Genetics, Genomics & Bioinformatics Research Updates

Understanding Manhattan Plots and QQ Plots in GWAS😍
One of the most common mistakes in GWAS is generating Manhattan and QQ plots without fully understanding what they mean.

📈 Manhattan Plot
A Manhattan plot displays the association between SNPs and a trait across the genome.

Key things to look for:

✅ Peaks above the significance threshold

✅ Chromosomal regions with strong associations

✅ Potential candidate loci for further investigation

Remember: A tall peak does not automatically mean you have found the causal gene. Further validation is always required.

📊 QQ Plot
A QQ plot compares observed p-values with expected p-values under the null hypothesis.

Key things to look for:

✅ Most points should follow the diagonal line

✅ Deviation at the tail may indicate true associations

⚠️ Strong deviation across the entire plot may suggest population structure, cryptic relatedness, or model misspecification

A good GWAS study should have both a meaningful Manhattan plot and a well-behaved QQ plot.

When reviewing GWAS results, most researchers often look at the QQ plot before interpreting significant SNPs because it provides insight into the reliability of the analysis.

What is the first thing you check when interpreting GWAS results?

Image credit: Dr. Mvuyeni Nyasulu

🤭😂

Happy father's day 🧬
>Did you know that Mitochondrial DNA can also come from Dad.
> A child gets 50% of their DNA from each parent
> The traits of a child are determined not only by what their parents look like, but all the genomes from direct ancestors who came before, on both sides of the family
> This genetic soup either can create a child that looks exactly like one or more parents, or a child that looks like neither, or something in between

Read more: Biparental Inheritance of Mitochondrial DNA in Humans https://www.pnas.org/content/115/51/13039

Sci-hub: the saviour😁
If the usual Sci-Hub links don’t work, you can try these new Sci-Hub links. They are continually updated as new links are released.

https://sci-hub.cc
https://sci-hub.se
https://sci-hub.st
https://sci-hub.se
https://sci-hub.do
https://sci-hub.it.nf
https://sci-hub.es.ht
https://sci-hub.im
https://sci-hub.se
http://sci-hub.tw
http://sci-hub.si
http://sci-hub.vk

Clarivate Releases Journal Citation Reports 2026

Comment "JCR2026" to recieve the complete list

🚀 China Dominates Global Research Rankings: 9 of the World's Top 10 Institutions Are Chinese!🇨🇳
A major shift in global science is underway. The latest Nature Index 2026 Research Leaders rankings reveal an unprecedented rise of Chinese research institutions, highlighting China's growing influence in cutting-edge scientific discovery and innovation.

🔬 Key Highlights:

• Chinese institutions occupy 9 of the world's top 10 research positions.
• For the first time in a decade, Zhejiang University surpassed Harvard University to become the world's leading academic institution in the Nature Index.
• The Chinese Academy of Sciences remains the world's top overall research organization.
• All Chinese institutions in the global top 10 recorded double-digit growth in research output.
• The rankings are based on contributions to high-quality research published in leading international journals during 2025.
• China's sustained investment in science, technology, and innovation continues to reshape the global research landscape.

📖 Read more:
link in the comment

Best Free Tools Used in Bioinformatics 😍👌
Whether you're a student, researcher, or beginner in bioinformatics, these free tools can significantly improve your workflow:
🔹 NCBI – Access to genomic, protein, and literature databases.
🔹 BLAST – Compare DNA, RNA, or protein sequences against global databases.
🔹 Clustal Omega – Multiple sequence alignment for evolutionary and functional analysis.
🔹 MEGA – Sequence alignment, phylogenetic tree construction, and evolutionary studies.
🔹 Swiss-Model – Automated protein structure prediction through homology modeling.
🔹 ExPASy – Comprehensive proteomics and protein analysis resources.
🔹 NEBcutter – Restriction enzyme analysis and DNA sequence digestion mapping.
🔹 PyMOL (Educational/Open Source Versions) – 3D visualization of proteins and molecular structures.
🔹 STRING – Protein–protein interaction network analysis.
🔹 Galaxy – Web-based platform for bioinformatics data analysis without coding.
🔹 IEDB Tools – Epitope prediction for vaccine and immunoinformatics studies.
🔹 AlphaFold Database – Predicted protein structures for millions of proteins.
Bioinformatics is becoming an essential skill in modern life sciences. Learning these tools can open doors to genomics, proteomics, drug discovery, molecular docking, and vaccine design.

Which bioinformatics tool do you use most often, and which one would you recommend to beginners?

Image credit: Tayyab

🔬 BREAKING: CAR-T Therapy CURES Patient with 3 Deadly Autoimmune Diseases After 10 Years of Failed Treatments!
For the first time ever, scientists used CAR-T cell therapy to completely reverse three life-threatening autoimmune diseases in a single patient who had suffered for over a decade. The patient is now in treatment-free remission after just one year!

✨ Key Findings:
Triple disease remission: Autoimmune hemolytic anemia (AIHA), immune thrombocytopenia (ITP), and antiphospholipid antibody syndrome all eliminated simultaneously

Treatment-free after 1 year: Patient needs no transfusions, steroids, or immune-suppressing medications whatsoever

Rapid recovery: Blood counts normalized within just 3 weeks—hemoglobin doubled, platelets stabilized, antibodies turned negative

Immune system reset: CAR-T cells eliminated all dysregulated B cells throughout the body, resetting the immune system to naive state

Quality of life restored: After 10+ years of daily blood transfusions, patient returned to almost normal life

This groundbreaking case study published in Med (Cell Press) suggests CAR-T therapy could transform treatment for severe autoimmune diseases that resist standard therapies

-T

Which row are you sitting in? 👀
Drop your answer in the comments and see if you and your friends are on the same row.

🤖 Can Turnitin Actually Catch AI-Generated Writing? The Truth Might Surprise You! 🔍📄
As generative AI tools like ChatGPT, Copilot, Gemini, and Grammarly become part of everyday writing, a massive question hangs over academia and professional publishing: Can detection tools accurately tell human and machine apart? A groundbreaking new study published in Education and Information Technologies put Turnitin’s AI detector to the ultimate test using dozens of hybrid scripts ranging from 100% human-written to 100% AI-generated.

Here are the eye-opening key findings you need to know:

Zero False Positives for Purely Human Text: Good news for original writers—Turnitin did not flag AI-generated scores for scripts that were completely written by humans.

The "Proportionality" Reality: In general, an increase in actual LLM-generated words in a script directly correlates with an increase in the AI-generated score detected.

The Inaccuracy Paradox: * Low AI content = Over-flagged: The lower the actual percentage of AI text in a document, the more inaccurately higher Turnitin’s AI score tends to be.

High AI content = Under-flagged: Conversely, the higher the actual percentage of AI text used, the more inaccurately lower the final detection score becomes.

A Call for New Standards: Because detection scores can be highly unstable on mixed/hybrid texts, the study suggests relying on computer-based lockdown browser assessments and deeper institutional cooperation rather than just relying on automated detectors.

Read the full study here: https://link.springer.com/article/10.1007/s10639-026-14049-2