AWS HealthOmics is a comprehensive suite of services offered by Amazon Web Services (AWS) designed to support the management, analysis, and integration to help bioinformaticians, researchers, and scientists manage and gain insights from large sets of genomic and biological data.

It streamlines the processes of storing, querying, and analyzing this information, supporting faster discovery and insight generation for both research and clinical applications. AWS HealthOmics aims to facilitate breakthroughs in these areas by providing scalable, secure, and efficient Cloud-based solutions, and is composed of three core elements:

• HealthOmics Storage: Enables efficient, scalable storage and sharing of petabyte-scale genomic datasets at a reduced cost.
• HealthOmics Analytics: Simplifies the preparation of genomic data for complex multi-omics and multimodal analyses.
• HealthOmics Workflows: Automates the setup and scaling of the computational infrastructure needed for bioinformatics processes.

AWS HealthOmics includes features designed to unlock the full potential of genomic and biological data, with the following benefits aligned to AWS HealthOmics’ informational page. It securely combines the multi-omics data of individuals with their medical history to facilitate more personalized care. It uses purpose-built data stores to support large-scale analysis and collaborative research across populations. It accelerates science and medicine with Ready2Run workflows or the ability to bring your own private bioinformatics workflows. Additionally, it protects patient privacy with HIPAA eligibility and built-in data access and logging.

 Below are some of the key technical features of AWS HealthOmics:

1. Scalable Data Storage and Management:
   • AWS S3 (Simple Storage Service): AWS S3 provides a durable and highly available storage solution for massive omics datasets. It supports data storage in various formats and allows easy retrieval and management.
   • AWS Glacier: For long-term archival storage, AWS Glacier offers a cost-effective solution for storing large volumes of omics data that are infrequently accessed but need to be preserved.
2. High-Performance Computing (HPC):
   • EC2 Instances: AWS EC2 instances with powerful CPU and GPU options enable the execution of computationally intensive tasks such as sequence alignment, variant calling, and structural biology simulations.
   • AWS Batch: AWS Batch simplifies the execution and scaling of batch processing jobs, automating the provisioning and management of the necessary compute resources.
3. Data Integration and Analytics:
   • AWS Glue: AWS Glue is a managed ETL (extract, transform, load) service that makes it easy to prepare and transform omics data for analysis.
   • Amazon Redshift: Amazon Redshift allows for the efficient querying and analysis of large-scale datasets, supporting complex analytical workflows.
   • AWS Lambda: AWS Lambda enables code execution in response to triggers, facilitating real-time data processing and integration workflows.
4. Machine Learning and AI:
   • Amazon SageMaker: Amazon SageMaker provides a fully managed environment for building, training, and deploying machine learning models, enabling advanced analyses such as predictive modeling and personalized medicine.
   • AWS Deep Learning AMIs: Preconfigured Amazon Machine Images (AMIs) for deep learning provide the tools and frameworks needed to develop and deploy deep learning models on AWS.
5. Data Security and Compliance:
   • AWS Identity and Access Management (IAM): AWS IAM allows for the secure management of access to AWS resources, ensuring that only authorized users can access sensitive data.
   • AWS Key Management Service (KMS): AWS KMS provides encryption key management, ensuring that omics data is securely encrypted at rest and in transit.
   • Compliance: AWS HealthOmics complies with various regulatory standards, including HIPAA, GDPR, and GxP, ensuring that Life Sciences data is handled per industry regulations.
6. Collaborative Research and Data Sharing:
   • AWS Data Exchange: AWS Data Exchange simplifies the process of finding, subscribing to, and using third-party data in the Cloud, facilitating collaboration and data sharing among researchers and institutions.
   • Amazon WorkSpaces: Amazon WorkSpaces provides secure and scalable virtual desktops, enabling researchers to access and analyze omics data from anywhere.

Below are some of the noteworthy benefits of AWS HealthOmics for Life Sciences teams:

1. Scalability:
   • AWS HealthOmics provides on-demand scalability, allowing organizations to handle massive amounts of omics data without significant upfront infrastructure investment.
2. Cost Efficiency:
   • With pay-as-you-go pricing and various cost-optimization tools, AWS HealthOmics ensures that organizations can manage their budgets effectively while leveraging advanced computational resources.
3. Accelerated Research:
   • By leveraging the high-performance computing capabilities and machine learning tools offered by AWS, researchers can accelerate the pace of discovery and innovation in fields such as genomics, proteomics, and precision medicine.
4. Enhanced Collaboration:
   • AWS HealthOmics facilitates data sharing and collaborative research, enabling scientists and clinicians to work together more effectively to advance healthcare outcomes.
5. Improved Data Security:
   • AWS’s robust security framework sensitive omics data, meeting the stringent requirements of Life Sciences.

AWS HealthOmics represents a significant advancement in the management and analysis of omics data, providing a powerful and flexible Cloud-based solution for Life Sciences organizations. By leveraging the comprehensive services offered by AWS, researchers and clinicians can overcome the challenges associated with large-scale omics data, driving innovation and improving patient outcomes. Whether for genomics, proteomics, or any other omics field, AWS HealthOmics offers the tools and infrastructure needed to unlock the full potential of omics research.

As an AWS Advanced Tier Service Partner, RCH Solutions is the premier partner to help Life Sciences organizations leverage AWS HealthOmics and fully optimize entire AWS environments. With over three decades of experience exclusively in the Life Sciences sector, we’ve supported 7 of the top 10 global pharmaceutical companies and more than 50 start-ups and mid-size Life Sciences teams across all stages of development and maturity. Currently finalizing our distinguished AWS Life Sciences Competency designation, our expertise ensures we deliver cutting-edge solutions tailored to the specific needs of the Life Sciences.

“Jupyter Notebooks have changed the narrative on how Scientists leverage code to approach data, offering a clean and direct paradigm for developing and testing modular code without the complications of more traditional IDEs.”

These versatile tools offer an interactive environment that combines code execution, data visualization, and narrative text, making it easier to share insights and collaborate effectively. To make the most of Jupyter Notebooks, it is essential to follow best practices and optimize workflows. Here’s a comprehensive guide to help you master your use of Jupyter Notebooks. 

Getting Started: Know-Hows 

1. Installation and Setup: 

• Anaconda Distribution: One of the easiest ways to install Jupyter Notebooks is through the Anaconda Distribution. It comes pre-installed with Jupyter and many useful data science libraries. 

• JupyterLab: For an enhanced experience, consider using JupyterLab, which offers a more robust interface and additional functionalities. 

1. Basic Operations: 

• Creating a Notebook: Start by creating a new notebook. You can select the desired kernel (e.g., Python, R, Julia) based on your project needs. 

• Notebook Structure: Use markdown cells for explanations and code cells for executable code. This separation helps in documenting the thought process and code logic clearly. 

1. Extensions and Add-ons: 

• Jupyter Nbextensions: Enhance the functionality of Jupyter Notebooks by using Nbextensions, which offer features like code folding, table of contents, and variable inspector.

Best Practices 

1. Organized and Readable Notebooks: 

• Use Clear Titles and Headings: Divide your notebook into sections with clear titles and headings using markdown. This makes the notebook easier to navigate. 

• Comments and Descriptions: Add comments in your code cells and descriptions in markdown cells to explain the logic and purpose of the code. 

1. Efficient Code Management: 

• Modular Code: Break down your code into reusable functions and modules. This not only keeps your notebook clean but also makes debugging easier. 

• Version Control: Use version control systems like Git to keep track of changes and collaborate with others efficiently. 

1. Data Handling and Visualization: 

• Pandas for Data Manipulation: Utilize the powerful Pandas library for data manipulation and analysis. Ensure to handle missing data appropriately and clean your dataset before analysis. 

• Matplotlib and Seaborn for Visualization: Use libraries like Matplotlib and Seaborn for creating informative and visually appealing plots. Always label your axes and provide legends. 

1. Performance Optimization: 

• Efficient Data Loading: Load data efficiently by reading only the necessary columns and using appropriate data types. 
• Profiling and Benchmarking: Use tools like line_profiler and memory_profiler to identify bottlenecks in your code and optimize performance. 

Optimizing Outcomes 

1. Interactive Widgets: 

• IPyWidgets: Enhance interactivity in your notebooks using IPyWidgets. These widgets allow users to interact with the data and visualizations, making the notebook more dynamic and user-friendly. 

1. Sharing and Collaboration: 

• NBViewer: Share your Jupyter Notebooks with others using NBViewer, which renders notebooks directly from GitHub. 

• JupyterHub: For collaborative projects, consider using JupyterHub, which allows multiple users to work on notebooks simultaneously. 

1. Documentation and Presentation: 

• Narrative Structure: Structure your notebook as a narrative, guiding the reader through your thought process, analysis, and conclusions. 

• Exporting Options: Export your notebook to various formats like HTML, PDF, or slides for presentations and reports. 

1. Reproducibility: 

• Environment Management: Use tools like Conda or virtual environments to manage dependencies and ensure that your notebook runs consistently across different systems. 
• Notebook Extensions: Utilize extensions like nbdime for diffing and merging notebooks, ensuring that collaborative changes are tracked and managed efficiently. 

Jupyter Notebooks can be a powerful tool that can significantly enhance your data science and research workflows. By following the best practices and optimizing your use of notebooks, you can create organized, efficient, and reproducible projects. Whether you’re analyzing data, developing machine learning models, or sharing insights with your team, Jupyter Notebooks provide a versatile platform to achieve your goals.  

How Can RCH Solutions Enhance Your Team’s Jupyter Notebook Experience & Outcomes?

RCH can efficiently deploy and administer Notebooks to free up the customer teams to focus on code/algorithms/data. Additionally, our team can add logic in the Public Cloud to shutdown Notebooks (and other Dev type resources) when not in use to ensure cost control and optimization—and more. Our team is committed to helping Biopharma organizations leverage both proven and cutting-edge technologies to achieve goals. Contact RCH today to learn more about support for success with Jupyter Notebooks and beyond. 

In the rapidly evolving Life Sciences landscape, leveraging advanced tools and technologies is crucial for BioPharmas to stay competitive and drive innovation. The Posit Suite’s powerful components—Workbench, Connect, and Package Manager—offer a comprehensive platform to significantly enable data analysis, collaboration, and package management capabilities.

Understanding The Posit Suite

The Posit Suite comprises three core components:

1. Workbench: An integrated development environment (IDE) tailored for data scientists and analysts, providing robust tools for coding, debugging, and visualization.
2. Connect: A platform for deploying, sharing, and managing data products, such as interactive applications, reports, and APIs.
3. Package Manager: A repository and management tool for R and Python packages, ensuring secure and reproducible environments.

Insights and Best Practices for The Posit Suite

1. Optimizing Workbench for Advanced Analytics

The Workbench is the heart of The Posit Suite, where data scientists and analysts spend most of their time. To maximize its potential:

• Leverage Integrated Tools: Utilize built-in features such as code completion, syntax highlighting, and version control to streamline workflows. The integrated Git support ensures seamless collaboration and tracking of code changes.
• Utilize Extensions: Enhance Workbench with extensions tailored to specific needs. Extensions can significantly boost productivity via additional language support or custom themes.
• Data Connectivity: Establish direct connections to databases and data sources within Workbench. This minimizes the need for external tools and enables real-time data access and manipulation.

2. Enhancing Collaboration with Connect

Connect is designed to bridge the gap between data creation and consumption. Here’s how to make the most of it:

• Interactive Dashboards and Reports: Deploy interactive dashboards and reports with which stakeholders can easily access and interact. Shiny and R Markdown are powerful tools that integrate seamlessly with Connect.
• Automated Reporting: Schedule and automate report generation and distribution to ensure timely delivery of critical insights without manual intervention.
• Secure Sharing: Utilize Connect’s robust security features to control access to data products. Role-based access control and single sign-on (SSO) integration ensure that only authorized users can access sensitive information.

3. Streamlining Package Management with Package Manager

Managing packages and dependencies is a critical aspect of reproducible research and development. The Package Manager simplifies this process:

• Centralized Repository: Maintain a centralized repository of approved packages to ensure organization consistency and compliance. This reduces the risk of dependency conflicts and ensures all team members use vetted packages.
• Snapshot Management: Use snapshots to freeze package versions at specific points in time, ensuring that analyses and models remain reproducible and stable over time.
• Private Package Repositories: Host private packages and custom tools within an organization. This allows one to leverage internal resources and share them securely across teams.

Tips for Maximizing the Posit Suite in Biopharma

1. Integration with Existing Workflows

Integrate The Posit Suite with existing workflows and systems. Whether connecting to a Laboratory Information Management System (LIMS) or integrating with cloud infrastructure, seamless integration enhances efficiency and reduces the learning curve.

2. Training and Support

Invest in training and support for teams. Familiarize users with the suite’s features and best practices. Partnering with experts like RCH Solutions can provide invaluable guidance and troubleshooting.

3. Regular Updates and Maintenance

Stay current with the latest updates and features of The Posit Suite. Regularly updating tools ensures access to the latest advancements and security patches.

Conclusion

The Posit Suite offers biopharma organizations a powerful and versatile platform to enhance their data analysis, collaboration, and package management capabilities. By optimizing Workbench, Connect, and Package Manager and following best practices and tips, one can unlock the full potential of The Posit Suite, driving innovation and efficiency in organizations.

At RCH Solutions, the team is committed to helping Biopharma organizations leverage both proven and cutting-edge technologies to achieve goals. Contact RCH today to learn more about support for success with The Posit Suite and beyond.

Life Sciences organizations engaged in drug discovery, development, and commercialization grapple with intricate challenges. The quest for novel therapeutics demands extensive research, vast datasets, and the integration of multifaceted processes. Managing and analyzing this wealth of data, ensuring compliance with stringent regulations, and streamlining collaboration across global teams are hurdles that demand innovative solutions.

Moreover, the timeline from initial discovery to commercialization is often lengthy, consuming precious time and resources. To overcome these challenges and stay competitive, Life Sciences organizations must harness cutting-edge technologies, optimize data workflows, and maintain compliance without compromise.

Amid these complexities, Amazon Web Services (AWS) emerges as a game-changing ally. AWS’s industry-leading cloud platform includes  specialized services tailored to the unique needs of Life Sciences and empowers organizations to:

1. Accelerate Research: AWS’s scalable infrastructure facilitates high-performance computing (HPC), enabling faster data analysis, molecular modeling, and genomics research. This acceleration is pivotal in expediting drug discovery.
2. Enhance Data Management: With AWS, Life Sciences organizations can store, process, and analyze massive datasets securely. AWS’s data management solutions ensure data integrity, compliance, and accessibility.
3. Optimize Collaboration: AWS provides the tools and environment for seamless collaboration among dispersed research teams. Researchers can collaborate in real time, enhancing efficiency and innovation.
4. Ensure Security and Compliance: AWS offers robust security measures and compliance certifications specific to the Life Sciences industry, ensuring that sensitive data is protected and regulatory requirements are met.

While AWS holds immense potential, realizing its benefits requires expertise. This is where a trusted AWS partner becomes invaluable. An experienced partner not only understands the intricacies of AWS but also comprehends the unique challenges Life Sciences organizations face.

Partnering with a trusted AWS expert offers:

• Strategic Guidance: A seasoned partner can tailor AWS solutions to align with the Life Sciences sector’s specific goals and regulatory constraints, ensuring a seamless fit.
• Efficient Implementation: AWS experts can expedite the deployment of Cloud solutions, minimizing downtime and maximizing productivity.
• Ongoing Support: Beyond implementation, a trusted partner offers continuous support, ensuring that AWS solutions evolve with the organization’s needs.
• Compliance Assurance: With deep knowledge of industry regulations, a trusted partner can help navigate the compliance landscape, reducing risk and ensuring adherence.

Certified AWS engineers bring transformative expertise to cloud strategy and data architecture, propelling organizations toward unprecedented success. 

AWS Certifications: What They Mean for Organizations

AWS offers a comprehensive suite of globally recognized certifications, each representing a distinct level of proficiency in managing AWS Cloud technologies. These certifications are not just badges; they signify a commitment to excellence and a deep understanding of Cloud infrastructure.

In fact, studies show that professionals who pursue AWS certification are faster, more productive troubleshooters than non-certified employees. For research and development IT teams, the AWS certifications held by their members translate into powerful advantages. These certifications unlock the ability to harness AWS’s cloud capabilities for driving innovation, efficiency, and cost-effectiveness in data-driven processes.

Meet RCH’s Certified AWS Experts: Your Key to Advanced Proficiency

At RCH, we’re proud to prioritize professional and technical skill development across our team, and proudly recognize our AWS-certified professionals:

• Mohammad Taaha, AWS Solutions Architect Professional
• Yogesh Phulke, AWS Solutions Architect Professional
• Michael Moore, AWS DevOps Engineering Professional
• Abdul Samad, AWS Solutions Architect Associate
• Baris Bilgin, AWS Solutions Architect Associate
• Isaac Adanyeguh, AWS Solutions Architect Associate
• Matthew Jaeger, AWS Cloud Practitioner & SysOps Administrator
• Lyndsay Frank, AWS Cloud Practitioner
• Dennis Runner, AWS Cloud Practitioner
• Burcu Dikeç, AWS Cloud Practitioner

When you partner with RCH and our AWS-certified experts, you gain access to technical knowledge and tap into a wealth of experience, innovation, and problem-solving capabilities. Advanced proficiency in AWS certifications means that our team can tackle even the most complex Cloud challenges with confidence and precision.

Our certified AWS experts don’t just deploy Cloud solutions; they architect them with your unique business needs in mind. They optimize for efficiency, scalability, and cost-effectiveness, ensuring your Cloud strategy aligns seamlessly with your organizational goals, including many of the following needs:

• Creating extensive solutions for AWS EC2 with multiple frameworks (EBS, ELB, SSL, Security Groups and IAM), as well as RDS, CloudFormation, Route 53, CloudWatch, CloudFront, CloudTrail, S3, Glue, and Direct Connect.
• Deploying high-performance computing (HPC) clusters on AWS using Parallel Cluster running the SGE scheduler
• Automating operational tasks, including software configuration, server scaling and deployments, and database setups in multiple AWS Cloud environments using modern application and configuration management tools (e.g., CloudFormation and Ansible).
• Working closely with clients to design networks, systems, and storage environments that effectively reflect their business needs, security, and service level requirements.
• Architecting and migrating data from on-premises solutions (Isilon) to AWS (S3 & Glacier) using industry-standard tools (Storage Gateway, Snowball, CLI tools, Datasync, among others).
• Designing and deploying plans to remediate accounts affected by IP overlap 

All of these tasks have boosted the efficiency of data-oriented processes for clients and made them better able to capitalize on new technologies and workflows.

The Value of Working with AWS Certified Partners 

In an era where data and technology are the cornerstones of success, working with a partner who embodies advanced proficiency in AWS is not just a strategic choice—it’s a game-changing move. At RCH Solutions, we leverage the power of AWS certifications to propel your organization toward unparalleled success in the cloud landscape.

Learn how RCH can support your Cloud strategy, or CloudOps needs today. 

Discover the differences between the two and pave the way toward improved efficiency.

Life sciences organizations process more data than the average company—and need to do so as quickly as possible. As the world becomes more digital, technology has given rise to two popular computing models: Cloud computing and edge computing. Both of these technologies have their unique strengths and weaknesses, and understanding the difference between them is crucial for optimizing your science IT infrastructure now and into the future.  

The Basics

Cloud computing refers to a model of delivering on-demand computing resources over the internet. The Cloud allows users to access data, applications, and services from anywhere in the world without expensive hardware or software investments. 

Edge computing, on the other hand, involves processing data at or near its source instead of sending it back to a centralized location, such as a Cloud server.

Now, let’s explore the differences between Cloud vs. edge computing as they apply to Life Sciences and how to use these learnings to formulate and better inform your computing strategy.

Performance and Speed

One of the major advantages of edge computing over Cloud computing is speed. With edge computing, data processing occurs locally on devices rather than being sent to remote servers for processing. This reduces latency issues significantly, as data doesn’t have to travel back and forth between devices and Cloud servers. The time taken to analyze critical data is quicker with edge computing since it occurs at or near its source without having to wait for it to be transmitted over distances. This can be critical in applications like real-time monitoring, autonomous vehicles, or robotics.

Cloud computing, on the other hand, offers greater processing power and scalability, which can be beneficial for large-scale data analysis and processing.  By providing on-demand access to shared resources, Cloud computing offers organizations greater processing power, scalability, and flexibility to run their applications and services. Cloud platforms offer virtually unlimited storage space and processing capabilities that can be easily scaled up or down based on demand. Businesses can run complex applications with high computing requirements without having to invest in expensive hardware or infrastructure. Also worth noting is that Cloud providers offer a range of tools and services for managing data storage, security, and analytics at scale—something edge devices cannot match.

Security and Privacy

With edge computing, there could be a greater risk of data loss if damage were to occur to local servers. Data loss is naturally less of a threat with Cloud storage, but there is a greater possibility of cybersecurity threats in the Cloud. Cloud computing is also under heavier scrutiny when it comes to collecting personal identifying information, such as patient data from clinical trials.

A top priority for security in both edge and Cloud computing is to protect sensitive information from unauthorized access or disclosure. One way to do this is to implement strong encryption techniques that ensure data is only accessible by authorized users. Role-based permissions and multi-factor authentication create strict access control measures, plus they can help achieve compliance with relevant regulations, such as GDPR or HIPAA. 

Organizations should carefully consider their specific use cases and implement appropriate security and privacy controls, regardless of their elected computing strategy.

Scalability and Flexibility

Scalability and flexibility are both critical considerations in relation to an organization’s short and long-term discovery goals and objectives.

The scalability of Cloud computing has been well documented. Data capacity can easily be scaled up or down on demand, depending on business needs. Organizations can quickly scale horizontally too, as adding new devices or resources as you grow takes very little configuration and leverages existing Cloud capacities.

While edge devices are becoming increasingly powerful, they still have limitations in terms of memory and processing power. Certain applications may struggle to run efficiently on edge devices, particularly those that require complex algorithms or high-speed data transfer.

Another challenge with scaling up edge computing is ensuring efficient communication between devices. As more and more devices are added to an edge network, it becomes increasingly difficult to manage traffic flow and ensure that each device receives the information it needs in a timely manner.

Cost-Effectiveness

Both edge and Cloud computing have unique cost management challenges—and opportunities— that require different approaches.

Edge computing can be cost-effective, particularly for environments where high-speed internet is unreliable or unavailable. Edge computing cost management requires careful planning and optimization of resources, including hardware, software, device and network maintenance, and network connectivity.

In general, it’s less expensive to set up a Cloud-based environment, especially for firms with multiple offices or locations. This way, all locations can share the same resources instead of setting up individual on-premise computing environments. However, Cloud computing requires careful and effective management of infrastructure costs, such as computing, storage, and network resources to maintain speed and uptime.

Decision Time: Edge Computing or Cloud Computing for Life Sciences?

Both Cloud and edge computing offer powerful, speedy options for Life Sciences, along with the capacity to process high volumes of data without losing productivity. Edge computing may hold an advantage over the Cloud in terms of speed and power since data doesn’t have to travel far, but the cost savings that come with the Cloud can help organizations do more with their resources.

As far as choosing a solution, it’s not always a matter of one being better than the other. Rather, it’s about leveraging the best qualities of each for an optimized environment, based on your firm’s unique short- and long-term goals and objectives. So, if you’re ready to review your current computing infrastructure or prepare for a transition, and need support from a specialized team of edge and Cloud computing experts, get in touch with our team today.

About RCH Solutions

RCH Solutions supports Global, Startup, and Emerging Biotech and Pharma organizations with edge and Cloud computing solutions that uniquely align to discovery goals and business objectives. 

Sources:

https://aws.amazon.com/what-is-cloud-computing/

https://www.ibm.com/topics/cloud-computing

https://www.ibm.com/cloud/what-is-edge-computing

https://www.techtarget.com/searchdatacenter/definition/edge-computing?Offer=abMeterCharCount_var1

https://thenewstack.io/edge-computing/edge-computing-vs-cloud-computing/

High-Performance Computing (HPC) has long been an incredible accelerant in the race to discover and develop novel drugs and therapies for both new and well-known diseases. And a HPC migration to the Cloud might be your next step to maintain or grow your organization’s competitive advantage.

Whether it’s a full HPC migration to the Cloud or a uniquely architected hybrid approach, evolving your HPC ecosystem to the Cloud brings critical advantages and benefits including:

• Flexibility and scalability
• Optimized costs
• Enhanced security
• Compliance
• Backup, recovery, and failover
• Simplified management and monitoring

And with incredibly careful planning, strategic design, effective implementation and with the right support, the capabilities and accelerated outcomes of migrating your HPC systems to the Cloud can lead to truly accelerated breakthroughs and drug discovery.

But with this level of promise and performance, comes challenges and caveats that require strategic consideration throughout all phases of your supercomputing and HPC development, migration and management.

So, before you commence your HPC Migration from on-premise data centers or traditional HPC clusters to the Cloud, here are some key considerations to keep in mind throughout your planning phase.

1. Assess & Understand Your Legacy HPC Environment

Building a comprehensive migration plan and strategy from inception is necessary for optimization and sustainable outcomes. A proper assessment includes an evaluation of the current state of your legacy hardware, software, and the data resources available for use, as well as the system’s capabilities, reliability, scalability, and flexibility, prioritizing security and maintenance of the system.

Gaining a deep and thorough understanding of your current infrastructure and computing environment will help identify technical constraints or bottlenecks that exist, and inform the order that might be necessary for migration. And that level of insight can streamline and circumvent major, arguably avoidable, hurdles that your organization might face.

2. Determine the Right Cloud Provider and Tooling

Determining the right HPC Cloud provider for your organization can be a complex process, but an irrefutable critical one. In fact, your entire computing environment depends on it. It involves researching the available options, comparing features and services, and evaluating cost, reputation and performance.

Amazon Web Service, Microsoft Azure, and Google Cloud – to name just the three biggest – offer storage and Cloud computing services that drive accelerated innovation for companies by offering fast networking and virtually unlimited infrastructure to store and manage massive data sets the computing power required to analyze it. Ultimately, many vendors offer different types of cloud infrastructure that run large, complex simulations and deep learning workloads in the cloud, and it is important to first select the one that best meets the needs of your unique HPC workloads between public cloud, private cloud, or hybrid cloud infrastructure.

3. Plan for the Right Design & Deployment

In order to effectively plan for a HPC Migration in the Cloud, it is important to clearly define the objectives, determine the requirements and constraints, identify the expected outcomes, and a timeline for the project.

From a more technical perspective, it is important to consider the application’s specific requirements and the inherent capabilities including storage requirements, memory capacity, and other components that may be needed to run the application. If a workload requires a particular operating system, for example, then it should be chosen accordingly.

Finally, it is important to understand the networking and security requirements of the application before working through the design, and definitely the deployment phase, of your HPC Migration.

The HPC Migration Journey Begins Here…

By properly considering all of these factors, it is possible to effectively plan for your organization’s HPC migration and its ability to leverage the power of supercomputing in drug discovery.

Assuming your plan is comprehensive, effective and sustainable, implementing your HPC migration plan is ultimately still a massive undertaking, particularly for research IT teams likely already overstretched or for an existing Bio-IT vendor lacking specialized knowledge and skills.

So, if your team is ready to take the leap and begin your HPC migration, get in touch with our team today.

The Next Phase of Your HPC Migration in the Cloud

A HPC migration to the Cloud can be an incredibly complex process, but with strategic planning and design, effective implementation and with the right support, your team will be well on their way to sustainable success. Click below and get in touch with our team to learn more about our comprehensive HPC Migration services that support all phases of your HPC migration journey, regardless of which stage you are in.

GET IN TOUCH

Learn the key considerations for evaluating and selecting the right application for your Cloud-environment.  

Good software means faster work for drug research and development, particularly concerning proteins. Proteins serve as the basis for many treatments, and learning more about their structures can accelerate the development of new treatments and medications. 

With more software now infusing an artificial intelligence element, researchers expect to significantly streamline their work and revolutionize the drug industry. When it comes to protein folding software, two names have become industry frontrunners: AlphaFold and Openfold. 

Learn the differences between the two programs, including insights into how RCH is supporting and informing our customers about the strategic benefits the AlphaFold and Openfold applications can offer based on their environment, priorities and objectives.

About AlphaFold2

Developed by DeepMind and EMBL’s European Bioinformatics Institute, AlphaFold2 uses AI technology to predict a protein’s 3D structure based on its amino acid sequence. Its structure database is hosted on Google Cloud Storage and is free to access and use. 

The newer model, AlphaFold 2, won the CASP competition in November 2020, having achieved more accurate results than any other entry. AlphaFold2 scored above 90 for more than two-thirds of the proteins in CASP’s global distance test, which measures whether the computational-predicted structure mirrors the lab-determined structure.  

To date, there are more than 200 million known proteins, each one with a unique 3D shape. AlphaFold2 aims to simplify the once-time-consuming and expensive process of modeling these proteins. Its speed and accuracy are accelerating research and development in nearly every area of biology. By doing so, scientists will be better able to tackle diseases, discover new medicines and cures, and understand more about life itself.

Exploring Openfold Protein Folding Software

Another player in the protein software space, Openfold, is PyTorch’s reproduction of Deepmind’s AlphaFold. Founded by three Seattle biotech companies (Cyrus Biotechnology, Outpace Bio, and Arzeda), the team aims to support open-source development in the protein folding software space, which is registered on AWS. The project is part of the nonprofit organization Open Molecular Software Foundation and has received support from the AWS Open Data Sponsorship Program.

Despite being more of a newcomer to the scene, Openfold is quickly turning heads with its open source model and more “completeness” compared to AlphaFold. In fact, it has been billed as a faster and more powerful version than its predecessor. 

Like AlphaFold, Openfold is designed to streamline the process of discovering how proteins fold in and around on themselves, but possibly at a higher rate and more comprehensively than its predecessor. The model has undergone more than 100,000 hours of training on NVIDIA A100 Tensor Core GPUs, with the first 3,000 hours boasting 90%+ final accuracy.

AlphaFold vs. Openfold: Our Perspective

Despite Openfold being a reproduction of AlphaFold, there are several key differences between the two.  

AlphaFold2 and Openfold boast similar accuracy ratings, but Openfold may have a slight advantage. Openfold’s interface is also about twice as fast as that of AlphaFold when modeling short proteins. For long protein strands, the speed advantage is minimal. 

Openfold’s optimized memory usage allows it to handle much longer protein sequences—up to 4,600 residues on a single 40GB A100.

One of the clearest differences between AlphaFold2 and Openfold is that Openfold is trainable. This makes it valuable for our customers in niche or specialized research, a capability that AlphaFold lacks.

Key Use Cases from Our Customers

Both AlphaFold and Openfold have offered  game-changing functionality for our customers’ drug research and development. That’s why many of the organization’s we’ve supported haveeven considered a hybrid approach rather than making an either/or decision.

Both protein folding software can be deployed across a variety of use cases, including:

New Drug Discovery

The speed and accuracy with which protein folding software can model protein strands make it a powerful tool in new drug development, particularly for diseases that have largely been neglected. These illnesses often disproportionately affect individuals in developing countries. Examples include parasitic diseases, such as Chagas disease or leishmaniasis. 

Combating Antibiotic Resistance

As the usage of antibiotics continues to rise, so does the risk of individuals developing antibiotic resistance. Previous data from the CDC shows that nearly one in three prescriptions for antibiotics is unnecessary. It’s estimated that antibiotic resistance costs the U.S. economy nearly $55 billion every year in healthcare and productivity losses.

What’s more, when people become resistant to antibiotics, it leaves the door wide open for the creation of “superbugs.” Since these bugs cannot be killed with typical antibiotics, illnesses can become more severe.

Professionals from the University of Colorado, Boulder, are putting AlphaFold to the test in learning more about proteins involved in antibiotic resistance. The protein folding software is helping researchers identify protein structures that they could confirm via crystallography.

Vaccine Development

Learning more about protein structures is proving useful in developing new vaccines, such as a multi-agency collaboration on a new malaria vaccine. The WHO endorsed the first malaria vaccine in 2021. However, researchers at the University of Oxford and the National Institute of Allergy and Infectious Diseases are working together to create a more effective version that better prevents transmission.

Using AlphaFold and crystallography, the two agencies identified the first complete structure of the protein Pfs48/45. This breakthrough could pave the way for future vaccine developments.

Learning More About Genetic Variations

Genetics has long fascinated scientists and may hold the key to learning more about general health, predisposition to diseases, and other traits. A professor at ETH Zurich is using AlphaFold to learn more about how a person’s health may change over time or what traits they will exhibit based on specific mutations in their DNA.

AlphaFold has proven useful in reviewing proteins in different species over time, though the accuracy diminishes the further back in time the proteins are reviewed. Seeing how proteins evolve over time can help researchers predict how a person’s traits might change in the future.

How RCH Solutions Can Help

Selecting protein folding software for your research facility is easier with a trusted partner like RCH solutions. Not only can we inform the selection process, but we also provide support in implementing new solutions. We’ll work with you to uncover your greatest needs and priorities and align the selection process with your end goals with budget in mind.

Contact us to learn how RCH Solutions can help.

Sources:

https://www.nature.com/articles/d41586-022-00997-5

https://www.deepmind.com/research/highlighted-research/alphafold

https://alphafold.ebi.ac.uk/

https://wandb.ai/telidavies/ml-news/reports/OpenFold-A-PyTorch-Reproduction-Of-DeepMind-s-AlphaFold–VmlldzoyMjE3MjI5

https://www.drugdiscoverytrends.com/7-ways-deepmind-alphafold-used-life-sciences/

https://www.cdc.gov/media/releases/2016/p0503-unnecessary-prescriptions.html

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929930/

In Life Sciences, and medical fields in particular, there is a premium on expertise and the role of a specialist. When it comes to scientists, researchers, and doctors, even a single high-performer who brings advanced knowledge in their field often contributes more value than a few average generalists who may only have peripheral knowledge. Despite this premium placed on specialization or top-talent as an industry norm, many life science organizations don’t always follow the same measure when sourcing vendors or partners, particularly those in the IT space. 

And that’s a mis-step. Here’s why.

Why “A” Talent Matters

I’ve seen far too many organizations that had, or still have, the above strategy, and also many that focus on acquiring and retaining top talent. The difference? The former experienced slow adoption which stalled outcomes which often had major impacts to their short and long term objectives. The latter propelled their outcomes out of the gates, circumventing cripping mistakes along the way. For this reason and more, I’m a big believer in attracting and retaining only “A” talent. The best talent and the top performers (Quality) will always outshine and out deliver a bunch of average ones. Most often, those individuals are inherently motivated and engaged, and when put in an environment where their skills are both nurtured and challenged, they thrive.  

Why Expertise Prevails

While low-cost IT service providers with deep rosters may similarly be able to throw a greater number of people at problems, than their smaller, boutique counterparts, often the outcome is simply more people and more problems.  Instead, life science teams should aim to follow their R&D talent acquisition processes and focus on value and what it will take to achieve the best outcomes in this space. Most often, it’s not about quantity of support/advice/execution resources—but about quality.

Why Our Customers Choose RCH

Our customers are like minded and also employ top talent, which is why they value RCH—we consistently service them with the best. While some organizations feel that throwing bodies (Quantity) at a problem is one answer, often one for optics, RCH does not. We never have. Sometimes you can get by with a generalist, however, in our industry, we have found that our customers require and deserve specialists. The outcomes are more successful. The results are what they seek— Seamless transformation.  

In most cases, we are engaged with a customer who has employed the services of a very large professional services or system integration firm. Increasingly, those customers are turning to RCH to deliver on projects typically reserved for those large, expensive, process-laden companies.  The reason is simple. There is much to be said for a focused, agile and proven company.  

Why Many Firms Don’t Restrategize

So why do organizations continue to complain but rely on companies such as these? The answer has become clear—risk aversion. But the outcomes of that reliance are typically just increased costs, missed deadlines or major strategic adjustments later on – or all of the above. But why not choose an alternative strategy from inception? I’m not suggesting turning over all business to a smaller organization. But, how about a few? How about those that require proven focus, expertise and the track record of delivery? I wrote a piece last year on the risk of mistaking “static for safe,” and stifling innovation in the process. The message still holds true. 

We all know that scientific research is well on its way to becoming, if not already, a multi-disciplinary, highly technical process that requires diverse and cross functional teams to work together in new ways. Engaging a quality Scientific Computing partner that matches that expertise with only “A” talent, with the specialized skills, service model and experience to meet research needs can be a difference-maker in the success of a firm’s research initiatives. 

My take? Quality trumps quantity—always in all ways. Choose a scientific computing partner whose services reflect the specialized IT needs of your scientific initiatives and can deliver robust, consistent results. Get in touch with me below to learn more. 

Data science has earned a prominent place on the front lines of precision medicine – the ability to target treatments to the specific physiological makeup of an individual’s disease. As cloud computing services and open-source big data have accelerated the digital transformation, small, agile research labs all over the world can engage in development of new drug therapies and other innovations.

Previously, the necessary open-source databases and high-throughput sequencing technologies were accessible only by large research centers with the necessary processing power. In the evolving big data landscape, startup and emerging biopharma organizations have a unique opportunity to make valuable discoveries in this space. 

The drive for real-world data

Through big data, researchers can connect with previously untold volumes of biological data. They can harness the processing power to manage and analyze this information to detect disease markers and otherwise understand how we can develop treatments targeted to the individual patient. Genomic data alone will likely exceed 40 exabytes by 2025 according to 2015 projections published by the Public Library of Science journal Biology. As data volume increases, its accessibility to emerging researchers improves as the cost of big data technologies decreases. 

A recent report from Accenture highlights the importance of big data in downstream medicine, specifically oncology. Among surveyed oncologists, 65% said they want to work with pharmaceutical reps who can fluently discuss real-world data, while 51% said they expect they will need to do so in the future. 

The application of artificial intelligence in precision medicine relies on massive databases the software can process and analyze to predict future occurrences. With AI, your teams can quickly assess the validity of data and connect with decision support software that can guide the next research phase. You can find links and trends in voluminous data sets that wouldn’t necessarily be evident in smaller studies. 

Applications of precision medicine

Among the oncologists Accenture surveyed, the most common applications for precision medicine included matching drug therapies to patients’ gene alterations, gene sequencing, liquid biopsy, and clinical decision support. In one example of the power of big data for personalized care, the Cleveland Clinic Brain Study is reviewing two decades of brain data from 200,000 healthy individuals to look for biomarkers that could potentially aid in prevention and treatment. 

AI is also used to create new designs for clinical trials. These programs can identify possible study participants who have a specific gene mutation or meet other granular criteria much faster than a team of researchers could determine this information and gather a group of the necessary size. 

A study published in the journal Cancer Treatment and Research Communications illustrates the impact of big data on cancer treatment modalities. The research team used AI to mine National Cancer Institute medical records and find commonalities that may influence treatment outcomes. They determined that taking certain antidepressant medications correlated with longer survival rates among the patients included in the dataset, opening the door for targeted research on those drugs as potential lung cancer therapies. 

Other common precision medicine applications of big data include:

• New population-level interventions based on socioeconomic, geographic, and demographic factors that influence health status and disease risk
• Delivery of enhanced care value by providing targeted diagnoses and treatments to the appropriate patients
• Flagging adverse reactions to treatments
• Detection of the underlying cause of illness through data mining
• Human genomics decoding with technologies such as genome-wide association studies and next-generation sequencing software programs

These examples only scratch the surface of the endless research and development possibilities big data unlocks for start-ups in the biopharma sector. Consult with the team at RCH Solutions to explore custom AI applications and other innovations for your lab, including scalable cloud services for growing biotech and pharma research organizations.

Do You Need Support with Your Cloud Strategy?

Cloud services are swiftly becoming standard for those looking to create an IT strategy that is both scalable and elastic. But when it comes time to implement that strategy—particularly for those working in life sciences R&D—there are a number of unique combinations of services to consider. 

Here is a checklist of key areas to examine when deciding if you need expert support with your Cloud strategy. 

• Understand the Scope of Your Project
  Just as critical as knowing what should be in the cloud is knowing what should not be. The act of mapping out the on-premise vs. cloud-based solutions in your strategy will help demonstrate exactly what your needs are and where some help may be beneficial. 
• Map Out Your Integration Points
  Speaking of on-premise vs. in the Cloud, do you have an integration strategy for getting cloud solutions talking to each other as well as to on-premise solutions? 
• Does Your Staff Match Your Needs?
  When needs change on the fly, often your staff needs to adjust. However, those adjustments are not always so easily implemented, which can lead to gaps. So when creating your cloud strategy, ensure you have the right team to help understand the capacity, uptime and security requirements unique to a cloud deployment.

Check our free eBook, Cloud Infrastructure Takes Research Computing to New Heights, to help uncover the best cloud approach for your team. Download Now

• Do Your Solutions Meet Your Security Standards?
  There are more than enough examples to show the importance of data security.  It’s no longer enough however, to understand just your own data security needs. You now must know the risk management and data security policies of providers as well. 
• Don’t Forget About Data
  Life Sciences is awash with data and that is a good thing. But all this data does have consequences, including within your cloud strategy so ensure your approach can handle all your bandwidth needs. 
• Agree on a Timeline
  Finally, it is important to know the timeline of your needs and determine whether or not your team can achieve your goals. After all, the right solution is only effective if you have it at the right time. That means it is imperative you have the capacity and resources to meet your time-based goals. 

Using RCH Solutions to Implement the Right Solution with Confidence

Leveraging the Cloud to meet the complex needs of scientific research workflows requires a uniquely high level of ingenuity and experience that is not always readily available to every business. Thankfully, our Cloud Managed Service solution can help. Steeped in more than 30 years of experience, it is based on a process to uncover, explore, and help define the strategies and tactics that align with your unique needs and goals. 

We support all the Cloud platforms you would expect, such as AWS and others, and enjoy partner-level status with many major Cloud providers. Speak with us today to see how we can help deliver objective advice and support on the solution most suitable for your needs. 

Studied benefits of Cloud computing in the biotech and pharma fields.

Cloud computing has become one of the most common investments in the pharmaceutical and biotech sectors. If your research and development teams don’t have the processing power to keep up with the deluge of available data for drug discovery and other applications, you’ve likely looked into the feasibility of a digital transformation.

Real-world research reveals these examples that highlight the incredible effects of Cloud-based computing environments for start-up and growing biopharma companies.

Competitive Advantage

As more competitors move to the Cloud, adopting this agile approach saves your organization from lagging behind. Consider these statistics:

• According to a February 2022 report in Pharmaceutical Technology, keywords related to Cloud computing increased by 50% between the second and third quarters of 2021. What’s more, such mentions increased by nearly 150% over the five-year period from 2016 to 2021. 
• An October 2021 McKinsey & Company report indicated that 16 of the top 20 pharmaceutical companies have referenced the Cloud in recent press releases.
• As far back as 2020, a PwC survey found that 60% of execs in pharma had either already invested in Cloud tech or had plans for this transition underway. 

Accelerated Drug Discovery

In one example cited by McKinsey, Moderna’s first potential COVID-19 vaccine entered clinical trials just 42 days after virus sequencing. CEO Stéphane Bancel credited Cloud technology, that enables scalable and flexible access to droves of existing data and as Bancel put it, doesn’t require you “to reinvent anything,” for this unprecedented turnaround time. 

Enhanced User Experience

Both employees and customers prefer to work with brands that show a certain level of digital fluency. In the survey by PwC cited above, 42% of health services and pharma leaders reported that better UX was the key priority for Cloud investment. Most participants – 91% – predicted that this level of patient engagement will improve individual ability to manage chronic disease that require medication.

Rapid Scaling Capabilities

Cloud computing platforms can be almost instantly scaled to fit the needs of expanding companies in pharma and biotech. Teams can rapidly increase the  capacity of these systems to support new products and initiatives without the investment required to scale traditional IT frameworks. For example, the McKinsey study estimates that companies can reduce the expense associated with establishing a new geographic location by up to 50% by using a Cloud platform. 

Are you ready to transform organizational efficiency by shifting your biopharmaceutical lab to a Cloud-based environment? Connect with RCH today to learn how we support our customers in the Cloud with tools that facilitate smart, effective design and implementation of an extendible, scalable Cloud platform customized for your organizational objectives. 

References
https://www.mckinsey.com/industries/life-sciences/our-insights/the-case-for-Cloud-in-life-sciences
https://www.pharmaceutical-technology.com/dashboards/filings/Cloud-computing-gains-momentum-in-pharma-filings-with-a-50-increase-in-q3-2021/
https://www.pwc.com/us/en/services/consulting/fit-for-growth/Cloud-transformation/pharmaceutical-life-sciences.html

Consider the Advantages of Guardrails in the Cloud

Cloud integration has quite deservedly become the go-to digital transformation strategy across industries, particularly for businesses in the pharmaceutical and biotech sectors. By integrating Cloud technology into your IT approach, your organization can access unprecedented flexibility while taking advantage of real-time collaboration tools. What’s more, Cloud solutions deliver sustained value compared to on-premises solutions, which require resources (both time and money) to upgrade and maintain the associated hardware, since companies can easily scale Cloud platforms in tandem with accelerating growth.

At the same time, leaders must carefully balance the flexibility and adaptability of Cloud technology with the need for robust security and access controls. With effective guardrails administered appropriately, emerging biopharma companies can optimize research and development within boundaries that shield valuable data and ensure regulatory compliance. Explore these advantages of adding the right guardrails to your biotech or pharmaceutical organization’s digital landscape to inform your planning process.

Prevent unintended security risks

One of the most appealing aspects of the Cloud is the ability to leverage its incredible ecosystem of knowledge, tools, and solutions within your own platform. Having effective guardrails in place allows your team to quickly install and benefit from these tools, including brand-new improvements and implementations, without inadvertently creating a security risk. 

Researchers can work freely in the digital setting while the guardrail monitors activity and alerts users in the event of a security risk. As a result, the organization can avoid these common issues that lead to data breaches:

• Maintaining open access to completed projects that should have privileges in place
• Disabling firewalls or Secure Shell systems to access remote systems
• Using sensitive data for testing and development purposes
• Collaborating on sensitive data without proper access controls

Honor the shared responsibility model

Biopharma companies tend to appreciate the autonomous, self-service approach of Cloud platforms, as the dynamic infrastructure offers nearly endless experimentation. At the same time, most security issues in the Cloud result from user errors such as misconfiguration. The implementation of guardrails creates a stopgap so that even with the shortest production schedules, researchers won’t accidentally expose the organization to potential threats. Guardrails also help your company comply with your Cloud service provider’s shared responsibility policy, which outlines and defines the security responsibilities of both organizations.

Establish and maintain best practices for data integrity

Adolescent biopharma companies often experience such accelerated growth that they can’t keep up with the need to create and follow organizational best practices for data management. By putting guardrails in place, you also create standardized controls that ensure predictable, consistent operation. Available tools abound, including access and identity management permissions, security groupings, network policies, and automatic enforcement of these standards as they apply to critical Cloud data. 

A solid information security and management strategy becomes even more critical as your company expands. Entrepreneurs who want to prepare for future acquisitions should be ready to show evidence of a culture that prizes data integrity.

According to IBM, the cost of a single Cloud-based security breach in the United States averaged nearly $4 million in 2020. Guardrails provide a solution that truly serves as a golden means, preserving critical Cloud components such as accessibility and collaboration without sacrificing your organization’s valuable intellectual property, creating compliance issues and compromising research objectives.