Global Clinical Genomics Markets Report 2021: Mergers, Acquisitions, and Partnerships Accelerate Adoption of Clinical Genomics

Dublin, April 14, 2021 (GLOBE NEWSWIRE) -- The "Clinical Genomics Report: The Interplay Between Clinical Research & Clinical Diagnostics" report has been added to ResearchAndMarkets.com's offering.

This "Clinical Genomics Report: The Interplay Between Clinical Research and Clinic Diagnostics" provides an in-depth analysis of differences in product characteristics related to data processing, analysis, knowledge extraction and reporting of findings (including the type of content integrated for meaningful extraction), and compliance and security mechanisms. Both clinical end-users and commercial companies who require insight into this expanding industry and its providers and products will benefit from the critical, investigative, and qualitative report.

This new, qualitative report provides an in-depth analysis across the complex, multi-step clinical genomics data process (which includes genomic data generation, data flow, and data warehousing), clinical genomics data solution providers, market trends, technologies impacting clinical genomics applications development, clinical genomics adoption challenges, and detailed challenges & needs analysis as identified in discussions with clinical end-users.

The report is unique, in that it is not a predictive market research report, but rather builds on data gathered from many end-user interviews combined with an extensive analysis of the clinical genomics sector.

Report Contents:

Leading medical organizations have established precision medicine programs that support personalized patient treatment. Implementation of clinical genomics applications and enterprise-wide clinical data warehouses are considered the foundation for successful genomic medicine programs. Innovative technological advancements have allowed us to sequence and uncover mutational events at an unprecedented scale while facilitating linking genomic data to high-quality clinical data and diagnosis.

Medical organizations understand the benefit of being empowered by data-driven approaches to reduce operational costs and time and to provide researchers and clinicians what is necessary to decipher critical research data and data for clinical decision making. However, technical and scientific limitations still need to be addressed for optimized and universal use of various data sources for both clinical and research purposes.

While data production is no longer a challenge, and targeted panels - and to some extent, whole-exome sequencing - are well adopted, the expected dramatic rise in whole-genome sequencing will result in unforeseeable quantities of data at the clinical level that needs to be managed, understood, and communicated. Low-cost sequencing of whole genomes at population scale is already in existence, but not yet widespread in the clinic, as many observed changes at the genome level cannot yet be fully interpreted or explain an existing phenotype.

Scalable, fully automated analysis and knowledge extraction solutions incorporating rich annotation information are necessary to overcome these challenges. With massive quantities of NGS data (linked to different clinical and other types of data), artificial intelligence and machine learning are hailed as pivotal solutions to address the data interpretation and knowledge extraction challenges and to advance the clinical application of genomics.

Despite increasing efforts and investments in implementing clinical applications and building data solutions, many organizations are still challenged with the multi-faceted complexities in transforming to become data-driven. Implementations are challenged by ineffective data sharing, scalability and automation issues, non-optimized data generation and data flow approaches and non-standardized data from numerous sources.

Implementing a complex clinical data warehouse presents many challenges starting with the various data sources it needs to support and the tools required to view the clinical information. Ingestion of data of different types and origins with relevant metadata; data transformation, standardization, and cleansing to support the needs of a diverse set of end-users in both the clinical and research settings; and varied end-users with individual needs and computation capabilities are all important considerations.

Clinical interviews detailed challenges associated with creating a workflow that incorporates a clinical data warehouse connecting clinical research with clinical diagnostics and vice versa. This important workflow leads to efficient clinical decision-making and reporting findings between clinical research and the clinic, which can optimize clinical outcomes and patient treatment.

Key Topics Covered:

1. THE CLINICAL DIAGNOSTICS & MOLECULAR PROFILING WORKFLOW

• Clinical Genomics Workflow End Users/Personas

• Mapping End Users to the Clinical Genomics Workflow

• Clinical Information Delivery

2. THE PRECISION MEDICINE VALUE CHAIN

3. LEADING MEDICAL ORGANIZATIONS PLATFORM AND INFRASTRUCTURE PREFERENCES IN SUPPORT OF THEIR CLINICAL GENOMICS WORKFLOWS

• Implementations and Choices of Molecular Profiling and Genetic Testing Processes

• Medical Organizations' Clinical Genetic Testing Labs

• Testing Lab Selection Criteria

• Platforms and Infrastructures Currently Implemented at Leading Medical Organizations

• Adoption of Commercial and Internally Developed Platforms, Tools, and Services

• Medical Organizations Platform and Infrastructure Preferences

• Profiles of Leading Medical Organizations

• Cedars-Sinai

• Emory Healthcare

• Geisinger Health Systems

• Intermountain Healthcare

• Kaiser Permanente

• Mayo Clinic

• MD Anderson Cancer Center

• Moffitt Cancer Center

• Mount Sinai Health System

• Nationwide Children's Hospital

• Partners HealthCare

• Sanford Health

• St. Jude Children's Hospital

• Vanderbilt University Medical Center

4. MEDICAL INDUSTRY CHALLENGES

• Clinical Genomics - Unmet Needs and Challenges

• Technical Challenges Associated with Scaling Clinical Genomics Applications

• Scientific Challenges Associated with the Implementation of Clinical Genomics

• Non-Technical / Scientific Challenges Associated with the Implementation of Clinical Genomics Applications

5. GENOMIC DATA GENERATION, DATA FLOW, AND DATA WAREHOUSING

• Rethinking Optimized Genomic Data Generation and Data Flow

• Structures of Successful Big Data Platforms

• Scalable Data Generation and Data Flow Lacking Data Standards

• Slow Integration of Clinical Genomics Data with Other Clinical/Patient Data

• Current EMR/EHR Systems Do Not Support Genomics Data

• Variant Data Warehousing for Data Analysis, Mining, and Querying

• Enterprise Data Platform Architecture

• Integration of Various Types of Data

• A Data Warehouse That Supports Clinical Research

• Data Warehousing and Fast Data Processing Requires a Scalable Infrastructure

• The Ideal Variant Data Analysis and Query Platform

• Data Warehouse versus a Data Lake

• Cloud Is the Preferred Infrastructure

6. CLINICAL GENOMICS DATA SOLUTIONS

• Genomic Data Infrastructures/Platforms for Data Storage, Processing, and Analysis

• Scaled Data Storage and Computing Solutions

• Clinical Genomics Data Platforms

• Integrated Genomic Workflow

• Data Processing Speed, Scalability, and Flexibility

• Clinical Data Management & Knowledge Extraction

• Clinico-Genomic Data Management and Integration

• Genomic/Variant Data Querying and Analysis

• Variant Data Interpretation/Decision Making/Reporting

• Tertiary Analysis - Sequence Data Interpretation and Insight Generation

• Tertiary Analysis - Embedded Interpretation Content for Insight Generation

• Data Processing, Knowledge Extraction, and Reporting Companies Side-by-Side

7. CLINICAL GENOMICS MARKET TRENDS

• Factors Impacting Clinical Sequencing Adoption

• Genetic Testing and Molecular Profiling Trends

• The Increasing Trend of Clinical Genetic Testing

• Clinical Genomics Applications to Be Established in the Clinic as a Routine

• Routine Genomic Applications Established over the Next Three to Five Years

• Requirements to Establish Genomic Applications as a Routine

• Mergers, Acquisitions, and Partnerships Accelerate Adoption of Clinical Genomics

• Agilent Is Building Integrated, Complete Clinical Workflow Solutions

• Illumina's M&As Suggest the Goal of a Fully Integrated Sequencing Solution

• Roche Is Expanding its Diagnostics Business

• Thermo Fisher Scientific Is Strengthening Its Presence in the Clinical Sector

• QIAGEN Is Pushing the Molecular Diagnostics and Clinical Research Markets

• Other Acquisitions and Partnerships

8. TECHNOLOGIES IMPACTING THE IMPLEMENTATION OF CLINICAL GENOMICS & MOLECULAR PROFILING APPLICATIONS

• 3rd and 4th Generation Sequencing Technology

• Artificial Intelligence/Machine Learning Applications

• AI in Healthcare Adoption Drivers

• Liquid Biopsy in Clinical Diagnostics

• Liquid Biopsy Clinical Applications

• Long-Read Sequencing Technology

• Real-World Data / Real-World Evidence

• The FDA Is Attempting to Define RWD and RWE

• An Uptick of RWD and RWE Publications

• The Benefits of Real-World Evidence

• The Challenges of Using RWD and RWE

• An Active Playing Field of Commercial RWD Players

• Clinical Genomics Adoption Challenges

9. COVID-19, THE HEALTHCARE DISRUPTER

• Diagnostics - Commercial Companies Revamping

• Diagnostics Companies Quickly Pivoted

• COVID-19, a Disrupter, but also an Opportunity

• Viral Sequencing Reveals How SARSCo-V-2 Evolves and Spreads

• COVID-19 Resulted in Unprecedented Data Sharing

• COVID-19 and the Massive Disruptions to Clinical Trials Processes

10. COMMERCIAL CLINICAL SOLUTIONS / PRODUCT PROVIDERS

• Company Profiles

• BC Platforms

• Bluebee (an Illumina company)

• Color

• Congenica

• DNAnexus

• Fabric Genomics

• Foundation Medicine

• Freenome

• Genoox

• Genuity Science

• Google Life Sciences

• GRAIL

• Helix

• Illumina

• Invitae

• PierianDx

• QIAGEN

• Seven Bridges

• SOPHiA Genetics

• Tempus

11. THE FUNDING SITUATION

12. APPENDIX

• Clinical Genomics End User Types

• The Computational Scientist/Bioinformatician

• The Data Scientist

• The Non-Computational Collaborator

• The Genetic Counselor

• The Clinical Geneticist

• The Physician

• The Patient

13. ABBREVIATIONS

For more information about this report visit https://www.researchandmarkets.com/r/hfqr98

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