John ten Bosch, PhD, FACMG’s Post

𝗕𝗶𝗼𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗰𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 and 𝗖𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗕𝗶𝗼𝗹𝗼𝗴𝗶𝘀𝘁 Opportunity at Cancer Research UK National Biomarker Centre Area: 𝗕𝗶𝗼𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗰𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: Genomic, epigenomic, (single-cell) transcriptomic, immunogenomic and imaging data), design, develop, and maintain bioinformatics pipelines and tools for processing and analysing high-throughput data. 𝗖𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗕𝗶𝗼𝗹𝗼𝗴𝗶𝘀𝘁: Developing a multi-modal liquid biopsy test for the early detection of non-small cell lung cancer (NSCLC). Computational data analysis of NGS-based methylation and fragmentomics data arising from cfDNA samples, including using machine learning to develop a multi-modal classifier. #b𝗶𝗼𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗰𝘀 #transcriptomic #liquidbiopsytest #𝗖𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹𝗕𝗶𝗼𝗹𝗼𝗴y #machinelearning #nonsmallcelllungcancer #highthroughputdata #cancerresearch

In today's posts, we'll explore three critical areas in bioinformatics: Next-Generation Sequencing (NGS), Single-cell RNA Sequencing (scRNA-seq), and essential bioinformatics pipelines. These technologies and methodologies have revolutionized biological research and clinical applications. ________________________________________ 🧬 What is Next-Generation Sequencing (NGS)? 🔍 Introduction to NGS: Next-Generation Sequencing (NGS) refers to high-throughput sequencing technologies that enable the simultaneous sequencing of millions of DNA fragments. Unlike traditional Sanger sequencing, NGS offers significantly higher speed, accuracy, and scalability. 🌟 Importance of NGS: NGS has become a cornerstone in genomics and molecular biology, revolutionizing research and clinical diagnostics. It allows for comprehensive and detailed analysis of genetic material, making it indispensable for modern scientific inquiries. 🔬 Use Cases of NGS: 1. Genomics: • Whole Genome Sequencing (WGS): Provides a complete picture of an organism’s DNA. • Variant Detection: Identifies genetic variations such as SNPs and indels. • Genome Assembly: Helps in constructing the genome from short sequence reads. 2. Transcriptomics: • RNA Sequencing (RNA-seq): Studies gene expression and transcriptome profiling to understand how genes are regulated. 3. Epigenomics: • DNA Methylation Analysis: Studies chemical modifications of DNA that affect gene expression without changing the sequence. • Histone Modification Analysis: Examines how proteins called histones are chemically modified to regulate DNA. 4. Metagenomics: • Microbial Community Analysis: Identifies and characterizes microorganisms in various environments. 🛠️ NGS Workflow: 1. Sample Preparation: • DNA/RNA Extraction: Isolating genetic material from samples. • Library Preparation: Preparing DNA/RNA fragments for sequencing. • Fragmentation: Breaking DNA/RNA into smaller pieces for sequencing. 2. Sequencing: • Platforms like Illumina, PacBio, and Oxford Nanopore are used to read the sequence of DNA/RNA fragments. 3. Data Analysis: • Read Alignment: Mapping sequence reads to a reference genome. • Variant Calling: Identifying genetic variants from the aligned sequences. • Downstream Analyses: Includes annotation, functional analysis, and interpretation using bioinformatics tools. 🔗 Applications in Research and Clinical Fields: NGS is pivotal in research areas such as genetic disease studies, evolutionary biology, and personalized medicine. In clinical settings, it aids in diagnosing genetic disorders, understanding cancer genomics, and developing targeted therapies. #NGS #Genomics #Transcriptomics #Epigenomics #Metagenomics #Bioinformatics #NextGenerationSequencing

Snippet 34 #Nextgenerationsequencing (NGS) is a powerful technology that rapidly sequences entire genomes, exomes, or targeted DNA/RNA regions. It has transformed genetic research, clinical diagnostics, and personalized medicine by offering detailed insights into genetic variations & mutations. Various types of mutations detectable through NGS have significant implications for biology & medicine. Here’s an overview of the mutations identified by NGS: ## 1. **Single Nucleotide Variations (SNVs)** SNVs, also known as point mutations, involve a change of a single nucleotide base in the DNA sequence. These can be classified into: - **Synonymous mutations**: Do not alter the amino acid sequence of a protein (silent mutations). - **Nonsynonymous mutations**: Alter the amino acid sequence of a protein. These include:  - **Missense mutations**: Change one amino acid to another.  - **Nonsense mutations**: Change a codon to a stop codon, leading to premature termination of protein synthesis. ## 2. **Insertions and Deletions (Indels)** Indels involve the insertion or deletion of small segments of DNA. These can cause frameshift mutations if the length of the inserted or deleted sequence is not a multiple of three nucleotides, potentially leading to significant changes in the protein sequence downstream of the mutation. ## 3. **Copy Number Variations (CNVs)** CNVs are larger segments of the genome (ranging from kilobases to megabases) that are duplicated or deleted. These variations can affect gene dosage and contribute to various diseases, including cancer. ## 4. **Structural Variations (SVs)** SVs encompass a wide range of larger genomic alterations, including: - **Translocations**: Segments of DNA are rearranged between non-homologous chromosomes. - **Inversions**: Segments of DNA are reversed within the chromosome. - **Duplications**: Large segments of DNA are copied and inserted into the genome. - **Large deletions**: Large segments of DNA are removed from the genome. ## 5. **Microsatellite Instability (MSI)** Microsatellites are short, repetitive sequences of DNA. MSI occurs when there are insertions or deletions of repeating units in these regions, often due to defects in the DNA mismatch repair system. MSI is a hallmark of certain types of cancers, such as colorectal cancer. ## 6. **Fusion Genes** Fusion genes result from rearrangements that join two previously separate genes. These are particularly common in cancer and can create novel proteins with oncogenic properties. ## 7. **Mitochondrial DNA Mutations** NGS can also sequence mitochondrial DNA, identifying mutations specific to the mitochondrial genome. These mutations can be linked to various mitochondrial diseases and conditions. ## 8. **Epigenetic Modifications** Although not mutations in the DNA sequence, NGS can also assess epigenetic changes, such as DNA methylation and histone modifications, which can affect gene expression and contribute to disease. https://shorturl.at/RlL2H

🧬 Next-Generation Sequencing (NGS) vs. Traditional STR Analysis in Forensic DNA Profiling 🔬 Next-generation sequencing (NGS) is transforming forensic DNA analysis, offering significant advantages over traditional short tandem repeat (STR) analysis using capillary electrophoresis (CE). Let's explore the impact of NGS on forensic genetics: 🚀 Advantages of NGS 1. 🔍 Enhanced Accuracy in Mixture Analysis 📊 Detailed Sequence Information: NGS provides comprehensive sequence data, allowing differentiation between alleles based on nucleotide sequences, not just fragment lengths. 🧩 Isoallele Detection: NGS can identify alleles with identical lengths but different sequences, increasing discriminatory power in complex mixtures. 🧬 SNP Integration: Incorporation of single nucleotide polymorphisms (SNPs) near STRs aids in differentiating contributors in mixed samples. 2. ⚡ Improved Efficiency 📈 High Throughput: NGS enables simultaneous analysis of numerous loci, surpassing the typical 20-30 loci in a single CE run. 💪 Better Performance with Degraded Samples: NGS can handle low-quality DNA more effectively using smaller amplicons and overlapping allele size ranges. 💰 Cost-Effectiveness for Large-Scale Analysis: While initial setup is costly, NGS becomes economical for high-volume processing. 🚧 Challenges of Traditional STR Analysis 📉 CE Limitations: Restricted by the number of analyzable markers per run and prone to artifacts like stutter. 🔀 Difficulty with Complex Mixtures: CE methods often struggle to resolve profiles with multiple contributors, especially with low-abundance DNA. 🤔 Considerations for NGS Implementation 1. 🛠️ Practical Challenges 💸 High Initial Costs: Significant investment required for equipment and training. 🧠 Specialized Expertise: Need for personnel skilled in NGS technology and bioinformatics. 🧮 Complex Data Interpretation: The wealth of data from NGS introduces new analytical challenges. 2. 📜 Regulatory Hurdles ✅ Validation Requirements: Many jurisdictions require extensive validation of NGS methods before courtroom use. 🏅 Accreditation Process: Laboratories must undergo rigorous accreditation to implement NGS in casework. 3. 🌐 Current Adoption Status 🔄 Gradual Integration: While NGS use is increasing, CE-based STR analysis remains the primary method in many forensic labs due to established protocols and extensive validation. 🔮 Conclusion NGS offers significant advancements in forensic DNA profiling, particularly for complex cases and degraded samples. However, its implementation faces practical, financial, and regulatory challenges. As the technology matures and becomes more accessible, NGS is poised to play an increasingly important role in forensic genetics, complementing rather than immediately replacing traditional methods. note : The image is Gemini AI-generated! #ForensicScience #DNAAnalysis #NGS #Forensics #Genetics #Innovation

Career Options for Recent #BiomedicalScience Graduates in #Genomics With the rapid advancements in #genomics and #personalizedmedicine, recent #biomedicalscience graduates have an exciting range of career options in this transformative field. 1. Genomics Research Scientist As a research scientist, you can work in academic or industry laboratories, exploring the genetic basis of diseases, drug responses, and human traits. This role involves using tools like next generation sequencing (NGS) and #bioinformatics to analyze large datasets and generate discoveries that drive innovation in diagnostics and therapeutics. 2. Clinical Genomicist Clinical genomicists work in healthcare settings, analyzing genetic data from patients to guide diagnosis and treatment, particularly in areas like cancer, rare genetic disorders, and #personalizedmedicine. This role offers the opportunity to directly impact patient care by translating complex genomic information into actionable insights. 3. Genetic Counselor With a deep understanding of genomics, genetic counselors work alongside healthcare teams to help patients understand the implications of genetic tests. This role involves communicating complex genetic information to patients and their families, helping them make informed decisions about their health and treatment options. 4. Bioinformatician As a #bioinformatician, you can develop and apply computational tools to manage, analyze, and interpret vast amounts of genetic data. This role is crucial in turning raw sequencing data into meaningful biological insights, making it ideal for those with strong data analysis and coding skills. 5. Public Health In public health, genomics is used to track disease outbreaks, understand population genetics, and inform policy decisions. Public health professionals can work with government agencies, NGOs, and international health organizations to apply genetic insights for better disease prevention and control strategies. 6. Genomic Data Scientist Combining expertise in #genomics and data science, genomic data scientists work on developing algorithms, models, and tools to process and interpret large genomic datasets. This is a high-demand role for those who enjoy working at the intersection of biology, mathematics, and computer science. 7. Laboratory Technician Lab technicians play a vital role in genomic research and clinical laboratories by preparing samples, running genomic assays, and maintaining advanced sequencing equipment. This hands-on role provides foundational experience in the day-to-day workings of a #genomicslab. The field of genomics is growing rapidly, and the demand for skilled professionals is higher than ever. At CMB Genomics, we are committed to supporting the next generation of genomic scientists by providing access to advanced tools, training, and career development opportunities. #Genomics #BiomedicalScience #CareerDevelopment #GenomicResearch #PersonalizedMedicine #Bioinformatics

NGS pipeline running smoothly in production: Just share my happiness that the NGS pipeline, a multi-module package that is able to analyze DNA/RNA NGS sequencing data has been in production for over half year without major issues. So it fulfill my goal of making a product that is useful/helping patients! This NGS pipeline was designed as multi-module format, each module could used individually for different purpose. Combination of different modules become individual pipeline for particular purpose. It is able to analyze DNA variants, DNA tmb/msi, DNA CNV, DNA structure variant (multiple types), RNA fusion, RNA splicing, RNA variants, RNA expression, RNA Subexon analysis as well as varieties of QC measures/controls/filters including contamination, panel wide summary, potential noise filter, which is able to filter out more than 95%-98% of calls and keep sen/spec. Most unique capability is that it is able to convert all RNA splicing, fusion from different package(designated) into a star-fusion like report, which simplifies a lot of things. It is also flexible in switching between regular NGS analysis as well as UMI analysis; flexible in switching between different acceleration package, like GPU/parallel CPU packages; flexible in switch of target by different panel definition; In addition, it is also also flexibly in switching between Germline NGS as well as Somatic NGS, with simple switching between the configure file and settings. Developing such pipeline wasn't easy, long story :-). In the design concept stage, have to refresh my mind that all the mistakes that I had made before; then create the fundamental prove-of-concept pipeline; Validation is not a happy process, a lot of debates on how exactly the validation should be performed, happily the reasoning prevailed and the validation was passed; Then in the last step there were modules designed to make the final result to the format consistent to the report system, quite some debate in between as that was a new system, but finally it pass through; Then is to the stage of pass the pipeline to the production team, based on their feedback to optimize the module to fit the production requirements, this was going very smooth and happy, thanks the production team! There was some fun stories regarding to the take-for-granted 3rd party pipelines, one example is that a structure caller was well-known, result turned out 14 out of 16 fusion break-point was wrong. Though it still detected but if some research need the corresponding fusion sequence, that would be an issue. More optimizations and new modules could be developed going forward. Since I also work in digital pathology by AI/machine learning, my next story may be my recent experience in AI in digital imaging and the interaction between the pathologist (also a lot of stories!). have fun!

Next-generation sequencing (NGS) encompasses a suite of advanced sequencing technologies that allow for high-throughput, accurate, and cost-effective DNA sequencing. These techniques have revolutionized genomics by enabling the rapid sequencing of entire genomes, transcriptomes, and epigenomes with significant improvements in speed and affordability compared to traditional Sanger sequencing. Several prominent NGS platforms utilize different sequencing strategies, each offering unique advantages. Illumina Sequencing (Sequencing by Synthesis) is the most widely used NGS technology. It involves fragmenting DNA, attaching adapters, and amplifying the fragments into clusters on a flow cell. During sequencing, fluorescently labeled nucleotides are incorporated one by one into the growing DNA strand, and the emitted fluorescence is detected to determine the base sequence. This method provides high accuracy and throughput, making it ideal for applications like whole-genome sequencing, exome sequencing, and transcriptomics. Ion Torrent (Semiconductor Sequencing) works by detecting pH changes as nucleotides are incorporated into a growing DNA strand. Each nucleotide release protons, which alter the pH of the solution, and these changes are detected by a semiconductor chip. Ion Torrent is known for its speed, ease of use, and relatively low cost, though it may have slightly lower accuracy than Illumina technology, making it more suitable for targeted sequencing or smaller-scale projects. PacBio (Single-Molecule Real-Time Sequencing, SMRT) offers long-read sequencing by detecting fluorescence emitted during the synthesis of DNA. This method provides long reads (up to tens of kilobases), enabling researchers to resolve complex genomic regions, structural variations, and de novo genome assemblies. PacBio is particularly valuable in applications requiring high accuracy and long-read sequences, such as metagenomics and large-scale structural variant detection. Oxford Nanopore (Nanopore Sequencing) works by passing DNA through a nanopore and measuring changes in electrical conductivity as each nucleotide passes through. This technology offers exceptionally long reads and can generate real-time sequencing data, making it ideal for applications such as rapid microbial sequencing and portable field-based analyses. While it has lower accuracy compared to other platforms, its ability to sequence long stretches of DNA in real time is unparalleled. 10x Genomics (Linked-Read Sequencing) uses barcoded short DNA fragments to provide long-range sequencing information. This method is valuable for resolving structural variations, haplotype phasing, and single-cell sequencing. It combines the benefits of both short-read accuracy and long-read insights, allowing for detailed genomic analysis with high resolution. #NextGenerationSequencing #Genomics #NGS #Bioinformatics

bioinformatics

Today, I would like to talk about 10x Genomics as a company. Last week, I gave a detailed analysis of 2 other genomics companies Illumina: https://lnkd.in/e7XSdeQV and Tempus AI: https://lnkd.in/gQxGRQg2 10X is also being beaten up by Wall Street and has lost over 6% of its value this year. From a high of $62/share, it is now trading below 20. Why are things looking so bad for 10x Genomics? Let's start by discussing their core business. 10X is a the leading provider of single-cell RNA sequencing and for most users, they are the default company. When the typical researcher thinks about single-cell sequencing, they usually mean 10X (see the poll below). This is similar to NGS where people usually mean Illumina even though many other companies provide sequencing instruments. The main purpose of single-cell sequencing is to determine the individual variations between a population of cells. In a tube of blood, a cancer biopsy or a section of brain, there can be a dozen or more different cell types represented. When you do standard bulk RNA-seq, you get an average of all of these cell types and can distinguish any of the components. Understanding sub-populations is critical for the investigation and treatment of resistant clones in cancer. Why are certain cells still surviving a targeted therapy? How many different subsets of cells are there in the tumor and can we find ways to attack them individually? Can we make a cocktail of cancer dugs to completely treat a patient? 10X makes the leading tools for this analysis. They also have a growing business in spatial and sub-cellular genomics, but the primary business is still on single-cell. Why is the market unhappy? 1. There is increasing competition from players such as Parse Biosciences, Scale Biosciences, Fluent BioSciences Inc. and others. These techniques do not require the purchase of expensive bespoke hardware 2. People are questioning the need for single-cell sequencing and its clinical benefits. 3. Single-cell costs are holding steady or increasing as compared to NGS that is getting much cheaper. I think that there is still a very strong market for 10X and other players in this space. It is still very early for single-cell sequencing and researchers are searching for the killer app. It is not hard to think about using 10X as part of a companion diagnostic. Presume that sensitivity to drug X in cancer Y can be predicted by a single-cell 10X profile. Anyone wanting to prescribe this drug will need to run patient samples on 10X and this will be a windfall for the company. Clinical development takes time Clinical trials take even longer 10X will be strong in the long run, just need to give them some time

Hello network! I’m not a routine poster type. It’s a tough biotech market out there with lots of job loss going on. I was laid off on Wednesday night so now I’m also officially looking for the next gig. New jobs are often like joining a new band…different players with different talents and backgrounds that come together to write new songs. I’m considering genomics R&D and data roles- would love to lead a team in a director role to launch new products. I’ve helped startup 4 different companies now. I also think I would make a good CSO if opportunity arose. I care deeply about keeping costs down and constantly innovating and improving (sometimes sequencing everything is not the answer, although then again with reagent costs per sample on Nova X and AVITI platforms, maybe we should?). I’ve been working with short read NGS since Illumina GAII days (yes paired end sequencing runs took 2 weeks without problems if you were lucky). I enjoy working on fragmentomics, DNA methylation, RNA-seq, aging and health associations with epigenetics, T cell biology, multiple cancer early detection with LBx, tissue of origin determination etc. In my earlier days I was a cell/developmental biologist by training and worked on muscle stem cells, coculture, IF, and aging in murine models. I’ve also worked with zebrafish and chick. I had some NIA support for some of my work. I’ve put in a few solid months with building imaging mass cytometry panels for colorectal cancer on Hyperion. I love working with data, finding tools to explore, cleaning and setting up data, plotting data, and fitting models. I’ve built genomics assays from design to results that include deconvolution of blood from methylation data, novel urine and plasma LBx in human and canine, custom genotyping and bisulfite seq using a target hyb capture method called CATCH-seq. Most don’t know that you don’t have to spend thousands on oligo-generated probes for hyb capture panels. Check out BioDynami if you have time. As head of R&D at Vidium, I just recently completed collecting data in a fairly large veterinary clinical study in oncology. We had an incredible team that worked tirelessly to make this happen and I’m super proud of this work (I posted about this challenging project we had ahead of us at the beginning of the year). I worked on age-dependent methylation using bisulfite seq and bead array data across multiple human tissues and showed that stochastic methylation effects alone are not sufficient to explain the age effect and also validated many of these sites. I’ve often thought why desert sunsets seem so spectacular as they are a fitting picture to the end of a chapter as we reflect on what we gain through each experience and what the next rotation will bring. https://lnkd.in/gAZE2hf6