Novel Genetic Testing Methods

Identifying cancer-related mutations accurately is a critical step in precision medicine. Today, we’ve published new research in Nature Biotechnology on 🧬DeepSomatic🧬, an AI-powered tool that uses machine learning to identify genetic variants, or mutations, in cancer cells more accurately than current methods. This work is aimed at helping researchers pinpoint what's driving a cancer and informing more effective treatment plans. Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies offer potential advantages to discover variants in the hardest to sequence parts of the genome. 🧬 About the model:  DeepSomatic was rigorously trained on high-confidence data, a feat made possible by working with our partners at UC Santa Cruz. The model is capable of accurately differentiating actual genetic cancer variants from the technical artifacts introduced during sample preservation, addressing a critical hurdle in early detection. 🧬 Superior Accuracy and Clinical Impact:  DeepSomatic consistently outperformed other tools across all major sequencing platforms. It shows major improvements in identifying complex insertions and deletions (Indels). Furthermore, in a new study with partners at Children's Mercy, DeepSomatic successfully found ten small variants in pediatric leukemia cells that were missed by other tools. 🧬 Flexible and Broad Use:  The model is flexible, working across all major sequencing platforms, and can be applied to both tumor-normal and challenging tumor-only samples, extending its utility for complex cancer types. 🧬 Open Access:  We are making DeepSomatic and the CASTLE dataset openly available to the research community. DeepSomatic is the most recent addition to our 10-year journey developing open source methods for geneticists to study the genomes of humans, plants, and animals. We are excited to see how researchers and drug manufacturers will use these resources to develop more effective, personalized treatments for cancer patients. The ability to accurately identify these subtle genetic drivers is key to unlocking new therapies. More in our blog authored by Kishwar Shafin and Andrew Carroll: https://goo.gle/4n23gIB   Read the full article in Nature Biotechnology: https://lnkd.in/drxii8fz

“UMass Amherst researchers have pushed forward the boundaries of biomedical engineering one hundredfold with a new method for DNA detection with unprecedented sensitivity. "DNA detection is in the center of bioengineering," says Jinglei Ping, lead author of the paper that appeared in Proceedings of the National Academy of Sciences. Ping is an assistant professor of mechanical and industrial engineering, an adjunct assistant professor in biomedical engineering and affiliated with the Center for Personalized Health Monitoring of the Institute for Applied Life Sciences. "Everyone wants to detect the DNA at a low concentration with a high sensitivity. And we just developed this method to improve the sensitivity by about 100 times with no cost." With traditional detection methods, he says, "The challenge is basically finding the needle in a haystack." There are lots of molecules present in a sample that aren't the target DNA that can interfere with the result. That's where this method is different. The test sample is put within an alternating electric field. Then, "We let the DNA dance," he says. "When the strands of DNA dance, they have a specific oscillation frequency." Researchers can then read samples to see if there is a molecule moving in a way that matches the movement of the target DNA and easily distinguish it from different movement patterns. This even works when there is a very low concentration of the target DNA. This new method has huge implications for speeding up disease detection. First, because it is so sensitive, diagnoses can happen at earlier stages of a disease progression, which can greatly impact health outcomes. Also, this method takes minutes, not days, weeks or months, because it's all electric. "This makes it suitable for point of care," he says. "Usually, we provide samples to a lab and they can provide the results quickly or slowly, depending on how fast they go, and it can take 24 hours or longer." Another benefit: it's portable. Ping describes the device to be similar in size to a blood sugar test tool, which opens the doors to improvements in health on a global scale. "It can be used at places where resources are limited." Ping is excited about the breadth of possible applications for this discovery, saying, "The nano-mechanoelectrical approach can be also integrated with other bioengineering technologies, like CRISPR, to elucidate nucleic acid signaling pathways, comprehend disease mechanisms, identify novel drug targets and create personalized treatment strategies, including microRNA-targeted therapies." Xiaoyu Zhang, a graduate research assistant from Ping Lab, will deliver an oral presentation relevant to this study at the Biomedical Engineering Society annual meeting on October 13, 2023 in Seattle, WA.” https://lnkd.in/g9Cjgv46

The Future of #LiquidBiopsy is Holistic For years, the field of cell-free DNA (#cfDNA) #fragmentomics has been primarily focused on the 5' ends of #DNA fragments. But what if we’re only looking at half the picture? I am excited to share a groundbreaking study recently published in #CellGenomics by Peiyong Jiang, Y.M. Dennis Lo, and their team at The Chinese University of Hong Kong: "Holistic determination of ends of cfDNA molecules". The #Innovation: Holistic Fragmentomics Conventional #sequencing library preparations often "repair" DNA ends, which, unfortunately, alters or destroys the native 3' end information. This study introduces two powerful new approaches to capture the full #molecular signature: 1. 2-End Sequencing: Using single-stranded #librarypreparation to preserve and measure both the native 5' (EM5) and 3' (EM3) ends, along with their flanking pre-end (PREM) and post-end (POEM) motifs. 2. 4-End Sequencing: A #novelprotocol using stem-loop adapters and long-read SMRT-seq to simultaneously interrogate all four ends of a single double-stranded cfDNA molecule for the first time. Why This Matters for #CancerDetection? The results are a significant leap forward for non-invasive diagnostics, particularly for #HepatocellularCarcinoma (#HCC). 1. Enhanced Accuracy: Integrating these new "constellation" markers achieved an AUC of 0.95 for HCC detection. 2. 3' FRAGMA: By applying fragmentomics-based #methylation analysis to the 3' ends, the team reached an impressive #AUC of 0.97. 3. Synergistic Power: The holistic 4-end analysis outperformed conventional 5' methods, reaching an AUC of 0.98. Beyond the Screen: #BiologicalInsights This isn't just about better #tests; it's about better #science. This holistic view enabled the researchers to dissect how #nucleases such as DNASE1L3, DNASE1, and DFFB coordinate to fragment DNA, providing a much deeper understanding of cfDNA #biology. This work opens a new dimension in liquid biopsy, moving us closer to high-fidelity, multi-dimensional #cancerscreening. Read the full study here: [https://lnkd.in/eDECCbe5) #LiquidBiopsy #CancerResearch #Genomics #PrecisionMedicine #Fragmentomics #cfDNA #Oncology #BiotechInnovation Helio Genomics OncoDaily

Ultra-sensitive CRISPR test detects pathogens in minutes- No lab needed. Researchers have supercharged the revolutionary gene-editing tool CRISPR, transforming it into a lightning-fast diagnostic test that can detect deadly pathogens in blood with unprecedented sensitivity and minimal lab work required. University of Illinois Urbana-Champaign. April 4, 2025 Overview: “One of CRISPR’s powerful features is its ability to find and bind to very specific DNA sequences,” explained Rashid Bashir, professor of bioengineering at the University of Illinois. This ability to recognize DNA sequences makes it useful for diagnostics as it can efficiently find pathogen DNA in a person’s blood. Using blood with a variety of bacterial and viral pathogens added — including hepatitis B and MRSA, the bacteria responsible for drug resistant and deadly infections — researchers found the test consistently produced clear and accurate results in only ten minutes. The tests also confirmed improved sensitivity of the method. Note: “This sensitivity is about one million times greater than a typical CRISPR enzyme assay with conventional optical signal can detect,” said Bashir. “One additional strength of the reported system is its ability to detect multiple pathogens at once. Many different pathogens can lead to bloodstream infections, having a test that can rapidly rule in or rule out key sources will help doctors begin the right antibiotic or antiviral treatment more quickly and with greater confidence.” CRISPR’s popularity as a gene editing tool means the components needed to produce the diagnostic test are widely available. “This test has strong potential for scalability,” Bashir said. Furthermore, by designing the test to use common reagents and function at moderate temperatures ensures widescale applicability. “It does not rely on expensive or complex machines,” Bashir said. “This makes it suitable for a wide range of environments including hospitals, local clinics, and mobile testing units.” Refer to the enclosed press release to review technical challenges and additional improvements which remain. Direct link to published research enclosed. https://lnkd.in/eh3JDYq6

Met a phenomenal founder - Agragesh today, our family office (The Mangrove Holdings) just invested in AcrannoLife Genomics Every cell that dies releases its DNA into your bloodstream. After an organ transplant, if rejection begins — the donor's cells start dying faster, flooding your blood with foreign DNA signals. Acrannolife - built India's first test to detect that signal. US patent. 94% accuracy. 5x cheaper than the West. This is what "Made in India" deep tech looks like. 🇮🇳🧬 The old way to detect rejection?A biopsy — a needle into the organ itself.Painful. ❌ Delayed results. ❌ Can miss early-stage rejection. ❌ Can't be done repeatedly. ❌For 2 lakh post-transplant patients in India alone, this was the only option. Until now. A Chennai-based startup called Acrannolife Genomics built a proprietary platform that: 📍 Counts individual DNA molecules in a blood sample 📍 Identifies which ones come from the donorvs. the patient 📍 Detects a spike in foreign DNA = early organ distress signal All from a simple blood draw. 🩸 Their published clinical results (Journal of Clinical & Experimental Hepatology, 2024): ✅ Sensitivity: 93.33% ✅ Specificity: 94.44% ✅ Overall Accuracy: 94.12% And it works on Day 7 post-surgery — weeks before traditional methods catch anything. The best part? The same platform that reads "is my transplanted organ dying?" can be tuned to ask: 🔬 "Is there a cancer forming?" 🔬 "Is my bowel inflamed?" (IBD) 🔬 "Is this a rheumatoid flare-up?" 🔬 "Is this pre-eclampsia?" One blood test. Infinite diagnostic potential. The global version of this technology (US company CareDx) is valued at ~$1 Billion. Acrannolife is doing it for 5x less cost. With India's 1.4 billion people as the testing ground, they're not just building a company. They're building the diagnostic infrastructure for the next billion. 🌍 Founded in 2014 by two brothers — one with a decade of research at IIT-M & MIT, one who scaled tech startups. They mortgaged their home to fund R&D. They survived COVID. They got a US patent before any other Indian genomics company.

In this edition of Research Spotlight, I want to highlight the incredible work our clinical partners are driving globally. Recently published in Human Mutation, a new study proves exactly why we cannot settle for sequence-only testing in cancer diagnostics. In nearly 3,000 patients tested with a multigene hereditary cancer panel, a deeper signal emerged only because copy number variations (CNVs) were systematically interrogated. · Nearly 10% of pathogenic findings were CNVs, variants still frequently under-detected with less comprehensive testing. · Strikingly, two-thirds of all pathogenic CNVs occurred in a single gene: PALB2, where large deletions or duplications accounted for over 60% of positive cases. · This pattern points to geography-specific effects that would be missed without robust, high-resolution CNV detection. The message is clear: structural variation matters, and so does how deeply you test for it. #NGS enables simultaneous detection of SNVs, Indels, and CNVs in a single workflow. When paired with AI-driven interpretation, its clinical value increases significantly. This is why we build category-defining AI agents like MUSKAT™ for CNV detection at SOPHiA GENETICS: to ensure patients are tested with the most precise methods available, to be able to recompute data as knowledge evolves, and help clinicians make confident, informed decisions. Excellent work from our customers at Azienda USL di Modena, who co-authored this study, on bringing these hidden variants to light and pushing the boundaries of what we can see. Authors: Lia Bonamici, Lucia Artuso, Marco Marino, Angela Toss, Diletta Sidoti, Elena Barbieri, Marta Venturelli, Isabella Marchi, Chiara Pescucci, Rossella Manfredini, Laura Papi, Massimo Dominici, Laura Cortesi, Elena Tenedini, Enrico Tagliafico

Scientists have developed a new blood based test that can identify malignant brain tumors with remarkable precision, signaling a big advance in early detection. The test focuses on gliomas, a common and aggressive type of brain cancer that is often diagnosed only after symptoms appear. In early clinical evaluation, the screening method correctly identified tumor presence in around eighty percent of cases and produced no false positives, meaning healthy people were not mistakenly flagged as having cancer. This combination of sensitivity and specificity is rare in cancer diagnostics and could transform how brain tumors are found. The test works by detecting tiny fragments of tumor derived DNA circulating in the bloodstream. Tumors shed bits of their genetic material as cells die or divide, and these fragments carry mutation patterns unique to glioma. By using advanced molecular sequencing and machine learning to spot these cancer linked signatures among the vast background of normal DNA, the test distinguishes affected individuals from healthy ones without the need for imaging or invasive biopsy. While further validation in larger and more diverse patient groups is needed, this approach hints at a future where simple blood draws could catch deadly brain cancers far earlier than current methods allow. Research Paper DOI: 10.1158/2159-8290.CD-24-1788

Every test came back normal. For 10 years, a family kept searching. DNA sequencing found nothing. Standard panels found nothing. The family kept searching. Then researchers at CHOP (Children's Hospital of Philadelphia) applied long-read RNA sequencing and finally saw what was hiding in the repetitive regions that routine tests skip. What does this mean?? Think of it like reading a page with stuttering text (the the the same same same words). Normal genetic tests skip right over those parts. This new test can actually read through all the repetition and find problems hiding there. Imagine, ten years to find one answer. Unfortunately, this isn't an isolated case. Less than 30% of rare disease patients can identify the specific genetic cause - even after DNA testing. The problem isn't that we're not testing enough. The problem is that DNA sequencing alone can't show us how variants affect RNA processing. Here's why: • DNA = the blueprint • RNA = what your cells actually build from that blueprint • DNA sequencing shows the written instructions • RNA sequencing shows what happens when cells try to follow them A variant can look perfectly normal in the DNA blueprint - but when cells try to build from it, the process breaks down. And standard DNA tests never see it. This creates a catch-22 for patient access. Payers want genetic confirmation before approving high-cost therapies. But if standard sequencing can't deliver that confirmation, patients stay in limbo. No diagnosis means no treatment eligibility. No eligibility means no access. The CHOP team built STRIPE to close this gap. It reads RNA from easy-to-collect samples like blood and skin. Lower cost than full transcriptome sequencing. Deep enough coverage to catch low-expression disease genes. They've now deployed it in 500+ patients across multiple rare disease programs. Four patients had genetic variants that were previously labeled 'uncertain' - now confirmed as the cause of their disease. Four families who had questions got answers. This is how we close diagnostic gaps that block treatment access. If you're building evidence strategies for genetic therapies, are you accounting for the patients who need RNA-level confirmation to qualify? Follow Dr. Suzanne Morgan for AI Innovation for Rare Disease

Move over "Cut and Paste." The era of "Search and Replace" genetics is officially here. We’ve all heard of CRISPR-Cas9, but Prime Editing (often called CRISPR 2.0) is the real game-changer in 2025. While traditional CRISPR acts like molecular scissors to cut DNA, Prime Editing acts like a word processor. 🧬 The Science: Why "Prime" is Better? Traditional CRISPR often causes double-strand breaks (DSBs), which can lead to "genomic chaos" or unintended mutations. Prime Editing avoids this entirely. How it works? • The Fusion Protein: It combines a "nicked" Cas9 (which only cuts one strand) with an enzyme called Reverse Transcriptase. • The pegRNA: Instead of just a guide, it uses a Prime Editing Guide RNA (pegRNA). This contains both the location of the edit and the new genetic code to be written. • The Result: It literally types new letters into the DNA without breaking the backbone of the helix. 📈 Why is this a massive shift for the industry? - Higher Precision: Traditional CRISPR can sometimes cause unintended mutations. Prime Editing "nicks" only one strand of DNA, making it significantly safer. - Proven Results: Recent 2025 trial data for Chronic Granulomatous Disease (CGD) showed a 90% correction rate with almost zero off-target errors. - Versatility: It’s estimated that this "search-and-replace" method could fix up to 89% of known pathogenic human genetic variants. 🌐 The Big Picture: We are moving away from simply managing genetic conditions toward permanently correcting them at the source. For biotech leaders and researchers, this means faster R&D cycles and a much higher safety bar for patients. #PrimeEditing #CRISPR #GeneEditing #MolecularBiology #Genomics #GeneticEngineering #Biotechnology #PrecisionMedicine #LifeSciences

🧬 Molecular amplification techniques: 1. Polymerase Chain Reaction (PCR) Principle: Uses DNA polymerase to amplify specific DNA sequences through repeated cycles of denaturation, annealing, and extension. Types: Conventional PCR: Standard method for DNA amplification. Real-Time PCR (qPCR): Monitors amplification in real-time using fluorescence. Reverse Transcription PCR (RT-PCR): Converts RNA to DNA before amplification. Multiplex PCR: Amplifies multiple targets in a single reaction. Nested PCR: Uses two sets of primers to improve specificity. 2. Loop-Mediated Isothermal Amplification (LAMP) Principle: Amplifies DNA at a constant temperature using multiple primers and DNA polymerase with high strand displacement activity. Advantages: Faster and highly specific; does not require thermal cycling. 3. Transcription-Mediated Amplification (TMA) Principle: Amplifies RNA using reverse transcriptase and RNA polymerase. Applications: Commonly used for detecting infectious agents like HIV and hepatitis viruses. 4. Nucleic Acid Sequence-Based Amplification (NASBA) Principle: Amplifies RNA at a constant temperature using reverse transcriptase, RNA polymerase, and RNase H. Applications: Used in viral diagnostics and gene expression studies. 5. Rolling Circle Amplification (RCA) Principle: Uses a circular DNA template and a DNA polymerase to produce long single-stranded DNA. Applications: Used in detecting pathogens and in nanotechnology applications. 6. Strand Displacement Amplification (SDA) Principle: Utilizes a DNA polymerase with strand displacement activity to amplify DNA sequences at a constant temperature. Applications: Used in clinical diagnostics. 7. Helicase-Dependent Amplification (HDA) Principle: Uses helicase enzymes to unwind DNA, eliminating the need for thermal cycling. Applications: Portable and suitable for point-of-care testing. 8. Recombinase Polymerase Amplification (RPA) Principle: Uses recombinase proteins to facilitate primer binding and amplification at a constant temperature. Advantages: Rapid and works at low temperatures. Applications of Molecular Amplification Techniques Medical diagnostics (e.g., COVID-19, tuberculosis, HIV detection) Forensic science (e.g., DNA fingerprinting) Genetic research (e.g., mutation analysis, gene expression studies) Agriculture and food safety (e.g., GMO detection, pathogen identification.