Over the last two decades, DNA synthesis has established itself as a standard tool propelling life science research in a wide range of fields. In this session, we will highlight how DNA synthesis tools can support mRNA therapeutics development.

One key benefit of synthetic DNA is that it enables flexible sequence design independent of any naturally occurring template. Methods for optimization of expression in a target organism will be discussed, focusing on how to achieve best results in mammalian systems. The ability to design sequences also enables incorporation of additional layers of information into the DNA. To this end, a method for “molecular watermarking” will be described, which can enable tracking and tracing of a molecule throughout the application.

Learning objectives:

1. Demonstrate understanding of the benefits of synthetic DNA, including flexible sequence design and incorporation of additional layers of information.

2. Discover methods for optimizing expression in target organisms, particularly in mammalian systems.

3. Develop insight on the various applications of mRNA therapeutics and how synthetic biology technologies can support their development and commercialization.

Infectious gastroenteritis is a disease caused by bacterial, viral, and parasitic pathogens in which the small and/or large intestines become severely inflamed. Because infectious gastroenteritis can be caused by bacteria, viruses, or parasitic pathogens, clinical laboratory testing to identify the cause is less than optimized. However, due to the number of pathogens and quickness of spread, responders must test broadly to reduce not only the spread of infectious gastroenteritis but complications and emergency room visits.

According to the U.S. Centers for Disease Control and Prevention, acute gastroenteritis is blamed for approximately 110,000 hospitalizations, more than 450,000 emergency room visits, and nearly than 2.2 million outpatient clinic visits annually, mostly threatening young children and infants. While gastroenteritis outbreaks may run underreported, these infections occur with concerning frequency and are extremely contagious.

Spread of infectious gastroenteritis appears to be worsening due to population growth and the ease of transmission from one host to another. This webinar is a must attend program for lab managers and administrators facing excess capacity issues with PCR analyzers, or those looking to reduce the cost and time in testing for gastrointestinal infections.

Learning Objectives:

1. Understand the overview of infectious gastroenteritis for reference laboratory, physician office, and nurse leaders.

2. Discuss why identifying cause of infectious gastroenteritis is difficult (causes and spread).

3. Compare advances in molecular diagnostics for infectious gastroenteritis to traditional culture and microscopy procedures (testing methods).

4. Discover new perspectives in the clinical utility of limited sets of pathogenic targets.

The personalized treatment of each cancer patient with targeted therapies selected based on our understanding of the molecular biology of cancer has been the long-standing goal of precision oncology. The Human Project completed 20 years ago provided the blueprint for the following cancer genome projects. Today, we can identify the genetic (driver) alteration in 95% of cancer cases based on whole genome sequencing (WGS), Pan-Cancer Analysis of Whole Genomes (PCAWG) Nature 578, 82–93 (2020). According to this study, each cancer harbors an average combination of four-five genetic driver alterations out of thousands of possible mutations of hundreds of cancer driver genes. The early successes of personalizing treatment decisions based on matching one genetic alteration as a predictive biomarker to one targeted therapy are not reproducible in most patients. To solve this problem, we have developed a computational (AI) method to rank treatment options based on the totality of molecular information available for each case instead of providing information about the actionability of genetic alterations one by one.

Learning Objectives:

1. Define the concept of precision oncology.

2. Explain the current state of the art method to interpret complex molecular profile.

3. Discuss the role of AI in precision oncology.

The defining characteristic of Companion Diagnostics (CDx) is that their use is associated with selection of a therapy or other medical intervention. This combination of test and treatment creates complications as both parts of any new regime need to be coordinated. At the same time there are great opportunities to improve healthcare outcomes when personalised medicine is implemented effectively. This talk will take a high-level view of healthcare systems and suggest strategies to ensure successful translation of a CDx into clinical practice.

Learning Objectives:

1. Recognize the development of the field of personalised medicine.

2. Describe the scientific, clinical and commercial forces influencing the translation of Companion Diagnostics into routine use.

3. Summarize perspectives on future developments in diagnostics and therapeutics which will further impact the field of personalised medicine.

Many oncology patients do not receive the most effective targeted treatments because of challenges associated with implementation of a personalized medicine approach. Despite a lengthy history of targeted treatment availability in advanced non-small cell lung cancer (aNSCLC), a recent study found that 644 of every 1,000 newly diagnosed aNSCLC patients (64.4%) did not receive a personalized treatment due to clinical practice gaps in the delivery of precision oncology.  Patients are lost at various steps along the precision oncology pathway because of operational inefficiencies, limited understanding of biomarkers, inappropriate test result usage, and access barriers. An increased understanding of the impact of each clinical practice gap can inform strategies to improve the delivery of precision oncology.

Learning Objectives:

1. List the clinical practice and policy challenges related to diagnostic testing and the delivery of appropriate targeted therapies leading to a failure of patients to receive precision oncology strategies.

2. Discuss what impact each clinical practice gap has on the population level delivery of precision oncology.

3. List the policies and practices that can help assure optimal and equitable delivery of appropriate high value personalized medicines.

Genomics has had rapid implementation over the past decade, with recent growth in application in oncology, mental health, maternal fetal medicine, solid organ transplantation, and more. Much of the focus has been in perinatal care, ranging from NIPT to rapid whole genome sequencing of NICU patients.  This is now expanding to cardiology, neurology, and autoimmune disease, where genomic explanations for so called general maladies are being understood.  There has also been much focus on specific drug-gene-action relationships, such that there are now a number of actionable pharmacogenomics examples. While there are exciting opportunities for diagnosis and management of specific disease areas, the application of broad sequencing panel testing gives an important moment impact a patient’s care broadly.  Altering disease trajectory across a population is an important way to apply precision genomics at scale.  This shift to understanding the broad population and the opportunities to alter a populations disease trajectory is making population health genomics an exciting area for impact.

Learning Objectives:

1. Identify various challenges in implementing genomics from research to practice.

2. Define the impact of pharmacogenomics on a patient’s risk: benefit decision.

3. Explain current approaches to applying genomics panel testing to patient populations

Next-generation sequencing (NGS) has become a foundational tool for both biological research and in-vitro diagnostics, particularly in oncology, immunology, and detection of genetic disorders. Despite its success, limitations of traditional NGS systems include long analysis times and high cost due to batching related inefficiencies.  

To address these issues Singular Genomics developed the G4 Platform, an innovative benchtop sequencer that leverages a novel 4-color Rapid sequencing by synthesis (SBS) chemistry with advanced optics and fluidics engineering to provide single-day turnaround times across all applications. By combining fast run times and the ability to run up to 4 flow cells, with 16 independently addressable lanes, the G4 Platform enables highly efficient laboratory operations.  

In this webinar Dr. Timothy Looney will describe the features of the G4 and showcase data from core NGS applications such as whole genome sequencing, targeted somatic variant detection, methyl-Seq, and bulk and single cell RNA-Seq.

Learning Objectives:

1. Explore the performance of the G4 for core NGS applications.

2. Describe how the adaptive capacity of the G4 enables fast, cost-efficient sequencing, resolving NGS sample batching and sequencing related inefficiencies.

3.Examine how Max Read technology reduces the cost of single cell RNA-seq.

Experience the Revolution of DNA Polymerase Technology: Choosing the Best DNA Polymerase for Efficient PCR Workflows.

PCR has revolutionized modern molecular biology, but its success would not be possible without the advancements in DNA polymerases. The initial polymerase used in PCR reactions was thermolabile, requiring replenishment in every cycle, making it a slow and monotonous process. Fortunately, modern thermostable and fast polymerases now offer accurate results, optimal performance, and streamlined workflows. In this webinar, gain insight into the evolution of DNA polymerase technology and learn tips for selecting the most suitable polymerase for your PCR applications. Don't miss out on this opportunity to improve your PCR efficiency and enhance your research outcomes.

Learning Objectives:

1. Discover comprehensive knowledge of PCR principles, components, and real-world applications

2. Discover the evolutionary path of DNA polymerase technology and its impact on PCR development

3. Evaluate the most effective DNA polymerases and workflows to achieve optimal performance in PCR applications

The Genomes2People Research Program (G2P), led by Robert Green at Brigham and Women’s Hospital, the Broad Institute, Ariadne Labs, and Harvard Medical School, conducts research to accelerate the implementation of genomic medicine and the promise of precision health. In his talk, "The Path to Universal Newborn Sequencing", Dr. Green discusses the latest advancements in genomics research and the promise it holds for personalized and preventive medicine, drawing on his work leading the MedSeq and BabySeq projects.

Throughout his talk, Dr. Green highlights the progress made by the MedSeq and BabySeq projects in advancing our understanding of genomics and its application to clinical care. MedSeq explored the use of whole genome sequencing in primary health care and cardiology specialty care, while BabySeq focuses on the use of genomic sequencing as screening in newborns. In its current phase, BabySeq is enrolling 1,000 families from diverse populations and using whole genome sequencing as screening to assess clinical utility and cost effectiveness.

Dr. Green discusses how the insights gained through these projects are paving the way for a more personalized and preventive approach to medicine. He emphasizes the importance of integrating genomic data into clinical care and the need for policies and guidelines to ensure that this is done in an ethical and responsible manner.

Through rigorous scientific research, G2P is establishing a foundation of evidence for genomic medicine that will accelerate the adoption of novel genomics-based technologies and hasten the day when illness is not just treated, but prevented.

Learning Objectives:

1. Discuss the current state of genomics research and its potential to improve human health through personalized and preventive medicine.

2. Discuss the arguments for and against population based genetic screening.

3. Identify the percentages of healthy adults and infants who carry unanticipated monogenic disease risks or atypical pharmacogenomic variants as observed in the MedSeq and BabySeq projects.

4. Explain the difference between indication-based genetic testing and predictive genetic testing.

5. Describe the actions taken for both infants and their families in the BabySeq Project based on unanticipated monogenic disease risks.

6. Assess whether follow up was appropriate or exaggerated for babies identified with unanticipated monogenic disease risk findings in the BabySeq Project.

The last 20 years have seen an explosion of genetic information and data. New technological advances have made it faster and less expensive to understand the human genomes but most of those resources have focused on northern European communities. 

Throughout his keynote speech, Dr. Bustamante elaborates on the major challenge to enabling precision health at a global scale, and the bias between those who enroll in state sponsored genomic research and those suffering from chronic disease. More than 30 million people have been genotyped by direct-to-consumer (DTC) companies such as 23andMe, Ancestry DNA, and MyHeritage, providing a potential mechanism for democratizing access to medical interventions and thus catalyzing improvements in patient outcomes as the cost of data acquisition drops. In this keynote speech, Dr. Bustamante presents a novel geno-pheno platform that integrates heterogeneous data sources and applies learnings to common chronic disease conditions. 

He further covers how the SARS-CoV-2 pandemic has differentially impacted populations of varied race, ethnicity and socioeconomic status. His research team leveraged a pandemic tracking strategy in which they sequenced viral and host genomes, and transcriptomes from nasopharyngeal swab residuals and integrated them with digital phenotypes from electronic health records. They demonstrated over-representation of individuals possessing Oceanian and Indigenous American ancestry in SARS-CoV-2 positive populations. This work demonstrated the power of multi-omic pandemic tracking and genomic analyses to reveal distinct epidemiologic, genetic and biological associations for those at the highest risk. 

The next phase of this work will leverage clinical-grade biobanking, research and clinical genomics, paired with best-in-class ancestry and genome annotation algorithms towards better understanding the genetic architecture of underserved populations and powering the next generation of precision medicine studies. 

Learning Objectives:

1. Discuss the need for diversity in advancing the future of precision medicine.

2. Uncover the adverse effects caused by the deficiencies in the approach to drug production today.

3. Explore unique mechanisms for enhancing currently employed biobanking practices.

With the health economic benefits of genetic testing clear, testing is becoming more accessible, which means constantly increasing throughput requirements for already stretched genomics sequencing labs. For this reason, many lab managers turn to automation as a potential solution to solve for scale. Similarly, discovery labs using multi-omics workflows in drug discovery and research are working with ever increasing throughputs.

Today, when a genomics lab is automated, iit usually means a series of very specialized instruments that are still reliant on humans to tend to them with often mundane tasks like waiting for an instrument to complete a run and move consumables and samples through a workflow.

This talk will introduce the concept of "open integrated automation" as a means of achieving the full potential of automation in laboratory workflows, and to truly define automation beyond the benchtop instrument. Integrated automation connects the steps of the workflow both physically and digitally to create a seamless and hands-off process, to enable increasing throughput by up to 4x, while reducing hands-on times by up to 98%, all in the same space.

The open platform is vendor agnostic and collaborative, with a modular approach that can be modified and scaled as needed, keeping the needs of your lab in mind. This approach minimizes errors, reduces contamination risk, improves data quality, and saves valuable time for your scientists.

Approximately 13% of the human genome at certain motifs have the potential to form non-canonical (non-B) DNA structures (e.g. G-quadruplexes, cruciforms, and Z-DNA), which regulate many cellular processes, but also affect the activity of polymerases and helicases. Because sequencing technologies utilize these enzymes, they might possess increased errors at non-B structures. To evaluate this, we analyzed error rates of Illumina, Pacific Biosciences (PacBio) HiFi, and Oxford Nanopore Technologies (ONT) sequencing at non-B motifs. Single-nucleotide error rates were generally increased for G-quadruplexes (G4s) and Z-DNA motifs in all three technologies. Deletion error rates were also increased for all non-B types except for Z-DNA motifs in Illumina and PacBio HiFi, while in ONT they were increased substantially only for G4 motifs. Insertion error rates for non-B motifs were highly elevated in Illumina, moderately elevated in PacBio HiFi, and only slightly elevated in ONT. Thus, elevated sequencing errors at non-B DNA motifs should be considered in low-read-depth studies (single-cell, ancient DNA, and pooled-sample population sequencing) and when scoring rare variants. Combining technologies should maximize sequencing accuracy in future studies of non-B DNA.

Learning Objectives:

1. Describe alternative (non-B) DNA structures, how prevalent are they and what are their biological implications?

2. Explain why non-B DNA structures might be relevant for sequencing technologies.

3. Identify examples where non-B DNA induced sequencing errors might be relevant.

Spinal muscular atrophy (SMA) is a devastating neuromuscular disease caused by mutations in the SMN1 gene that reduce survival motor neuron (SMN) protein expression. Despite progress in the field that has resulted in therapies, there remains no permanent cure for SMA. SMN2 is a paralogous gene that mainly differs from SMN1 by a C•G-to-T•A transition in exon 7, resulting in the skipping of exon 7 in most SMN2 transcripts. Editing of the SMN2 exon 7 mutation should in principle offer a therapeutic strategy to restore SMN protein levels. Here, I will present a base editing approach to precisely edit SMN2 to revert the T•A to C•G mutation. We tested a range of different adenosine base editors (ABEs) and Cas9 variants, resulting in >80% intended editing in patient-derived fibroblasts with concomitant increases in SMN2 exon 7 and SMN protein levels. Delivery of these optimized ABEs via a dual adeno-associated virus approach resulted in precise SMN2 editing in an SMA mouse model. Moreover, we generated and characterized ABEs fused to high-fidelity versions of Cas9 variants which reduced potential off-target editing. These novel technologies may provide a long-lasting cure for SMA with several advantages compared to exogenous gene replacement. 

Learning Objectives:

1. Discuss the genetic basis of a severe neuromuscular disease.

2. Explain how base editors work.

3. List some basic scientific considerations while developing a gene editing therapy.

Viome Operating System (ViOS) is an open access Systems Biology platform that has integrated laboratory, computational, and logistics components to enable efficient clinical research on a global scale and rapid translation of research findings into products that improve human health. The main features of ViOS are big data and artificial intelligence. The data are very relevant to chronic diseases and cancers, and mainly comprise metatranscriptomic (RNA sequencing) data from both human and microbial cells in multiple sample types (typically stool, blood, and saliva). So far, ViOS has generated 750 trillion RNA bases of sequencing information, using >500,000 samples from >350,000 people in >100 countries.

Learning Objectives:

1. Discover how Genetics is not the major determinant of chronic diseases and cancers; human microbiome and nutrition heavily contribute to their onset and progression.

2. Explain how the Systems Medicine approach that uses gene expression, in addition to genetics, is a powerful approach to identifying root causes of chronic diseases.

3. Discuss how personalized nutrition is becoming the backbone of preventative medicine.

Despite major advances in deciphering the genome, barriers to incorporating genomics into clinical care abound. Overcoming these barriers requires extensive infrastructure, including standards, knowledgebases and global data sharing, as well as innovative solutions and a rich interface between research and clinical care. This talk will present both local and global efforts to support clinical genomics from diagnosis to gene discovery.

Learning Objectives:

1. Describe the rationale for sharing genomic and health data to improve patient care.

2. Identify approaches that can aid in the interpretation of non-coding regions of the genome to solve rare disease.

3. Locate a platform that is freely available to analyze genomic data.

It has been publicly stated by the CDC that one in 5 Americans had an STI in 2018 and costs our healthcare system roughly $16 billion in direct medical costs. Routine sexually transmitted infection (STI) screening is a critical part of health monitoring among sexually active individuals and an important public health issue.

During the pandemic there was an increased demand for self-collected sampling (SCS) screening for STIs. SCS in combination with telemedicine services for managing and treating STIs removes barriers of privacy, social stigma and access to care in rural areas. Nearly half of all new STI asymptomatic infections are in young adults aged 15-24, in which barriers such as social stigma and access to care are easily resolved with at-home testing in combination with telehealth. This combination of testing services has shown comparable NPV compared to standard of care collection by a healthcare provider, and is becoming more widely accepted within the regulatory world. Similar lab testing methodologies are used when screening for STI by at-home SCS showing comparable analytical sensitivity and specificity. The ability to receive high-quality accessible STI screening and diagnostic results (where applicable) via At-home testing provides an effective solution to sexually active individuals to take care of their own healthcare.

Learning Objectives:

1. Discuss the differences in standard of care (SOC) testing and self-collection sampling (SCS) for STI testing, as whereas where current regulation stands.

2. Explain the accuracy of at-home testing.

3. Recognize the importance of telemedicine in providing access to care in STIS.

Silk is essential to the life histories of more than 800,000 species across the arthropod tree of life. Humans have harnessed the ability of a handful of these for use in the sericulture industry. However, little to nothing is known about the genomic basis of this remarkable biomaterial for the vast majority of silk-spinning species. Efforts focused on uncovering the genomic basis of silk fibers have historically been hindered by the inability to sequence and assemble the large, repetitive regions of the genome that encode for silk proteins. Recent advances in DNA sequencing, e.g., high fidelity long-read sequencing, have revolutionized our ability to sequence and assemble high-quality genomes at an unprecedented scale. Here I discuss these advances and share how this new technology has allowed us to uncover hidden variation in silk genes across insects and spiders.

Learning Objectives:

1. Discuss the differences between short-read sequencing and long-read sequencing.

2. Explain why long-read sequencing is essential to repetitive regions of the genome.

3. Explain why assembling both haplotypes of a diploid species is essential to understanding the true variation in a genome.

Racial and ethnic classification in research and medicine is rooted in a centuries-long practice of categorizing humans into distinct groups to justify colonization, slavery, and genocide. The legacy of these harms to communities lingers today, as the U.S. still uses six distinct categories in racial and ethnic classification for official reporting. Increasingly, people identify with more than one (or none) of these categories, which presents a dilemma for biomedical research and clinical trials that incorrectly assume they can be used to approximate genetic populations. Just as the human genome’s complexity necessitates an updated reference, the spectrum of human diversity requires a dynamic and inclusive model to account for social, cultural, geographic, and genomic populations. Categorical and free-text data from different studies can be combined to reveal complexity in human populations that racialized frameworks have erased. For example, geographic origin(s), language(s), and genomic variation can be combined with demographic data to reveal trends in relationships among variables across cohorts. When aggregated with additional data streams, these data will offer more inclusive, respectful, and granular descriptions of study populations than categorical frameworks. This has the potential to improve the utility of existing free-text data on study populations that have been traditionally excluded from genomic analyses and clinical risk evaluation. Adoption of an open-ended model for self-identification may also influence the way science and society consider population diversity, enhancing precision, inclusiveness, and respect for individual and community identities.

Learning Objectives:

1. Discuss the conceptual limitations of the term, "population" as applied to human genomics.

2. Explain the main difference(s) between "race", "ethnicity", and "ancestry" and how they are conflated with human genetic background.

3. Describe the overall approach to sampling and representation in the 1000 Genomes Project.

The current version of the human reference genome, GRCh38, contains a number of errors including 1.2 Mbp of falsely duplicated and 8.04 Mbp of collapsed regions. These errors impact the variant calling of 33 protein-coding genes, including 12 with medical relevance. We present a modified GRCh38 reference that corrects errors while maintaining the same coordinates allowing us to leverage the extensive existing annotations of GRCh38, along with an efficient remapping approach, FixItFelix, that enables quick and efficient re-analysis to gain improved insights from existing data while maintaining the same reference coordinates. We showcase these improvements over multi-ethnic control samples across short and long-read DNA-, and RNA sequencing , demonstrating improvements for population variant calling as well as eQTL studies.

Learning objectives

1. Discuss what are some Issues are with GRCh38 human reference genome.

2. Explain how the errors in reference can be corrected and the impact of those errors can be fixed.

3. Discuss a better remapping approach for re-analysis of using the fixed reference.

The All of Us (AoU) initiative aims to enhance personalized medical care by sequencing the genomes of over one million Americans of diverse ethnic backgrounds. To improve sequencing accuracy, we conducted a recent technical pilot comparing traditional short-read sequencing with long-read sequencing in a small cohort of samples from the HapMap project and two AoU control samples, representing eight datasets. Our analysis revealed significant differences in the accuracy of these technologies in sequencing complex, medically relevant genes, particularly in terms of gene coverage and identification of pathogenic variants. We also evaluated the advantages and challenges of using low-coverage sequencing to increase the number of samples in large cohort analysis. Our findings show that HiFi reads provide better results for both single nucleotide variants (SNVs) and structural variants (SVs). We developed a cloud-based pipeline optimized for long-read analysis, which includes tools for SNV, indel, and SV calling at scale. These results have significant implications for improving the accuracy and efficiency of sequencing in the AoU initiative and beyond. Furthermore, we extended our findings to produce a complex medically relevant gene analysis panel encompassing 389 genes, including genes related to cardiovascular, neurodegenerative diseases, and cancer-related genes (e.g., LPA, MSN1/2). By sequencing 12 samples per SMRT cell, we achieved highly accurate variant calling, reaching up to 98.95% for substitutions, 94.44% for indels, and 94% for SVs while reducing the sequencing costs per sample. We also developed a tailored publicly available pipeline and gene-specific tools that exploit mapping and assembly-based methods to achieve optimal results.

In summary, our study demonstrates the advantages of using HiFi sequencing technology and provides a pipeline and gene-specific tools that can be used to improve sequencing accuracy and reduce costs in large-scale genomic studies.

Learning objectives:

1. Demonstrate understanding of the purposes and goals of the All of Us program and learn how to utilize the program's data for research purposes.

2. Differentiate between short-reads and long-reads and identify the limitations of short-read sequencing compared to long-read sequencing.

3. Discover the best practices for calling Single Nucleotide Variants (SNVs) and Structural Variants (SVs) using long-read sequencing data and explore ways to reduce costs while still benefiting from long-read sequencing technology.

Long-read sequencing was declared the method of the year for 2022 and, indeed, has fully arrived on the computational biology scene. Specifically, Oxford Nanopore Sequencing technology has enabled computational approaches to assemble accurate and complete human genome haplotypes while providing unprecedented methylation profiling. However, homozygous regions and variability in read lengths limit traditional SNP-based haplotype phasing methods. To address this, we have developed MethPhase, a computational method for automated methylation-based haplotype phasing of the human genome. MethPhase is the first in the long-read era to utilize SNP and methylation signals for human genome phasing. We show MethPhase improves upon widely used SNP-based human genome phasing approaches by bridging the SNP-phased blocks to resolve the ambiguity in homozygous regions. MethPhase is also able to rescue previously un-haplotagged reads from SNP-based methods. We will conclude with future directions and the next steps.

Accurate prediction of anticancer drug efficacy on each patient’s cancer before the initiation of therapy has the potential to significantly reduce the treatment burden and enhance clinical outcomes. Because cancer is a systematic disease, we need to analyze a patient’s cancer cells in many aspects and combine complex biological and clinical data. This session will introduce a precision medicine platform that predicts anticancer drug efficacy and prognosis evaluation by combining a patient’s live-cell-based ex vivo drug sensitivity assay, immune profiling, blood work, and genotyping with computational modeling that uses artificial intelligence (AI) algorithms. We will cover basic concepts of AI and machine-learning-based predictive models and see how powerful it is to apply machine learning algorithms in improving cancer treatment outcomes.

Learning Objectives:

1. Discuss the concepts of machine learning (ML) and functional precision medicine.

2. Explain the essentials to build successful machine learning models.

3. Discuss how artificial intelligence (AI) models built on patients' live-cell analytics help predict anticancer drug response and prognosis and provides clinical benefits in risk stratification and treatment selection in the real world.

In this talk, cellular and molecular details on the human and microbial responses to short and long-duration spaceflight will be detailed, which span several NASA, SpaceX, and Axiom missions and provide key lessons for upcoming lunar and Mars missions. Then, recent advances in biotechnology, multi-omics, data modeling, and cross-species genome engineering will be highlighted, which have shown novel means of extremophile adaptation in space and chimeric human cells with increased radioresistance. Together, these tools, methods, and data support an ethical and ambitious, 500-year plan of reengineering biology to enable life on other worlds, and also reveal the best candidate planets for life in new solar systems.

Learning Objectives:

1. List types of sequencing and genomics methods.

2. List several methods for epigenome engineering.

3. Describe the genomic changes that occur in a spacecraft and in astronauts.

Mutations accumulate in the genome of every cell of the body throughout life, causing cancer and other genetic diseases. Almost all of these mosaic mutations begin as nucleotide mismatches or damage in only one of the two strands of the DNA prior to becoming double-strand mutations if unrepaired or misrepaired. However, current DNA sequencing technologies cannot resolve these initial single-strand events from which mutations originate. This talk will present a novel single-molecule, long-read sequencing method that achieves single-molecule fidelity for single-base substitutions when present in either one or both strands of the DNA. It also detects single-strand cytosine deamination events, a common type of DNA damage. Since the double-strand DNA mutations interrogated by prior studies are only the endpoint of the mutation process, this approach to detect the initiating single-strand events at single-molecule resolution will enable new studies of how mutations arise in healthy tissues and in cancer.

Learning Objectives:

1. Describe the key features of aging-related somatic mutation in the body.

2. Discuss the limitations of standard DNA sequencing in studying somatic mutation.

3. Evaluate the principles of new DNA sequencing technologies with higher accuracy that can detect aging-related somatic mutations in healthy tissues.

Genomic testing based on chromosome microarray (CMA) and Next Generation Sequencing (NGS) revolutionized clinical genetics. That said, microarray, targeted panel, exome and generic whole genome sequencing (WGS) share some of the same shortcomings. Targeted sequencing is sensitive to inappropriate test selection, poor coverage in the intron /exon junctions and often fails to detect copy number variants (CNV) that affect only a few or a single exon. For WGS the main shortcoming is that structural variants in the 50bp-10,000bp range are either completely ignored during analysis or are not reported because of the difficulty to confirm these findings with an orthogonal method. Copy number neutral structural variants such as inversions and reciprocal translocations can also escape detection and complex structural rearrangements are often difficult or impossible to disentangle without the use of an orthogonal technology such as optical genome mapping (OGM). Short repeat expansions (e.g. FragileX syndrome) and long repeat contractions (FSHD) cannot be properly evaluated with short read WGS either.

OGM is a novel technology that can be best described as high-resolution cytogenetics. Depending on the genomic region, its ability to detect structural variants is down to the 5-10 thousand base pair range. Since it is based on microscopic evaluation of individual, very high molecular weight DNA molecules, copy number neutral structural variants and large repeat expansions and contractions can be directly visualized. Combined usage of WGS and OGM not only makes detection of these variants easier, but also provides a method to provide orthogonal confirmation of the findings.

Variants detected by WGS and OGM often classify only as variants of unknown significance since many have not been previously reported. Transcriptome analysis allows both the evaluation of the expression levels of the affected genes and alterations in their promoter usage and splicing. It can provide the required functional characterization allowing classification.

We propose that WGS should always be performed together with OGM and the functional significance of the findings should be verified using transcriptome sequencing.

Learning Objectives:

1. Discuss the reason why microarray, targeted panel , whole exome and even generic whole genome testing might not result in a genetic diagnosis

2. Discuss how to increase sensitivity of genomic testing by incorporating optical genome mapping in the whole genome analysis pipeline

3. Discuss the utility of transcriptome sequencing in the interpretation of variants detected using Whole Genome Sequencing and Optical Genome Mapping.

Our health, social, and economic systems are becoming increasingly interconnected across the globe. Though substantial benefits have emerged global interconnection has created novel biological risks. Invasive species, habitat destruction, and the spread of human infectious disease have all been consequences of globalized systems. The COVID-19 pandemic showed that these risks not only pose risk to people and the environment but can create domino effects in systems themselves. Recent studies have shown that more than half of all infectious diseases could be made worse by climate change, further exacerbating these risks.In spite of this there are few effective systems to model biological risk at a global scale. This is partly because modeling biological systems is difficult. Such systems are inherently complex and cannot be understood using only one type of data. Any framework for modeling data must necessarily incorporate data from genomics, earth systems, public health, and sociology. It must be able to incorporate local knowledge and empower experts to share their approaches and expertise.