Sequence-to-Graph Hardware Accelerators
Published:
Sequence-to-Graph (S2G) Hardware Accelerators
Overview
This research focuses on designing efficient hardware accelerators for genomic sequence analysis, specifically targeting sequence-to-graph (S2G) transformation paradigms such as SeGraM (Sequence-to-Graph Matching). These accelerators are crucial for modern genomic data processing pipelines that work with large-scale sequencing datasets.
Research Focus
- Hardware acceleration of sequence alignment and analysis algorithms
- Sequence-to-graph transformation and construction
- De Bruijn graph construction for genomic assembly
- Memory-efficient processing of large genomic sequences
- Real-time genomic data analytics
Key Algorithms & Architectures
- SeGraM (Sequence-to-Graph Matching) acceleration
- De Bruijn graph construction
- Sequence alignment primitives
- Graph traversal optimization
Computational Challenges Addressed
- Handling massive volumes of genomic data efficiently
- Reducing memory bandwidth requirements
- Decreasing latency of critical genomic operations
- Supporting variable-length input sequences
- Enabling real-time analysis pipelines
Impact
Specialized hardware accelerators for sequence-to-graph operations can significantly improve the throughput and energy efficiency of modern genomic analysis pipelines, enabling faster disease discovery and personalized medicine applications.
Collaboration
Working with Prof. Rob Knightâs bioinformatics group and Niema Moshiri on understanding real-world genomic workload characteristics and requirements.