Our laboratory uses tools from pharmacology, genomics, and cell signaling to identify new precision anticancer therapeutic strategies. Under this broad heading, our work involves three key areas of emphasis. First, we use custom functional and structural genomic tools to define the landscape of signaling pathways capable of driving resistance to therapy (for example, Science Signaling 2012, 5, rs4; Science Signaling 2014, 7, ra121; and Cell Reports 2017, 20, 999). Second, we utilize knowledge of resistance landscapes to identify new therapeutic strategies that have the potential to circumvent resistance evolution (for example, Science Signaling 2014, 7, ra122; Nature Communications 2017, 8, 15617; Cell Reports 2017, 21, 2796; Nature Communications 2018, 9, 4274; Science Advances 2019, 5, eaaw9162; and Nature Genetics 2020, 52, 408). Finally, we use unbiased approaches to define mechanism-based, targetable vulnerabilities in human cancers for application to malignancies characterized by “undruggable” genetic drivers or immunologically “cold” microenvironments, where in many cases these vulnerabilities center on the dysregulated structure and function of tumor mitochondria (for example, Science Translational Medicine 2016, 8, ra175; Nature Communications 2018, 9, 1677; Nature Communications 2018, 9, 3513; and Cell Metabolism 2019, 29, 1217). Collectively, these studies are leading to both fundamental new insights into the core survival circuitry operating in defined human tumor subsets as well as novel translational therapeutics. In this talk, I will provide an overview of our work, with a particular focus on studies that have identified mechanisms of convergent tumor evolution and associated strategies to leverage this phenomenon to build combination therapies that select against resistance.

Learning Objectives:
1. Understand strategies for designing mechanism-based combination therapies to overcome resistance

2. Learn examples of convergent resistance evolution

3. Use functional genomics and pharmacological screening to identify therapeutic targets

Recent technological advances as well as longitudinal monitoring not only have the potential to improve the treatment of disease (Precision Medicine) but also empower people to stay healthy (Precision Health). We have profiled 109 participants using multiomics technologies (genomics, immunomics, transcriptomics, proteomics, metabolomics, microbiomics and wearables etc) for up to 9.5 years and made 49 major health discoveries. We have also profiled people after an acute bout of exercise and found that thousands of blood molecules change after a short bout of exercise. Lastly, we demonstrate that individuals have distinct aging patterns that can be measured in a period of time that is actionable. We conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health.

Learning Objectives:

1. What is wrong with healthcare today

2. What kinds of omics and wearables data can be used to best predict disease risk and manage health

The development of automated DNA sequencers using fluorescent di-deoxy nucleotide sequencing and capillary electrophoresis made it possible to generate the first draft sequences of the human genome about 20 years ago. This information revolutionized our understanding of the organization of the human genome, but the cost for doing this was several hundred million dollars. The solution to dramatically decreasing the cost of sequencing was the development of technologies that all depended upon the simultaneous sequencing of a very large number of DNA molecules which was termed massively parallel sequencing (MPS). There are two generations of these technologies. The first generation depended upon amplifying individual DNA fragments and then determining the sequence of those amplified fragments. The amplification process and the subsequent sequencing of those fragments could only be performed on very short fragments and thus all first generation MPS are short-read technologies. There are two strengths to these approaches and that is sequence accuracy and ultimately tremendous sequence output. In my talk I will focus on the various first generation MPS technologies that were developed and their strengths and weaknesses. I will also describe how with the use of these technologies it became possible to increase sequence output (and correspondingly decrease sequence costs) from hundreds of millions of dollars two what is now approaching $100 per genome. I will also describe how this will change just about everything in our society. Other speakers in this session will then describe some of the second generation MPS technologies which can obtain very long DNA sequences and are thus long read sequencing technologies. We will also hear about some of the clinical applications of both first and second generation MPS technologies as well.

Learning Objectives:

1. To understand about the different short read MPS technologies and their strengths and weaknesses

2. To understand how being able to characterize the complete genome sequence for an individual for $100, or less, will completely transform just about everything

Resolving the genetics of underlying Mendelian diseases as well as host immune responses to infectious diseases is an important prerequisite for understanding their biology and for ultimate treatment options. A number of affected genomic loci are difficult to fully characterize due to underlying sequence complexities. The introduction of long and accurate, single-molecule (HiFi) sequence reads by PacBio has allowed for an improved resolution of many of these genomic regions, resulting in highly accurate, full-length gene sequencing with allelic phasing. The associated workflows are efficient and allow for cost effective scaling to large sample volumes. I will highlight several examples of recent advances in clinical research applications, with a particular emphasis on COVID-19 host response relevant targets, including HLA typing, pharmacogenetics, B Cell repertoire sequencing and resolution of the IGH locus.

Learning Objectives:

1. Become familiar with HiFi sequence reads which are both long and accurate at the individual sequence read level

2. Learn about the benefits of full-length, phased gene sequencing with efficient and cost effective workflows allowing for large numbers of samples

3. Learn about improved resolution of previously difficult to resolve clinical research genomic targets, with a particular emphasis on COVID-19 research related loci

Single-cell RNA sequencing has emerged as an important technology that can reveal gene expression differences between different cell types. This webinar will demonstrate how bioinformatics solutions from QIAGEN Digital Insights were used to analyze and interpret whole transcriptome data from a human single-cell sequencing experiment. Using a public dataset from normal liver from deceased donors that has been carefully curated and processed for the QIAGEN OmicSoft Land database, we will describe the gene expression differences between cell types, and illuminate the underlying biology using QIAGEN Ingenuity Pathway Analysis (IPA).

Learning Objectives:

1. Identify single-cell RNA-sequencing projects of interest within QIAGEN OmicSoft

2. Download a Single Cell Land Project of choice into QIAGEN OmicSoft Suite (Array Studio) and analyze and visualize gene expression differences in different cell types, or within a unique cell type

3. Compare gene signatures enriched in clusters of cells versus tens of thousands of gene expression signatures in QIAGEN IPA to pinpoint significant genes associated with hepatic or immune function

4. Identify significant cell-specific canonical pathways or function-specific regulators

5. Generate hypotheses about novel regulatory networks that suggest drivers of the expression changes observed in the different cell types

6. Visualize important information regarding a gene of interest in other contexts (disease, expression)

7. Gain additional insights about single cell biology by comparing this human liver cellular landscape dataset in QIAGEN IPA to more than 60,000 additional public datasets which have been curated and processed in QIAGEN OmicSoft Suite

CRISPR-Cas9 gene editing is a powerful technique that enables genetic modification with greater speed and accuracy than previous approaches. In this webinar, Dr. Chen highlights the advantages of single-stranded DNA vs double-stranded DNA for knock in experiments. He further discusses the tips for designing ssDNA, methods for synthesis and various applications of single-stranded DNA.

Learning Objectives:

1. Understanding the mechanism and importance of the Homology Directed Repair (HDR) pathway for CRISPR mediated gene knock-ins

2. Understand why ssDNA is a powerful tool for gene knock-ins and why ssDNA is superior compared to dsDNA in terms of efficiency, cellular toxicity, and specificity

3. Multiple methods of ssDNA synthesis are available; however, the nicking enzyme-based method results in optimum quality

The introduction of improved, more cost efficient target capture technologies has accelerated the adoption of Whole Exome Sequencing (WES) in clinical diagnostics. The ability to provide more complete answers to patients with rare diseases in turn has increased the interest of physicians in genetic testing. Clinical genetic testing today is generating more real world evidence than any large scale research sequencing project has or will ever have. However, this valuable real world evidence remains underutilized due to the lack of proper technology and due to legal obstacles. In practice WES generates large amounts of information that need to be processed and interpreted. If used in routine diagnostics, the data processing and the interpretation must be standardized, clinically validated and ultimately automated to a high degree. The hands-on time of creating a diagnosis must be a matter of minutes and not of days or weeks. Taking together the genotype and phenotype information from a large number of patients can improve the success rate in diagnostic testing significantly. Healthcare institutions address these challenges to a variable degree by creating and maintaining proprietary in-house software or by using a patchwork of third-party solutions. A single, streamlined solution for these challenges is varvis, a diagnostic software that covers the entire workflow from raw data to interpretation and reporting. Using varvis, diagnostic laboratories across Europe, Canada and the USA are aggregating and sharing real-world evidence and improving patient care in compliance with data privacy regulations. The aggregated database now contains information about 19,000,000 unique variants from 35,000 cases. I will present case examples how patients benefit from this knowledgebase and how it enables clinical labs to quickly scale up WES diagnostics.

Learning Objectives:

1. How Whole Exome Sequencing has matured to become a first-line diagnostic method in clinical genetics and what its benefit is for the patient

2. Which tools and processes are necessary to successfully deploy this powerful method in routine diagnostics at scale

3. How every data point from every single patient can be leveraged to continuously improve the quality and success of the diagnostic process.

Estrogen metabolism is the ability to favorably excrete potentially toxic estrogens through urine and sweat. Impaired estrogen metabolism is associated with Endometriosis and Polycystic Ovarian Syndrome as well as Breast and other cancers. For women with suboptimal function, excess estrogen exposures such as estrogen-based medications and environmental phytoestrogens may result in greater risk of sporadic breast cancer. Improving a woman’s ability to excrete estrogens along optimal pathways may lessen these risks.

The objective is to improve estrogen health through individualized nutritional supplementation.

Tests available in commercial labs examine modifiable genes with the goal of providing individualized nutritional interventions to improve estrogen health. Genes frequently tested are: CYP1A1, CYP1B1, CYP3A4, GSTM1, & COMT. Each gene may have 2 mutations or a deletion.

Information about a patient’s genetic mutations provides a critical understanding of each woman’s lifelong disposition to excrete estrogens. This guidance allows physicians more precision in prescribing estrogen medications such as birth control pills, hormone replacement and fertility treatments. Test results also advise physicians which supplements may improve estrogen health and which patients require stricter oversight.

TOWARD PERSONALIZED TREATMENT PROTOCOLS

Personalized treatment requires:

1. Estrogen Metabolism Genetic Testing to provide data on lifelong capability to excrete estrogens

2. Physician Intake of Estrogen Metabolism related disease, Exogenous Estrogen Exposure and Extended Family History of Estrogen Metabolism related conditions

3. Nutritional Supplementation to Improve Estrogen Health

4. Metabolite Testing for Supplement Dosing Adjustment

Learning Objectives:

1. Overview of Estrogen Metabolism & Sporadic Breast Cancer Risk Factors

2. Discussion of how Estrogen Metabolism Genetic mutations or health can guide precision interventions and prescribing estrogenic medications

3. Review of case studies

Realizing the promise of Precision Medicine requires both a deep understanding of the landscape of genomic evidence and an understanding of the molecular drivers that influence all aspects of the drug development pipeline. Advances in genomic sequencing technology provide incredible opportunities for the use of genetics to drive drug discovery, but the lack of organized empirical data and the challenge of manually interpreting and annotating genomic evidence has limited its enormous potential. Using real-world examples, Dr. Kiel will demonstrate how Genomenon has overcome these obstacles and is being used by pharmaceutical companies to provide invaluable insight into the genomic drivers of disease. Drawing from its exhaustive database of empirical evidence and a unique combination of AI and expert curation, Genomenon’s Mastermind® produces comprehensive genomic landscapes to validate candidate targets, inform downstream research and discovery, and segregate patients for enrollment in clinical trials. Dr. Kiel will also discuss how Genomenon is pursuing its vision of curating the entire human genome to advance the drug discovery process and maximize the efficiency of clinical diagnosis.

Learning Objectives:

1. Why a deep understanding of genomic evidence is critical at each stage of the drug development process

2. How genomic evidence can be used in each stage using real-world examples

3. How Mastermind Curated Genomic Datasets are being used to increase the success rate of drug candidates, broaden patient populations, and identify new disease indications

Complex genomes, including the human genome, contain ‘dark’ regions that standard short-read sequencing technologies do not adequately resolve—overlooking many variants that may be relevant to disease. We systematically characterized these regions in genomes with high short-read coverage, identifying more than 6000 gene bodies that are at least partially dark, and more than 100 protein-coding genes are 100% ‘camouflaged’. Many known disease-relevant genes are also camouflaged, including CR1, a top Alzheimer’s disease gene. Other disease-relevant genes include NEB, SMN1 and SMN2, and ARX. Long-read technologies resolve major portions of these regions. We specifically compared 10x Genomics, PacBio’s Sequel, and Oxford Nanopore Technologies’ PromethION, demonstrating potential long-term benefits of using long-read technologies. We are also utilizing optical DNA mapping from the Bionano Genomics’ Saphyr System to construct full individual haplotypes across challenging genomic regions that are strongly implicated in disease, including the C9orf72 ‘GGGGCC’ repeat expansion, CR1, and major histocompatibility locus, which harbors the human leukocyte antigen (HLA) genes.

Learning Objectives:

1. Identify types of genetic variants that are overlooked with standard short-read sequencing approaches, including ‘dark’ and ‘camouflaged’ genes

2. Identify specific genes that are ‘dark’ or ‘camouflaged’, including 76 disease-associated genes

3. Demonstrate how long-range technologies resolve these regions

In recent years Nanotherapeutics has revolutionized the healthcare strategies and envisioned to have a tremendous impact to offer better health facilities. It involves design, fabrication, regulation, and application of therapeutic drugs and devices having a size in nano-range (1–100 nm). Owing to the revolutionary implications in drug delivery and gene therapy, lipid nanoparticles has gained increasing research interest in the current medical sector of the modern world. Current clinical efforts are focused on vaccination, protein replacement therapies, and treatment of genetic diseases. Since the first pre-clinical studies in the 1990s, significant progress in the clinical translation of mRNA therapeutics has been made through advances in the design of mRNA manufacturing and intracellular delivery methods. The growth of such powerful biologic therapeutics has gone hand in hand with the progress in delivery systems technology, which is absolutely required to improve their safety and effectiveness.

One of the most developed methods for RNA delivery is co-formulation into lipid nanoparticles (LNP). LNP as drug delivery vehicles is becoming more attractive because it can pack drug product more efficiently, enhance its bioavailability, and target specific cells. In this presentation, we discuss the revolution of lipid nanoparticles into precision medicine and recent advances in biomaterials and drug delivery strategies, challenges for clinical translations of RNA based therapeutics with an overview of the future clinical applications in protein therapy, gene editing, and vaccination.

Learning Objectives:

1. What current Nanomedicine are being developed for drug delivery

2. Why use lipid nanoparticles for RNA delivery

3. Recent advances and clinical applications

With the completion of the human genome nearly two decades ago, consumers, patients, and physicians are wondering when genetic testing will go PRIME TIME.  While the diagnostic utility of genetic testing continues to steadily increase, the idea of population health driven genetic testing is just getting started.  This talk will explore how genetic testing can be used to better manage the healthcare of large populations, the challenges and concerns with launching such a program, and how it is different than diagnostic genetic testing.

Learning Objectives:

1. Define how genetic testing fits into population health management strategies

2. Understand how genetic testing for screening purposes is different than that used for diagnostic purposes

3. Describe the value of population health genetic screening to society

As the spread of infectious diseases, current pandemic, and growing antimicrobial resistance (AMR) continues globally, next-generation sequencing (NGS) and specifically metagenomics became a tool to diagnose infectious diseases. Biotia’s comprehensive NGS test profiles the microbiome including bacteria, viruses, fungi, and parasites by leveraging our robust database including 16,000 microbial species, 3,000 AMR and 34,000 virulence factors. The rapid identification of pathogens providing key information on species, strains, virulence factors, and antimicrobial resistance markers. Biotia’s NGS and artificial intelligence-based approach enables emerging pathogen and strain identification, patient risk stratification, AMR tracking, clinical interpretation and outcome prediction.

Learning Objectives:

1. Facilitate in-depth knowledge about harnessing microbial genetic information to develop breakthrough diagnostic tools

2. Leverage insights gathered from the microbiome and virome to fight against infectious diseases through the development of precision diagnostics

As genomics plays an ever expanding role in healthcare and research, educational programs focused on applied genomics have not kept pace with the demand for trained students.  In this talk, we will discuss some of the challenges and opportunities in training the next generation of genomics scientists, particularly in the field of clinical genomics.  Special attention will be paid to the knowledge domains and skills in highest demand in academic, industry, and healthcare settings.  We will also discuss delivery platforms and program modalities, especially considering the disruption to higher education institutions due to the COVID-19 social distancing efforts.

Learning Objectives:

1. To identify the primary skills and knowledge domains in applied genomics training programs

2. To recognize challenges in applied genomics education in the current environment

3. To appreciate opportunities to expand the genomics expertise pipeline

In this presentation Dr. Kevin Halling describes the use of RNA-seq testing for identifying clinically significant gene fusions in tumors that can be used to help establish diagnosis and guide therapy.  The methodology used to perform the testing and the bioinformatic analyses that are used to interpret the data are explained.  A case study is used to illustrate the identification of a gene fusion in a sarcoma.

Learning Objectives:

1. Describe the clinical utility of gene fusion detection in tumors

2. Explain how clinically significant gene fusions are distinguished from clinically insignificant gene fusions that are identified by RNA-seq

Achieving a diagnosis in rare disease patients can be challenging for even the most experienced clinician or scientist, creating a barrier to delivering truly personalised care. An exome sequencing approach in these patients provides a balance between cost and benefit, allowing for all disease-causing genes to be analysed simultaneously and therefore reducing the need for sequential testing whilst optimizing diagnostic yield. In this talk state-registered Clinical Scientist Louisa Ive will present real-world cases that exemplify how exome sequencing is being used to end the diagnostic odyssey for rare disease patients and their families.

Learning Objectives:

1. Why is exome sequencing of particular clinical utility for patients with neurodevelopmental disorders?

2. What was the diagnostic yield following exome sequencing in this cohort (prenatal and postnatal)?

3. Discuss the clinical utility of achieving a diagnosis for one of the patients presented

Graphene-based Biology-gated Transistors (Cardean Transistors) directly read molecular signals of active biology using advanced electronics. This proprietary tech breakthrough can be used as a point-of-care reader to rapidly detect and identify disease markers, forever changing the tracking of current and future infectious diseases as it becomes near real-time and handheld.

Learning Objectives:

1. Explain current available testing options and their limitations

2. Define how Biology-gated Transistors can be used for diagnostic purposes

3. The analytical and real-time opportunities that come with cloud-computing software and machine learning

Careful and deliberate packaging of the genome is essential to ensuring correct and timely transcriptional programs. Chromatin conformation capture (3C and Hi-C) is a powerful approach for capturing the long-range chromatin interactions that directly influence gene expression. During this seminar, you will learn how…

- Chromatin conformation capture is being applied to address a variety of biological questions, particularly in the field of epigenetics

- Uniform sequence coverage offered by the Dovetail™ Omni-C™ Kit is expanding the utility of Hi-C to include WGS applications

- The highest resolution views of genome topology are being generated with the Dovetail™ Micro-C Kit

- To map protein-directed chromatin architecture using the Dovetail™ HiChIP MNase Kit, an approach that combines Hi-C and ChIP-seq

Learning Objectives:

Learn how chromatin conformation capture is being applied to address a variety of biological questions, particularly in the field of epigenetics

Learn about chromatin conformation works and how to interpret data

Learn about the different solutions offered by Dovetail Genomics and how they can be applied to your research

Within the clinical arena, the oncology community has led the way in the early adoption of next generation sequencing. NGS is in widespread use in clinical cancer research and is now beginning to help guide clinical treatment in many hospitals and cancer centers. There remain, however, significant challenges in using standard NGS technology alone to construct a fully detailed picture of cancer and other human disease. New tools and methods are needed to discover and reliably detect critical structural rearrangements, such as fusions and translocations, particularly in challenging areas of the genome where short-read approaches stumble. Here we deploy a new to proximity ligation methodology based on Dovetail™ Omni-C™ technology that provides genomic coverage akin to whole genome shotgun sequencing and captures long-range information from a single NGS library. The observed uniformity of coverage expands the utility of proximity ligation to interrogate variants from SNVs through to large SVs. Using a variety of samples, including the cancer cell line HCC1187, we demonstrate that the novel data type enables the capture and phasing of variants. Additionally, we have developed an informatic tool to accurately identify and call inter-chromosol translocation events. This program detects 100% of previously described tloc events down to 3X coverage. The tool has also identified tloc events that are undescribed in well characterized cell lines. To highlight the flexibility of this data type, we used an off-the-shelf hybrid capture panel to enrich the data for known oncogenes. Proximity ligation techniques, like Omni-C technology, offer a powerful data type for building more complete genomes, unraveling genome structure, and unveiling new insights into genome biology and human disease.

Learning Objectives:

1. Gain an understanding of how proximity-ligation data can reveal large structural variants

2. Delineate an approach to detecting inter-chromosomal translocations in proximity-ligation contact matrices

3. Learn how to build a multi-dimensional genomic profile through proximity-ligation data

The introduction of PacBio HiFi sequence reads, which are both long (up to 25 kb currently) and accurate (>99%) at the individual single-molecule sequence read level, has allowed for advances in de novo genome assemblies. I will briefly review the characteristics of HiFi read data obtained with the Sequel II System, followed by examples of high-quality genome assemblies for human, plant and animal genomes. I will highlight the different aspects of evaluating genome assemblies (contiguity, accuracy, completeness and allelic phasing) and illustrate their high quality by examples of resolving centromeres, telomeres, segmental duplications and other previous difficult to resolve regions. I will also present on advances with regard to reducing the DNA input requirements (down to 5 ng for some of the presented examples) to allow for the sequencing of single individuals of small organisms, and summarize the latest advances in using full-length RNA seq (Iso-Seq) for genome annotation.

Learning Objectives:

1. Understand the characteristics of HiFi sequence reads

2. Understand the latest advances in de novo genome assemblies using HiFi read data for high-quality (contiguous, accurate and complete) human, plant and animal assemblies

3. Learn about advances in reducing the DNA input requirements for sequencing single individuals of small organisms

Karyomapping, a technique for preimplantation genetic diagnosis (PGD) of single gene disorders, involves using very few or single cells, and comes with challenges due to limited amounts of sample DNA. To overcome these issues, whole genome amplification (WGA) can be used to provide high quality amplified DNA for karyomapping applications - but reliable WGA performance is necessary for accurate results.

In this webinar, we'll discuss the REPLI-g Advanced DNA Single Cell kit for whole genome amplification using MDA (multiple Displacement Amplification) technology and its use in karyomapping applications. Get comprehensive overview of how MDS works, the features and workflow of the new kit, user feedback and several case study examples.

Learning Objectives:

1. Overview of Multiple Displacement Amplification (MDA) technology

2. Discussion of performance data and REPLI-g workflow

3. Review of two case studies

Modern biomedical research is being driven by large scale genetic and proteomic research to identify new targets for the study of disease mechanisms. While these techniques have been incredibly successful, it has been difficult to follow-up on these increasingly complex data sets at large scale with precision. CRISPR/Cas9 genome engineering has emerged in the last decade as a versatile and powerful toolkit to address target validation and functional analysis of genes of interest. However, many of the methods, especially those for knocking in foreign DNA sequences, require a high level of expertise, are limited in their efficiency, and are labor intensive to deploy. In this webinar, we will present a powerful new technology termed Homology-independent Universal Genome Engineering (HiUGE).

Key topics we’ll cover include:

- CRISPR/Cas9 Knock-In Mechanisms

- Homology Directed Repair vs Non-Homology End Joining

- HiUGE – payload libraries to simplify research workflows

Learning Objectives:

1. Can you utilize CRISPR Knock-In to benefit your research?

2. What advantages does HiUGE have for CRISPR Knock-In?

3. What applications are possible leveraging the HiUGE platform?

The versatility of standard PCR and qPCR is well known. Digital PCR is now taking this to a whole new level. The power of partitioning enables you to explore new frontiers which have been limited with present-day standard technologies: the more partitions, the higher the resolution and sensitivity. Besides, with more partitions, a higher volume of diluted sample can still be analyzed. QIAGEN’s digital PCR technology is superior to any standard technology in handling difficult samples with inhibitors or complex mixtures. Its versatility and capability find enormous use in the fields of gene expression analysis, ancient DNA analysis, environmental DNA analysis, viral load detection, biomarker identification from liquid biopsy, copy number variation detection and validation of NGS results.

In this webinar, we’ll take a deep dive into the following topics:

• The power of partitioning (eDNA, aDNA, SNP, miRNA and gene editing)
• Detection of high copy number variation
• Assay setup, tips, and tricks

Learning Objectives:

1. Can you perform absolute quantification?

2. What's better absolute or relative quantification?

3. What's the advantage over NGS and qPCR?

Long-read DNA sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore (ONT) platforms, have demonstrated enhanced detection of genomic variation, including Single Nucleotide Variants (SNVs), Structural Variants (SVs) and methylation changes. Individual studies so far have, however, have focused only on one of the three classes of variation: SNVs, SVs or methylation changes. Furthermore, only a few studies include phasing information to improve prediction of these classes of variation, to better associate genetic variation with phenotypes. Thus, clinical and research studies both currently lack a comprehensive view of genomic variation, even though the primary data is present in their DNA sequences. Here we introduce PRINCESS, a method that provides haplotype resolved SNVs, SVs and methylation changes based on a single long-read sequencing run from either PacBio or ONT. PRINCESS automatically adapts to different sequence coverage levels to optimally leverage the data set at hand. Thus, PRINCESS provides cost and time efficient comprehensive insights of haplotype resolved genomic variation. This information can be leveraged to simultaneous study the interaction of SNVs, SVs and methylation changes and their impact on phenotypic changes. PRINCESS was evaluated using Genome in a Bottle (GIAB) Oxford Nanopore standard and ultra-long reads as well as PacBio Continuous Long Reads (CLR) and Circular Consensus Sequencing (CCS) data. Using only one SMRT or PromethION flow cell Princess achieved high SNV precision (97.01%, 99.54%, 92.11%) and sensitivity (80.32%, 70.32%, 87.45%) for PacBio CLR, CCS and ONT PromethION, respectively, with minimum Genotype accuracy 98% of all read types. For SVs Princess also reached a high precision (93%, 94%, 86%) and a high sensitivity (77%, 79%, 79%). Both variant types were phased, achieving high N50s of 152Kbp, 117kbp and 17.42Mbp for PacBio CLR, CCS and ONT PromethION, respectively. We are currently evaluating methylation results from ONT. This highlights the versatility and performance of PRINCESS. PRINCESS applied to 18 PacBio with matching RNA-Seq data samples improved the detection of SVs (on average 22,105), SNVs and phasing (~5 Mbp average N50) and thus allowed the detection of eQTL in an automated, fast and comprehensive fashion.

Learning Objectives:

1. Princess: one-stop for all variant detection

2. Comprehensive understanding of variations using long-reads (PacBio and Oxford Nanopore Technologies)

3. Effect of CCS inserts size on Single Nucleotide and Structural Variation detection

The diagnostic yield in genetic disease has seen very little improvement over the last few decades, despite the introduction of whole genome sequencing. The Bionano Genomics platform for genome imaging offers an extremely long-read technology, providing unmatched sensitivity and specificity to detect structural variation, genome-wide, at low cost. Our de novo maps can resolve complex repetitive regions, identify Copy Number Variations, and elucidate genome-wide structural variation like balanced/unbalanced translocations, inversions, and indels with much higher sensitivity and precision than sequencing-based methods. For mosaic samples, Bionano’s high coverage depth allows for the detection of any type of structural variant with more than 90% sensitivity, present in as little as 10% of the cells, genome wide, and completely unbiased. Examples will be presented of how Bionano’s platform is helping solve genetic mysteries for patients with a variety of genetic disorders by detecting genomic rearrangements and structural variants missed by NGS and cytogenetic methods.

Learning Objectives:

1. Learn why the complexity and repetitive structure of the human genome makes it impossible to detect all structural variants using short-read sequencing

2. Learn how high coverage optical mapping enables detection of structural variants at extremely low allele fraction

Structural variation within plant genomes has fueled domestication and selection of traits critical for modern breeding of crop plants. Nevertheless, dissecting the diversity of structural variation at the population level has remained challenging. I will discuss how we revealed the extent and the genomic features of structural variants in 100 tomato accessions using long-read sequencing. I will show that transposons underlie a large fraction of structural variants. In a second part, I will provide a characterization of a transposon family that has driven structural variation and has facilitated the emergence of phenotypic variation in tomato.

Learning Objectives:
1. Dissection of the structural variation landscape in the tomato genome

2. Characterization of a transposon family involved in structural variation in the tomato genome

Since the introduction of single cell sequencing techniques to the genomics community, interest in its use has grown exponentially and the number of single cell publications has exploded. The first single cell application was single cell RNA-Seq, which enabled scientists to focus on the gene expression profiles of individual cells rather than on tissue averages. As new single cell applications have been developed the trend is now the integration of two or more omics data sets known as single cell multi-omics. Currently, the most common is the integration of gene and protein expression data such as feature barcoding and CITE-Seq. Another exciting development is tissue transcriptomics, where gene expression data is collected together with histological information. There are many bioinformatic tools and analysis strategies available that can pose significant research challenges and easily shift the focus from biological questions to “how do I” questions. 

Learning Objectives:

1. An ECCITE-Seq project (cell hashing, CRISPR, gene expression, and protein expression) and

2. A spatial transcriptomics project (transcriptomics and histology)

Single cell RNA sequencing (scRNA-seq) emerged to characterize gene expression differences between individual cells, allowing a higher resolution look at mRNA abundance than bulk RNA- seq. However, most scRNA-seq methodologies are coupled to short read sequencing platforms that report either sparse information spread across the entire mRNA or 3’ end tags that serve to quantify gene counts. Neither reveals contiguous mature mRNA sequences and the associated isoform-specific open reading frames which often encode proteins with different functional properties. To address this, we and other groups have modified the workflows of existing single cell transcriptomic technologies to instead generate long read RNA-seq libraries.  These libraries are then profiled using PacBio’s SMRT sequencing technology on the Sequel II instrument. In the steps that follow the single cell technology, the workflow is similar to PacBio’s RNA-seq offering, Iso-Seq. Importantly, the single molecule HiFi reads are highly accurate so the required single cell information (barcode and unique molecular index) can be directly assessed in the sequencing reads. In this Labroots session, I discuss publications and results from this approach using three different single cell technologies, with a special focus on quality control metrics that we have applied to the single cell long reads using orthogonal data types. I will show that existing single cell protocols from these providers can be modified in a subtle fashion to generate matched short read and long reads sequencing libraries harboring the same single cell information.  I’ll also touch on the bioinformatic methods we are using to process and analyze the single cell data.  Visualizing some examples from this single cell data type will highlight new insights uncovered when single cells are profiled for their isoform expression.

Learning Objectives:

1. How to modify laboratory workflows from existing single cell transcriptomic platforms to obtain full-length isoform information

2. Bioinformatic methods and how these can be applied as a quality control step for long read isoform sequencing from single cells

3. How isoform-level information can uncover biological phenomena that remain invisible with short reads alone.