iiinova — where your digital self keeps evolving

Djoser Genomics @djosergenomics.github.io · 07/12/2025

Introducing Project Menhed 🎨 Ancient Egyptian scribes used a palette (Menhed) to mix ink. I'm using Python to mix genes. Phase 1: Design a bacteria that produces a Red Dye with high efficiency & 100% In Silico. I'll share Updates Publicly Read More 👇 djosergenomics.github.io/Introducing-...

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Djoser Genomics @djosergenomics.github.io · 11/11/2025

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)

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Djoser Genomics @djosergenomics.github.io · 11/11/2025

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)

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Djoser Genomics @djosergenomics.github.io · 31/10/2025

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

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Djoser Genomics @djosergenomics.github.io · 15/08/2025

📜 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

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Djoser Genomics @djosergenomics.github.io · 15/08/2025

📜 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

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Djoser Genomics @djosergenomics.github.io · 14/08/2025

📜 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

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Djoser Genomics @djosergenomics.github.io · 12/08/2025

📜 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

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Djoser Genomics @djosergenomics.github.io · 09/08/2025

📜 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

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Djoser Genomics @djosergenomics.github.io · 07/08/2025

🧬 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

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Djoser Genomics @djosergenomics.github.io · 05/08/2025

🔍 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

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Djoser Genomics @djosergenomics.github.io · 05/08/2025

🎊 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

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Djoser Genomics @djosergenomics.github.io · 05/04/2025

🧬 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

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Djoser Genomics @djosergenomics.github.io · 05/04/2025

🧬 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

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Djoser Genomics @djosergenomics.github.io · 05/04/2025

🧬🔬 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)

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Djoser Genomics @djosergenomics.github.io · 01/04/2025

📌 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

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Djoser Genomics @djosergenomics.github.io · 01/04/2025

📊 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.

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Djoser Genomics @djosergenomics.github.io · 01/04/2025

🧬 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

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Djoser Genomics @djosergenomics.github.io · 01/04/2025

🟥🟦 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

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Djoser Genomics @djosergenomics.github.io · 01/04/2025

🔥 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

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Djoser Genomics @djosergenomics.github.io · 01/04/2025

🔹 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

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Djoser Genomics @djosergenomics.github.io · 24/03/2025

📌 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.

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Djoser Genomics @djosergenomics.github.io · 24/03/2025

📊 Expression distributions across samples show stable medians with no major batch effects. 📊 HS02 has a slight outlier. 📊 CD01 follows expected trends. #Bioinformatics #RNAseq

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Djoser Genomics @djosergenomics.github.io · 24/03/2025

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Djoser Genomics @djosergenomics.github.io · 24/03/2025

🟥🟦 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

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Djoser Genomics @djosergenomics.github.io · 24/03/2025

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Djoser Genomics @djosergenomics.github.io · 24/03/2025

🔹 PCA analysis shows clear separation between HS (top) and CD (bottom), except for CD01, which clusters closer to healthy samples. 🔹 PC1 explains 39.9% of variance, PC2 26.4%. #Bioinformatics #RNAseq

PCA plot with sample labels, PC1 vs PC2, PC1 shows 39.9% variance & PC2 shows 26.4% variance

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Djoser Genomics @djosergenomics.github.io · 01/01/2025

🌟 Project Update🧬 The most overrepresented Gene Ontology terms included 🦠 Defense response to virus (GO:0051607) 🤝 Defense response to symbiont (GO:0140546) 🛡️ Immune response (GO:0006955) ⚡ Response to biotic stimulus (GO:0009607) 💡 Innate immune response (GO:0045087) #Bioinformatics #GeneOntology

Go Enrichment Analysis of how melatonin alleviates TNF-α-induced damage in human coronary artery endothelial cells (HCAECs) using RNA-seq

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Djoser Genomics @djosergenomics.github.io · 25/12/2024

5) P-Value Histogram I got the first image when using the raw counts from feature counts & got this U-shaped histogram, I was lucky enough to check out @tommytang.bsky.social thread post about P-values & his explanation about the red flag U-shape Removing the low count genes from fixed! 🎊👏

Old DESeq2 P-value Histogram showing U shapped appearance

New DESeq2 P-value Histogram showing a normal histogram with a spike at P-value = 0

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Djoser Genomics @djosergenomics.github.io · 25/12/2024

3) Dispersion Estimates plot showing a smooth fitted line implying a good model fit showing existing variability for gene expression counts across the samples. 4) MA Plot having a funnel shaped appearance showing differentially expressed genes with significant fold changes #DESeq2 #Plots

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Djoser Genomics @djosergenomics.github.io · 25/12/2024

Here are some Plots from DESeq2: 1) PCA Plot showing a distinction between the Melatonin & Control samples revealing significant expression changes 2) A Sample-to-Sample Distance Heatmap showing treatment groups tight clustering & distinct differences between Melatonin & Control samples #DESeq2

PCA Plot showing PC1: 96% variance and PC2: 1% variance

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Djoser Genomics @djosergenomics.github.io · 14/12/2024

GO Analysis 🧬🖥️ Genes associated with ribosomal structural functions, biological pathway of translation,the molecular function of translation initiation, & its biological pathway are significantly enriched among differentially expressed genes (DEGs) under HOCl stress in Streptococcus pneumoniae D39.

Top 10 Over-represented Categories in Wallenius Method - Goseq Plots

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Djoser Genomics @djosergenomics.github.io · 05/12/2024

Here's also some other plots that were generated by DESeq2! A MA plot showing differentially expressed genes with significant fold changes A Sample-to-Sample Distance Heatmap showing treatment groups tight clustering & distinct differences between HOCl-treated & control samples #Bioinformatics

DESeq2 Sample-to-Sample Distance Heatmap

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Djoser Genomics @djosergenomics.github.io · 05/12/2024

Dispersion model fit successfully! 🎯 This plot illustrates how gene-wise dispersion estimates are modeled across mean expression levels, ensuring reliable statistical inference during differential expression analysis. #Bioinformatics #DESeq2 #RNAseq #Genomics

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Djoser Genomics @djosergenomics.github.io · 05/12/2024

The p-value histogram shows a strong peak near zero, confirming that many genes are significantly differentially expressed in response to HOCl treatment. #Bioinformatics #RNAseq #PValues

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Djoser Genomics @djosergenomics.github.io · 05/12/2024

Check out the PCA plot from my RNA-seq analysis! 🧬 🖥️ Streptococcus pneumoniae D39 HOCl-treated samples cluster distinctly from controls, revealing significant expression changes due to oxidative stress. #RNAseq #Bioinformatics #PCA #DESeq2 #Bacteria

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