Double-stranded

Mike Kaz
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Exploring the fascinating world of genetics and its impact on human health! We break down complex science topics like DNA, genome wide association studies, CRISPR gene editing, and epigenetics into accessible conversations. Join us for weekly episodes covering cutting-edge biology research and how your genome shapes your health. Subscribe for the latest genetic discoveries explained simply!

  1. 2025. 09. 29.

    The Killer Gene That FOUGHT BACK

    In this deep dive, we break down the journey of the PIM Kinase target. We explore the complex PIM1 molecular mechanism, examine why first-generation pan-PIM inhibitors like AZD-1208 faced safety issues and a lack of monotherapy efficacy, and reveal the final clinical verdict of the Terminated and Completed Phase 1 studies. 📖 Video Chapters: 0:00 - Intro 1:05 - Biobanks Associations for PIM1 22:18 - PIM1 Gene 39:00 - PIM1 Protein 52:52 - PIM1 Known Drugs and Clinical Trials

    1시간 3분

  2. 2025. 09. 24.

    The Mysterious Gene Behind Blood Clots

    Disclaimer: This video is for educational purposes only and should not be used for medical diagnosis or treatment decisions. Always consult with healthcare professionals for medical advice. In this episode, we explore the association between the PEAR1 gene and various platelet disorders. Using data and research, we aim to explain the significance of platelet count and the effects of thrombocytopenia and low platelets in these conditions. This includes genetic analysis of the genome. #genetics #blood #platelets

    1시간 7분

  3. 2025. 09. 22.

    KGWAS: novel genetics discovery enabled by massive functional genomics knowledge graph

    KGWAS is a novel geometric deep learning method that leverages a massive functional knowledge graph across variants and genes to significantly improve detection power in small-cohort GWASs. Thank you, Kexin Huang and Martin Jinye Zhang, for joining me on this one.Preprint:https://www.medrxiv.org/content/10.11...GitHub Page:https://github.com/snap-stanford/KGWASOfficial Website:https://kgwas.stanford.edu/ 📖 Video Chapters:0:00 - Introduction to Rare Disease Genetics Problem0:54 - Welcome & Paper Overview1:22 - What is KGWAS? The Elevator Pitch2:41 - Why Finding More GWAS Hits Matters4:46 - Origin Story: Collaboration with GSK6:09 - Knowledge Graph Architecture Overview7:36 - Building the Variant-Gene-Pathway Network9:55 - Handling Linkage Disequilibrium (LD) Challenges13:55 - Training the Large-Scale Graph Neural Network14:16 - Model Training Requirements and Scalability15:22 - Open Source Availability and Accessibility16:05 - Scaling to Whole Genome Analysis17:12 - Figure 2: Validation Through Simulations18:24 - UK Biobank Downsampling Experiments20:04 - Precision-Recall Performance Results21:00 - Figure 3: Real-World Disease Applications21:38 - Ulcerative Colitis Case Study Example22:48 - Experimental Validation of Ulcerative Colitis Discovery24:03 - Myasthenia Gravis Case Study Analysis26:10 - Knowledge Graph Component Analysis and Ablation Studies27:54 - Tissue-Specific vs Context-Agnostic Approaches32:03 - Figure 4: Network Interpretation and Attention Mechanisms36:30 - Alzheimer's Disease Network Visualization40:01 - Drug Development Applications and Implications41:15 - Post-GWAS Era Applications and End-to-End Solutions43:01 - Figure 5: Compatibility with Existing GWAS Tools48:16 - Population Diversity and Cross-Ancestry Applications49:41 - Future Directions and Technical Improvements52:16 - Input/Output Requirements and Computational Resources54:28 - Website Demo and Interactive Features56:59 - PhD Student Advice and Research Philosophy59:36 - Closing Remarks

    1시간

  4. 2025. 08. 18.

    Low overlap of transcription factor DNA binding and regulatory targets

    This podcast was made possible by CIUT-FM 89.5, University of Toronto Community Radio. For more content visit https://ciut.fm/ Link to the paper: https://www.nature.com/articles/s41586-025-08916-0 Join me in a conversation with two of the authors, Lakshmi Mahendrawada and Steven Hahn.

    1시간 9분

  5. 2025. 07. 17.

    K562 cell Masterclass | Dr. Tom Karagiannis

    This podcast was made possible by CIUT-FM 89.5, University of Toronto Community Radio. For more content, visit https://ciut.fm/ Join us for this comprehensive masterclass episode as we explore the fascinating world of K562 cells with Dr. Tom Karagiannis, who has worked with these remarkable cells for almost three decades. From his first encounter with them as a young researcher in 1996 during the exciting era of targeted therapy development, Dr. Karagiannis shares both the scientific breakthroughs and practical realities of working with these "weeds" of the lab. 📖 Video Chapters: 0:00 - The 1917 Origin Story: A Woman's Legacy 2:40 - Interview Begins: Dr. Tom Karagiannis on K562 Basics 5:42 - The 1990s Gleevec Revolution: Why K562 Cells Became Essential 7:10 - The Lozio Discovery: From Patient to Immortal Cell Line 8:48 - What Makes K562 Cells Special: Continuous Growth vs Normal Cells 10:36 - Cell Culture Evolution: From Glass Pipettes to Modern Labs 12:18 - Historical Context: HeLa Cells to Cancer Virus Research 14:26 - The Philadelphia Chromosome: Understanding the Genetic Driver 17:06 - Gleevec Revolution: The First Targeted Cancer Therapy 19:48 - K562 Cell Genetics: The Near-Triploid Puzzle 21:00 - Cell Differentiation: Research Applications and Advantages 24:08 - Transferrin Receptor Research: Dr. Tom's Early Work 28:37 - K562 Cells as "Weeds": Why They're So Easy to Grow 33:08 - Evolution of Cancer Research: From Targeted Therapy to CAR-T 35:42 - Personal Research Stories: Genetic Discoveries and Insights 36:03 - Genetic Discoveries: Novel Chromosomes and Cellular Evolution 42:27 - Drug Resistance Studies: Creating Doxorubicin-Resistant Cell Lines 44:12 - Epigenetic Research: HDAC Inhibitors and Combination Therapies 49:16 - Combination Therapy Mechanisms: How HDAC Inhibitors Enhance Chemotherapy 50:40 - Drug Resistance Reality: Gleevec Limitations and Cancer Stem Cells 53:54 - K562's Continued Value: Why Genetic Drift Doesn't Diminish Research Utility 57:27 - PhD Advice

    1시간

  6. 2025. 06. 28.

    Diabetes, GWAS, Biobanks | Dr. Andrew Paterson

    Dr. Andrew Paterson is a Senior Scientist in Genetics & Genome Biology at SickKids and a Professor at the Dalla Lana School of Public Health, University of Toronto. His work focuses on uncovering the genetic basis of diabetes and its complications. In this episode, we discuss the real promise—and current limits—of GWAS in diabetes research, why the X chromosome is still often ignored, how datasets like GTEx and UK Biobank are shaping the future of precision medicine, and what functional follow-up really looks like. If you're curious about the path from variant to function to treatment, this one’s for you. 📖 Video Chapters: 0:00 - Highlights of the episode 2:07 - Welcome to Dr. Paterson & What Diabetes Really Is 3:11 - Monogenic, Type 1 & Type 2 — Mapping the Diabetes Family 5:11 - Life Before Insulin: Why Early Type 1 Was Fatal 6:46 - Where Each Diabetes Type Sits on the Genetic Spectrum 8:02 - HLA Breakthroughs: Early Genetic Clues to Type 1 8:45 - Childhood & Ancestry Risk: Who Gets Diabetes? 9:40 - Industrialization and the Type 2 Surge 10:55 - Do the Stats Match Patients in Clinic? 11:40 - Why Dr. Paterson Chose Diabetes & Genetics 14:02 - Genetics vs Confounding: Tracing Causal Pathways 16:17 - How Geneticists Link Variants to Disease 17:57 - 1 Billion SNPs & Linkage Disequilibrium 101 18:45 - SNP Arrays: From $1,000 to $20 Per Genome 20:07 - Whole-Genome Sequencing vs Genotyping Chips 20:58 - Cleaning Genotype Data: QC Essentials 21:49 - Regression Basics: Testing SNP–Trait Links 22:57 - Blood or Saliva? Where GWAS DNA Comes From 25:00 - The DCCT Trial & Its Landmark Design 26:44 - Conventional vs Intensive Diabetes Therapy 28:00 - C-Peptide: Residual Beta-Cell Function Insights 30:28 - Genetics, Environment & the Polygenic Reality 32:07 - Biobanks Power the Modern GWAS Boom 33:43 - Mega-Datasets: UK Biobank & Million Veteran Program 34:59 - Landmark 2010 Discovery: Gene for Glycemic Control 36:40 - Mining DCCT’s Longitudinal A1C Data with GWAS 38:44 - Diabetes-Specific Variant & DCCT’s Selective Cohort 40:00 - Conventional vs Intensive Therapy in the DCCT 41:50 - From Finger-Prick Meters to Continuous Glucose Monitors 42:48 - Breakthrough #2: COL4A3 Variant That Protects Kidneys 45:35 - Meta-GWAS Uncovers a Protective Missense Allele 47:49 - COL4A3 Minor Allele: Reduced DKD Risk 48:56 - RAST Trial & Renal Biopsy Insights 49:52 - GBM Thickness Links Genetics to Pathology 50:45 - Diabetes-Specific Gene-Environment Interaction 52:12 - Type 2 Diabetes: Power & Confounders 52:55 - Variant-to-Function Workflow: Starting the Hunt 53:45 - Tools: GWAS Catalog & GTEx for Functional Clues 55:39 - GTEx & eQTLs — Turning GWAS Hits into Gene-Expression Clues 56:46 - pQTL Goldmines: UK Biobank, deCODE & Plasma Proteomics 57:36 - From Association to Biology: How Long Does Functional Follow-Up Take? 58:49 - Modeling COL4A3 in Diabetic Mice: A Roadmap for Mechanistic Proof 60:05 - Genetics at the Top of the Drug-Discovery Funnel 61:01 - Why Genetic Targets Double a Drug’s Odds of Success 62:35 - Stop Skipping the Sex Chromosomes! X & Y in GWAS Explained 1:03:35 - Cracking the X-Chromosome Problem 1:04:30 - Imputation & Meta-Analysis Fixes 1:05:59 - Quality Concerns: Why Researchers Hesitate 1:06:55 - Polygenic Scores Need X-Linked Signals 1:07:33 - Is the Field Getting Better? 1:08:30 - Publishing Standards & Acronym Debates 1:09:12 - So… What Does This Mean for the Average Person? 1:10:53 - Precision Screening: Genetics Guides Cancer Checks 1:11:28 - From Promise to Practice: Genes in Everyday Care 1:12:03 - Career Wisdom for Aspiring Researchers

    1시간 13분

  7. 2025. 02. 20.

    Nextflow, nf-core pipelines, MultiQC

    If you’ve ever analyzed sequencing data, chances are you’ve used tools developed by Phil Ewels—whether you knew it or not. He’s the creator of MultiQC, a key developer behind Nextflow and nf-core, and a major advocate for making bioinformatics workflows scalable, reproducible, and user-friendly. In this episode, we dive into:🔬 How MultiQC became the standard for bioinformatics quality control⚙️ The philosophy behind nf-core and the trade-offs of workflow standardization🤖 The role of AI in bioinformatics—will machines build pipelines for us?🌍 The future of open-source collaboration and making workflows more accessible If you’ve ever struggled with workflow management, questioned reproducibility, or wondered where bioinformatics is heading, this is the episode for you. 🎧 Tune in now to hear my conversation with Phil Ewels. 🤝 Connect with Phil here: https://phil.ewels.co.uk/ 🐦 Tell me what you think of the episode on X: https://x.com/Mike__Kazemi

    1시간 23분

  8. 2024. 12. 10.

    How Genomics Shapes Public Health in Bangladesh | Dr. Senjuti Saha

    Welcome to today's episode. I'm excited to introduce our guest, Dr. Senjuti Saha, the Deputy Executive Director of the Child Health Research Foundation in Bangladesh. Dr. Saha is a trailblazer in genomic research and a passionate advocate for equitable science education. From her discovery of the chikungunya virus as a cause of pediatric meningitis to her leadership roles with the WHO and her program 'Building Scientists for Bangladesh,' Dr. Saha’s work bridges the gap between cutting-edge research and real-world impact in low- and middle-income countries. Today, we’ll dive into her journey, the challenges and opportunities in global health research, and her vision for the future. Stay tuned for an enlightening conversation.

    1시간 12분
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소개

Exploring the fascinating world of genetics and its impact on human health! We break down complex science topics like DNA, genome wide association studies, CRISPR gene editing, and epigenetics into accessible conversations. Join us for weekly episodes covering cutting-edge biology research and how your genome shapes your health. Subscribe for the latest genetic discoveries explained simply!

정보

  • 제작진
    Mike Kaz
  • 방송 연도
    2023년 - 2025년
  • 에피소드
    14
  • 등급
    전체 연령 사용가
  • 저작권
    © Mike Kaz
  • 웹사이트 보기
    Double-stranded