SubsidieMeestersProcessing-in-memory architectures and programming libraries for bioinformatics algorithms
This project aims to enhance genomics research by developing energy-efficient, cost-effective edge computing solutions using processing-in-memory technologies for high-throughput sequencing data analysis.
Projectdetails
Introduction
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.
Applications of HTS Technologies
These include, but are not limited to:
- Large-scale sequencing studies for population genomics and disease-causing mutation discovery including cancer.
- Metagenomics.
- Comparative genomics.
- Transcriptome profiling.
- Outbreak detection and tracking including COVID-19, Ebola, and Zika.
Impact on Healthcare
HTS also impacts the whole healthcare 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:
- Identify genetic mutations that enable rare disease diagnosis.
- Determine cancer subtypes, therefore guiding treatment options.
- Characterize infections and antibiotic resistance.
Challenges in Current Systems
Currently, all genomics data are processed in energy-hungry computer clusters and data centers, which also necessitate the transfer of data via the internet. This process consumes substantial amounts of energy and wastes valuable time.
Need for Efficient Technologies
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.
Project Goals
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.
Broader Impact
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.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 1.966.665 |
| Totale projectbegroting | € 1.966.665 |
Tijdlijn
| Startdatum | 1-5-2022 |
| Einddatum | 30-4-2026 |
| Subsidiejaar | 2022 |
Partners & Locaties
Projectpartners
- BILKENT UNIVERSITESI VAKIFpenvoerder
- INSTITUT PASTEUR
- CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
- TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY
- SAS UPMEM
- BAR ILAN UNIVERSITY
- EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
- IBM RESEARCH GMBH
Land(en)
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PEARL-DNA aims to develop a high-throughput, modular DNA-based data storage platform to enhance longevity, efficiency, and integration in sustainable data management solutions.
DNA-based Infrastructure for Storage and Computation
The DISCO project aims to engineer a robust DNA-based storage and computing platform, starting with a 10-bit prototype and scaling to hundreds of bits using advanced molecular techniques.
Proof of concept of affordable and scalable DNA data storage by developing a writing technology based on enzymatic DNA synthesis with a very dense semiconductor integrated circuit (CMOS chip)
The Hyperion project aims to develop affordable, scalable DNA data storage technology through advanced enzymatic synthesis and high-density chip multiplexing, targeting exabyte capacity and efficiency.
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MIcrobe-synthesised DNA NAnostructures for DIsplay-controlled Storage Cartridges
Develop a low-cost, energy-efficient data drive using bacterial cells to efficiently write, edit, store, and retrieve DNA-based data for long-term storage.
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