Computational biology is undergoing a revolution. However, the analysis of single cells is a hard problem to solve. Standard statistical techniques used in genomic analysis fail to capture the complexity present in single-cell datasets. Open Problems in Single-Cell Analysis is a community-driven effort using AWS to drive the development of novel methods that leverage the power of single-cell data.
One of the biggest scientific achievements of the twenty-first century was the completion of the Human Genome Project and the publication of a draft human genome. The project took over 13 years to complete and remains one of the largest private-public international collaborations ever. Advances since in sequencing technologies, computational hardware, and novel algorithms reduced the time it takes to produce a human genome assembly to only a few days, at a fraction of the cost. This made using the human genome draft for precision and personalized medicine more achievable. In this blog, we demonstrate how to do a genome assembly in the cloud in a cost-efficient manner using ARM-based AWS Graviton2 instances.
A generalized approach to benchmarking genomics workloads in the cloud: Running the BWA read aligner on Graviton2
The AWS Cloud gives genomics researchers access to a wide variety of instance types and chip architectures and this elasticity allows us to rethink genomics workflows when running workloads in the cloud. Given the increased performance of the Graviton2 instances, we wanted to explore if they can be used for cost-effective and performant genomics workloads. Read on to learn about our generalized approach for determining the most effective instance type for running genomics workloads in the cloud.
Taking COVID in STRIDES: The National Center for Biotechnology Information makes coronavirus genomic data available on AWS
AWS and the National Institutes of Health’s (NIH) National Center for Biotechnology Information (NCBI) announced the creation of the Coronavirus Genome Sequence Dataset to support COVID-19 research. The dataset is hosted by the AWS Open Data Sponsorship Program and accessible on the Registry of Open Data on AWS, providing researchers quick and easy access to coronavirus sequence data at no cost for use in their COVID-19 research.
In 2014, the Wall Lab at Stanford University sought to answer one of the most pressing questions in neuroscience: What genes influence autism spectrum disorder (ASD)? According to the Centers for Disease Control (CDC), this neurodevelopmental disorder affects roughly one in 54 children in America and is on the rise—nearly tripling since 1992. In the lab’s study of ASD genetics, they chose the cloud—and a unique experimental approach—to speed the time to science.
Academic medical centers (AMCs) are under pressure to reduce costs, innovate at scale, and improve operational performance. To do this, they’re turning to the cloud. Two AWS Partner Network (APN) Public Sector Partners used the cloud to create solutions for AMCs that use large datasets to help advance medical research and analyze genomic data. Learn how these two partners are building solutions in the cloud to help AMCs further their mission.
How the University of British Columbia uses the cloud to reduce sunflower genomic processing time and research costs with a data lake
The botany department at the University of British Columbia (UBC) and the UBC Data Science Institute are working together to research the evolution and genetic makeup of sunflowers – a critical crop in addressing global food security.
The Centre for Genomic Pathogen Surveillance (CGPS) is based at the Wellcome Genome Campus, Cambridge and The Big Data Institute, University of Oxford in the United Kingdom. Much of its work involves collaborating with laboratories around the world to enhance genomic surveillance by using big data, engineering, training, and genomic capacity building. Ultimately, the Centre hopes to enable the linking and real-time interpretation of data globally to track pathogens and antimicrobial resistance at an affordable rate. Typically, spikes in cost for research are a common challenge for laboratories. With the cloud, the team wanted to mitigate their costs, and particularly those of their partners in low and middle-income countries, by exploring the Amazon Web Services (AWS) Cloud’s pay-as-you-go infrastructure.
September was monumental for the blog. Thanks to the continuous innovation of our customers, the blog topped 500 posts since launch! Here are the top September stories that helped us get there.
Cromwell is a workflow management system from Broad Institute of MIT and Harvard, a Cambridge, Massachusetts-based research institute that leverages the power of genomics to help us understand biology as well as treat human disease. Cromwell aims to facilitate scientific workflows for genome analysis. It enables genomic researchers, scientists, developers, and analysts to efficiently run their experiments, without deep expertise in computing capabilities. And it’s now on AWS.