sheets, Green In between Loops) (PDB ID: 3NM8) PyMOL Visualization of Active Sites of Tyrosinase (Copper (II) ions in Orange) - Transparency = 0.35 (PDB ID: 3NM8)
November 11, 2025 at 2:27 AM
djosergenomics.github.io
Week 3 of HTGAA is all about lab automation! 🤖

I wrote a script to program a virtual Opentrons robot to draw my Djoser Genomics pyramid. It simulates robots pipetting 100+ dots of fluorescent E.coli because manually it is slow & error-prone.

#HTGAA #synbio #Opentrons #automation #bioinformatics
Djoser Genomics Step Pyramid Drawn by Opentrons virtual robot
October 31, 2025 at 12:34 AM
djosergenomics.github.io
📜 Scroll VIII: Djoser's Discoveries!

The Grand Finale, we dive into Gene set Enrichment Analysis (GSEA), why we do it, how we do it and how to visualize and interpret it.

Check it out: djosergenomics.github.io/Scroll-8-Djo...

#bioinformatics #RNAseq #scicomm
Scroll 8 of Bulk RNAseq Tutorial Codex: Djoser's Discoveries
August 15, 2025 at 10:55 PM
djosergenomics.github.io
📜 Scroll VII: Thutmose's Trends!

Here, We identify Trends among our DEGs using Functional (GO) and Pathway (KEGG) Enrichment Analysis!

Check it out: djosergenomics.github.io/Scroll-7-Thu...

#bioinformatics #RNAseq #scicomm
Scroll 7 of Bulk RNAseq Tutorial Codex: Thutmose's Trends
August 15, 2025 at 12:58 AM
djosergenomics.github.io
📜 Scroll VI: Ramses' Plots!

We dive into Visualizations!

MA, PCA, Volcano Plots and Heatmaps!

Check it out: djosergenomics.github.io/Scroll-6-Ram...

#bioinformatics #RNAseq #scicomm
Scroll 6 of Bulk RNAseq Tutorial Codex: Ramses' Plots
August 14, 2025 at 12:37 AM
djosergenomics.github.io
📜 Scroll V: Menkaure's Measures!

We dive into Differential expression analysis (DEA), using DESeq2, and how to interpret the findings

Check it out: djosergenomics.github.io/Scroll-5-Men...

#bioinformatics #RNAseq #scicomm
Scroll 5 of Bulk RNAseq Tutorial Codex: Menkaure's Measures
August 12, 2025 at 1:56 AM
djosergenomics.github.io
📜 Scroll IV: Khafre’s Connections is here!

We step into the next stage of our RNAseq journey!
Importing Kallisto results into R, linking them to gene annotations, & building a strong foundation for downstream analysis

Here: djosergenomics.github.io/Scroll-4-Kha...

#bioinformatics #RNAseq #scicomm
Scroll 4 of Bulk RNAseq Tutorial Codex: Khafre’s Connections
August 9, 2025 at 9:09 PM
djosergenomics.github.io
🧬 Scroll 3 of the Bulk RNA-seq Tutorial Codex is here!

⚒️ Guided by King Khufu, we pseudoalign our reads using Kallisto — fast, efficient, and accurate.

🌐 Full tutorial + cultural spotlight:

📜 djosergenomics.github.io/Scroll-3-Khu...

#RNAseq #bioinformatics #scicomm
Scroll 3 of the Bulk RNA-seq Tutorial Codex: Khufu's Calculations
August 7, 2025 at 10:38 PM
djosergenomics.github.io
🔍 Scroll 2 of Djoser’s Bulk RNAseq Tutorial Codex is here!

We dive into RNA-seq Quality Control with FASTQC — guided by Hesy-Ra, the first known dentist & physician in history.

🧪 Read here: djosergenomics.github.io/Scroll-2-Hes...

#bioinformatics #RNAseq #scicomm
Scroll 2 of Djoser’s Bulk RNAseq Tutorial Codex: Hesy-Ra's Diagnostics
August 5, 2025 at 10:57 PM
djosergenomics.github.io
🎊 Scroll 1 of Djoser’s Bulk RNAseq Tutorial Codex: Imhotep's Insights 🎊

We start by collecting real RNA-seq data from ENA using Google Colab.

Inspired by Imhotep - ancient Egypt’s architect of the Pyramid of Djoser.

📜 Check it out: djosergenomics.github.io/Scroll-1-Imh...

#bioinformatics #RNAseq
Scroll 1 of Djoser’s Bulk RNAseq Tutorial Codex: Imhotep's Insights
August 5, 2025 at 2:00 AM
djosergenomics.github.io
🧬 GSEA Enrichment Results!

📊 40 significant pathways (padj < 0.05, |NES| > 1) highlighting strong immune and inflammatory signals.

🏆 Top enriched pathway: KEGG Allograft Rejection
(NES = 2.44, p.adj = 1.87e-3)

Suggests a major immune activation signature 🚨

#Bioinformatics #RNAseq #GSEA
Enrichment Plot showing upregulation of the most significant GSEA pathway which is KEGG Allograft Rejection
April 5, 2025 at 12:35 AM
djosergenomics.github.io
🧬 KEGG Pathways Enrichment Results!

🧪 Top 10 significant enriched pathways include:

🔬 Infectious disease pathways (COVID-19, Tuberculosis, Staphylococcus aureus)

🔬 Ribosomal activity & phagosome function

🔬 Immune-driven diseases (Asthma, Alcoholic liver disease)

#Bioinformatics #RNAseq #KEGG
Dotplot showing Top 10 KEGG Pathways
April 5, 2025 at 12:35 AM
djosergenomics.github.io
🧬🔬 GO Enrichment Results!

Functional enrichment analysis reveals 148 significant GO terms (padj < 0.05), clustering into key biological processes:

💡 BP: Immune response, Cell Adhesion & Complement Activation
💡 CC & MF: Ribosomes & MHC class I & II Protein Complexes

#Bioinformatics #RNAseq #GO
Dotplot showing Top GO Enrichment terms (BP,MF,CC)
April 5, 2025 at 12:35 AM
djosergenomics.github.io
📌 Sample to Sample distance heatmap

📌 HS02 & CD01 are the most different.

📌 HS02 & HS03 cluster together, suggesting shared expression.

📌 CD03 appears distinct from other CD samples, possibly due to biological or technical factors.

#RNAseq #Bioinformatics
Sample to Sample distance heatmap, HS02 & CD01 are the most different, HS02 & HS03 cluster together, suggesting shared expression, CD03 appears distinct from other CD samples, possibly due to biological or technical factors.
April 1, 2025 at 10:20 PM
djosergenomics.github.io
📊 Expression distributions across samples show stable medians with no major batch effects.

📊 HS02 & HS03 have a slight outlier.

📊 CD03 follows expected trends.

#Bioinformatics #RNAseq
Expression distributions across samples show stable medians with no major batch effects, HS02 & HS03 has a slight outlier, CD03 follows expected trends.
April 1, 2025 at 10:20 PM
djosergenomics.github.io
🧬 The MA plot highlights significantly differentially expressed genes (p < 0.05) between disease & healthy samples.

🧬 Most genes stay around log2FC = 0, while some show strong up/downregulation.

🧬 These genes are potential candidates for further functional analysis.

#Bioinformatics #RNAseq
The MA plot highlights significantly differentially expressed genes (p < 0.05) between disease & healthy samples, Most genes stay around log2FC = 0, while some show strong up/downregulation.
April 1, 2025 at 10:20 PM
djosergenomics.github.io
🟥🟦 Heatmap of all 771 DEGs shows HS & CD samples clustering separately, revealing distinct expression patterns.

🟥🟦 The grouping indicates consistent transcriptomic differences between healthy and diseased conditions.

#Bioinformatics #RNAseq #Transcriptomics
Heatmap of all 771 DEGs shows HS & CD samples clustering separately, revealing distinct expression patterns.
April 1, 2025 at 10:20 PM
djosergenomics.github.io
🔥 Volcano plot of disease vs. healthy!

🔥 It shows differentially expressed genes with log2FC > 1 or < -1 & padj < 0.05 are in red.

🔥 These genes are the most significantly dysregulated and could be important in disease mechanisms.

#Bioinformatics #RNAseq
Volcano plot of disease vs. healthy, It shows differentially expressed genes with log2FC > 1 or < -1 & padj < 0.05 are in red.
April 1, 2025 at 10:20 PM
djosergenomics.github.io
🔹 PCA analysis shows clear separation between CD (top) and HS (bottom), except for CD03, which clusters closer to healthy samples.

🔹 PC1 explains 39.2% of variance, PC2 26.7%.

I realized I mistakenly swapped the Disease and Healthy samples in my old PCA plot. 😅🙈

#Bioinformatics #RNAseq
PCA plot with sample labels, PC1 vs PC2, PC1 shows 39.2% variance & PC2 shows 26.7% variance
April 1, 2025 at 10:20 PM
djosergenomics.github.io
📌 Sample to Sample distance heatmap

📌 CD01 & CD02 are highly similar.

📌 HS02 & CD03 are the most different.

📌 HS01 & HS02 cluster together, suggesting shared expression.

📌 CD03 appears distinct from other CD samples, possibly due to biological or technical factors.

#RNAseq #Bioinformatics
Sample to Sample distance heatmap, CD01 and CD02 are highly similar, while HS02 and CD03 are the most different. HS01 and HS02 cluster together, suggesting shared expression. CD03 appears distinct from other CD samples, possibly due to biological or technical factors.
March 24, 2025 at 5:54 PM
djosergenomics.github.io
📊 Expression distributions across samples show stable medians with no major batch effects.

📊 HS02 has a slight outlier.

📊 CD01 follows expected trends.

#Bioinformatics #RNAseq
Expression distributions across samples show stable medians with no major batch effects, HS02 has a slight outlier, CD01 follows expected trends.
March 24, 2025 at 5:54 PM
djosergenomics.github.io
🧬 The MA plot highlights significantly differentially expressed genes (p < 0.05) between disease & healthy samples.

🧬 Most genes stay around log2FC = 0, while some show strong up/downregulation.

🧬 These genes are potential candidates for further functional analysis.

#Bioinformatics #RNAseq
The MA plot highlights significantly differentially expressed genes (p < 0.05) between disease & healthy samples, Most genes stay around log2FC = 0, while some show strong up/downregulation.
March 24, 2025 at 5:54 PM
djosergenomics.github.io
🟥🟦 Heatmap of my 645 DEGs shows HS & CD samples clustering separately, revealing distinct expression patterns.

🟥🟦 The grouping indicates consistent transcriptomic differences between healthy and diseased conditions.

#Bioinformatics #RNAseq #Transcriptomics
Heatmap of my 645 DEGs shows HS & CD samples clustering separately, revealing distinct expression patterns.
March 24, 2025 at 5:54 PM
djosergenomics.github.io
🔥 Volcano plot of disease vs. healthy!

🔥 It shows differentially expressed genes with log2FC > 1 or < -1 & padj < 0.05 are in red.

🔥 These genes are the most significantly dysregulated and could be important in disease mechanisms.

#Bioinformatics #RNAseq
Volcano plot of disease vs. healthy, It shows differentially expressed genes with log2FC > 1 or < -1 & padj < 0.05 are in red.
March 24, 2025 at 5:54 PM