Summary
Low cost, high throughput DNA and RNA sequencing (HTS) data is now the main workforce for various genomics and transcriptomics applications. HTS technologies have already started to impact a broad range of research and clinical use for the life sciences. These include, but are not limited to 1) large-scale sequencing studies for population genomics and disease-causing mutation discovery including cancer, 2) metagenomics, 3) comparative genomics, 5) transcriptome profiling, and 6) outbreak detection and tracking including COVID-19, Ebola, and Zika. HTS also impacts the whole health care system in several directions. Although there is still much room for improvement, sequencing of personal genomes is now becoming a part of preventive and personalized medicine as HTS technologies make it possible to 1) identify genetic mutations that enable rare disease diagnosis, 2) determine cancer subtypes therefore guiding treatment options, and 3) characterize infections and antibiotic resistance. Currently all genomics data are processed in energy-hungry computer clusters and data centers, which also necessitate the transfer of data via the internet, which also consumes substantial amounts of energy and wastes valuable time. Therefore there is a need for fast, energy-efficient, and cost-efficient technologies that enable all forms of genomics research without requiring data centers and cloud platforms. In this project we aim to leverage the emerging processing-in-memory (PIM) technologies to enable such powerful edge computing. We will focus on co-designing algorithms and data structures commonly used in bioinformatics together with several types of PIM architectures to obtain the highest benefit in cost, energy, and time savings. BioPIM will also impact other fields that employ similar algorithms. Our designs and algorithms will not be limited to cheap hardware, and they will impact computation efficiency on all forms of computing environments including cloud platforms.
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| Web resources: | https://cordis.europa.eu/project/id/101047160 |
| Start date: | 01-05-2022 |
| End date: | 30-04-2026 |
| Total budget - Public funding: | 1 966 665,00 Euro - 1 966 665,00 Euro |
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Original description
Low cost, high throughput DNA and RNA sequencing (HTS) data is now the main workforce for various genomics and transcriptomics applications. HTS technologies have already started to impact a broad range of research and clinical use for the life sciences. These include, but are not limited to 1) large-scale sequencing studies for population genomics and disease-causing mutation discovery including cancer, 2) metagenomics, 3) comparative genomics, 5) transcriptome profiling, and 6) outbreak detection and tracking including COVID-19, Ebola, and Zika. HTS also impacts the whole health care system in several directions. Although there is still much room for improvement, sequencing of personal genomes is now becoming a part of preventive and personalized medicine as HTS technologies make it possible to 1) identify genetic mutations that enable rare disease diagnosis, 2) determine cancer subtypes therefore guiding treatment options, and 3) characterize infections and antibiotic resistance. Currently all genomics data are processed in energy-hungry computer clusters and data centers, which also necessitate the transfer of data via the internet, which also consumes substantial amounts of energy and wastes valuable time. Therefore there is a need for fast, energy-efficient, and cost-efficient technologies that enable all forms of genomics research without requiring data centers and cloud platforms. In this project we aim to leverage the emerging processing-in-memory (PIM) technologies to enable such powerful edge computing. We will focus on co-designing algorithms and data structures commonly used in bioinformatics together with several types of PIM architectures to obtain the highest benefit in cost, energy, and time savings. BioPIM will also impact other fields that employ similar algorithms. Our designs and algorithms will not be limited to cheap hardware, and they will impact computation efficiency on all forms of computing environments including cloud platforms.Status
SIGNEDCall topic
HORIZON-EIC-2021-PATHFINDEROPEN-01-01Update Date
09-02-2023
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