Powering genomic data analysis on Azure
Discover insights from the genome using open source and open standard based solutions that take advantage of the performance and scalability of the global Azure cloud infrastructure.
Genomics workspace on Azure
With Azure, you have everything you need to build your genomics workspace in the cloud providing a centralized and secure environment to upload, analyze, and share data within and across organizations.
Customers building with Microsoft Genomics solutions
Belford Institute of Oceanography, Nova Scotia
"Leveraging Cromwell on Azure and data science tools is helping us eliminate months of time and manual work to analyze thousands of fish genomes and build complex multi-modal data models."
Dr. Tony Kess, Scientist, Bradbury Population Genomics Lab
"At Biotia, by using Cromwell on Azure to back our compute-intensive genomics workflows we have achieved substantial parallelization for our novel next generation sequencing (NGS) based COVID-19 detection and characterization assay."
Joe Barrows, Director of Software Engineering
St. Jude Children's Research Hospital
"Access to high-quality clinical genomic data, generated leveraging Microsoft Genomics service and streamed to St. Jude Cloud, will help further research in precision medicine for childhood cancer and other diseases."
Dr. Jinghui Zhang, Chair, Department of Computational Biology
Updates, blogs, articles
- Accelerating genomics workflows and data analysis on Azure
- Microsoft for Healthcare: new people, products, and partnerships.
- Harnessing big data in pediatric research to reimagine healthcare.
- St. Jude Cloud to accelerate scientific discoveries through access to real-time clinical genome sequencing data.
- Cancer researchers embrace AI to accelerate development of precision medicine.
- Accelerate precision medicine with Microsoft Genomics. (PDF)
- De(con)struction of the lazy-F loop: improving performance of Smith Waterman alignment. (Roman Snytsar)
- Vectorized Character Counting for Faster Pattern Matching. (Roman Snytsar)
- Parallel approach to sliding window sums. (Roman Snytsar, Yatish Turakhia)
- Exploring the consistency of the quality scores with machine learning for next-generation sequencing experiments. (Erdal Cosgun, Min Oh)