5 Inspiring Biotech Innovations on AWS

The cloud computing capabilities offered by Amazon Web Services (AWS) are advancing novel biological, life sciences, and genomics research that our clients are known for. The reliability, scalability, and, most importantly for many researchers, affordability of the services enable groundbreaking research and development methods that were unimaginable only a few short years ago. The metered, pay-as-you-go structure alleviates the barriers associated with processing data in-house, freeing up time and resources to accelerate discoveries. That’s why we love collaborating with organizations hoping to take advantage of AWS to elevate their work.

Through AWS, biotech companies—often with the help of software developers like GenUI—are pursuing fresh ideas, returning to existing research to draw new conclusions, empowering real-world applications, and revising how research is conducted in the first place. Cutting-edge cloud computing services have expanded the meaning of what is possible for biotech companies. The list below offers a mere glimpse into the innovations that are already taking place. More opportunities for progress in bioinformatics, facilitated by AWS, are on the horizon for those willing to think creatively.

01   Scaling genomic data analysis to make advancements in human and plant health

Large-scale genomic projects are helping scientists uncover genetic associations with disease, species diversity, and targeted traits. These discoveries generate better strategies to promote human health, insights into humans as a species, and more sustainable agriculture. Genomics projects require massive datasets, on a scale of terabytes and petabytes, to find trends across populations. Navigating such expansive datasets has been a challenge for researchers since the dawn of genomics, but the ability to scale projects at a reasonable pace and cost is already leading to breakthroughs, like the application of precision medicine.

To bypass the restrictions of in-house storage and compute capabilities, research organizations are turning to the cloud to scale projects further than was once possible. By building high-performance computing environments on AWS, researchers can upload genomics datasets and shorten the time required to process each sequence. Clusters run on Amazon Elastic Compute Cloud (Amazon EC2)  while storing research data in buckets on Amazon Simple Storage Service (Amazon S3). With GenUI’s S3P open source, massively parallel tool, we help researchers list, compare, copy, summarize and synch AWS S3 buckets to make their work even more streamlined. AWS Auto Scaling allows platforms to automatically adjust compute capacity. The result is seamless genomics analysis at any scale that can be shared across organizations. GenUI is committed to helping biotech organizations surmount genomic data hurdles so the data can be put to work to advance human and plant health research.

02   Using IoT technology to power voice-controlled scientific labs for more efficient research

The analysis of drugs, chemicals, and biological matter is necessary to move biological understanding forward, but manual processes in wet labs can hinder research. Lab work done by hand always carries the risk of human error and inefficiency. Optimizing wet lab productivity is key to accelerating many types of research. By integratingAmazon Alexa for Business into labs through AWS IoT Core, research organizations are upgrading to hands-free workflows in labs. This technology enhances productivity and accuracy by allowing lab workers to operate equipment or request readings with voice commands.

IoT Core links scientific instrumentation to the AWS Cloud, creating a reliable and secure connection to devices. It can process and route messages to and from devices. Using IoT platforms powered by AWS, lab equipment and instrumentation can also be managed and commanded remotely. This includes the collection of analytics, such as device status and configuration, that can then be applied to further improvement. The automation of typically manual workflows speeds up research and development efforts by allowing labs to process more tests, more efficiently.

03   Accelerating clinical trials with machine learning

Reducing the time to market for drugs and medical treatments is a perpetual goal for drug developers. Patients only benefit from ongoing development when new treatments get into the hands of healthcare providers, making speed a top priority. A major determinant in time to market is the ability to run efficient clinical trials. Unfortunately, trials often fail due to factors such as faulty trial design, inadequate cohort size, or patient non-adherence. By employing cloud computing and machine learning (ML) technology, many complications can be rectified.

From clinical trial planning to implementation and drawing insights, ML on AWS has many applications within clinical trial execution. The AWS ML stack and the supporting cloud infrastructure can bolster the design of more effective trials. Amazon SageMaker helps researchers build and deploy ML models to recruit high quality participants, predict non-adherence, and analyze study results faster, often in minutes instead of days. With tools to assist the ML development process, trials are designed and implemented faster with AWS.

Outside of ML, clinical trials can be made more efficient through the digitization of communication with participants through chatbots and intelligent contact centers run on AWS. This strategy preserves resources and adds to the data available for post-trial analysis. Depending on the type of trial, researchers can also incorporate mobile and wearable technology connected through AWS IoT Core. As the number of clinical trials run every year continues to grow rapidly, we believe that cloud computing will be at the core of optimization.

04   Using cloud storage and management to aggregate extant research to make new discoveries

Novel scientific discoveries are often made through the review of extant research to unearth trends or see findings in new contexts. Especially in today’s bioinformatics landscape that enables rapid research and sharing of data, the potential to make new advancements by aggregating existing research is staggering. Before cloud computing, the ability to make new discoveries this way was limited by the manual reading and curation of research. Today, the automation of this research method can cast new light on historical and current studies alike.

Biotech companies are capable of building knowledge graph databases through workflows run on AWS and platforms built on AWS. Knowledge graphs identify relationships sourced from research studies and literature. When a new problem arises, like COVID-19 for example, researchers can look back at existing knowledge for potential solution repurposing. Not only do artificial intelligence (AI) models help to build knowledge graphs, but they also assist in extracting inferences, predictions, and new ideas. This information guides scientists toward promising areas of study. Additionally, knowledge graphs can be rebuilt frequently as new research is published or new targets of study are identified. AWS allows researchers to spin up new instances and environments without jeopardizing current work.

AWS Glue helps make all of this possible through scalable, serverless data integrations. This service enriches, cleans, normalizes, and combines data while organizing it into the databases that build knowledge graphs. It can also help with the discovery and extraction of data from various sources. By automating the data integration process, researchers can immediately get to work analyzing and applying data to make new discoveries.

05   Storing and analyzing large sets of multi-omics data from multiple locations

Multi-omics helps scientists make geno-pheno-envirotype associations that help build elaborate indicators of disease. Rather than focusing on the impact of individual genes, like in genomics, multi-omics researchers observe the interaction between an array of biological factors. In this work, researchers discover the relationships between varied types of data such as transcriptomic and epigenomic data. This research can be used to better understand the biology of plants, animals, and humans, often with the goal of enhancing human health.

Due to the layers of data required, multi-omics studies frequently necessitate collaboration between organizations with vast, diverse datasets. Organizations carrying out multi-omics research build platforms hosted on AWS that harmonize data from multiple sources and facilitate collaboration. For example, a platform might allow users to compare genomics, transcriptomics, and epigenomics data and view a map of resulting gene networks. The platform also makes these datasets accessible by researchers at several locations across the globe. The process of analyzing multiple types of biological data has the potential to advance our understanding of human health and point scientists in new research directions. Our own experience helping companies manage data on AWS has led to better cancer diagnosis and facilitated collaboration across organizations.

Where will biotech innovators go next on AWS?

The capabilities and products available with AWS have facilitated the completion of innumerable complex research projects faster. What used to take weeks, now takes hours or even minutes. The many solutions offered by AWS enable creative strategies to aggregate, analyze, compare, and prepare massive datasets. Whether you’re generating new data every day or have a bank of data that needs analysis, GenUI can help you leverage AWS to make it happen quickly, securely, and on a budget. What innovations are next?

As cloud computing experts in the biotech field, we can help make your big data idea become a reality on AWS. Start a conversation with us or learn more about our AWS services here.