GenPairX: A Hardware-Algorithm Co-Designed Accelerator for Paired-End Read Mapping
This paper discusses a new technology called GenPairX that helps speed up the process of mapping DNA sequences to reference genomes, which is important for understanding genetics and diseases.
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- 1 The objective of the Partitioned Seeding stage is to extract seeds from each read-pair and encode the DNA segment into a hash value.
- 2 The design objective is to maximize the number of seeds extracted per second.
- 3 The design objective of this module is to maximize the throughput of processed genome locations returned by the SeedMap Query step.
- 4 ISA extensions for edge SoCs , vector acceleration frameworks like QUET-ZAL , and tiling-based approaches like TALCO further aim to optimize DP.
Introduction
Genome study is essential for personalized medicine , disease outbreak tracking , agriculture , scientific discovery , and evolutionary studies . In the sequencing stage, raw DNA or RNA is fragmented and read by high-throughput sequencing machines, producing billions of short sequences, namely reads.
Among sequencing techniques, paired-end sequencing has been widely adopted, used in over 90% of sequencing workflows today .
1 In paired-end sequencing, both ends of each DNA fragment are read, generating two sequences per fragment with a known orientation and approximate distance between them.
The goal of the Light Alignment step ( 4 in Fig. 3 ) is to efficiently align sequences with a limited number of edits, without relying on DP based methods.
We integrate GenPairX with GenDP , a hardware accelerator that serves as a fallback mechanism for the small fraction of read-pairs that cannot be mapped or aligned by GenPairX.
Research Question
The objective of the Partitioned Seeding stage is to extract seeds from each read-pair and encode the DNA segment into a hash value. The design objective is to maximize the number of seeds extracted per second.
The design objective of this module is to maximize the throughput of processed genome locations returned by the SeedMap Query step.
ISA extensions for edge SoCs , vector acceleration frameworks like QUET-ZAL , and tiling-based approaches like TALCO further aim to optimize DP.
Methodology
Modern genome studies involve two major stages: sequencing and analysis. The analysis stage processes this data to reconstruct the genome, identify genetic variants, and extract biologically meaningful insights, requiring intensive computation and memory accesses across multiple software pipelines.
Study Design
A central and computationally demanding task in the analysis stage is read mapping, where reads are aligned to a reference genome.
Unfortunately, existing state-of-the-art filters and DP accelerators are not optimized for paired-end read mapping, a major step in paired-end sequence analysis.
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Results & Findings
This provides increased context for each read pair, enabling higher alignment accuracy, improved resolution of structural variants, and better handling of repetitive or ambiguous genomic regions . These filters effectively reduce the workload of fine-grained compute-intensive operations (e.g., DP), thereby increasing the performance of read mapping.
- This provides increased context for each read pair, enabling higher alignment accuracy, improved resolution of structural variants, and better handling of repetitive or ambiguous genomic regions.
- These filters effectively reduce the workload of fine-grained compute-intensive operations (e.g., DP), thereby increasing the performance of read mapping.
- This step presents significant computational challenges, as it involves aligning both ends of a DNA fragment to a reference genome while maintaining their relative orientation and.
- Based on our experiments (detailed in §3), we identify two key reasons.
- Hence, GenPair by default performs simple XOR operations, replacing costly DP operations with computationally simpler alternatives to reduce alignment time.
(vi) GenPairX + GenDP: Our ASIC accelerator of GenPair is integrated with GenDP, where GenDP serves as the fallback for reads that GenPairX cannot map (see Fig. 10 ).
We evaluate the impact of GenPairX on an end-to-end read alignment use case, which accounts for the residual read-pairs that cannot be directly mapped by GenPairX, but need traditional DP-alignment.
Practical Applications
The current and previous index of the Seed Table correspond to a continuous region in the Location Table, that includes all possible locations in the reference genome where the seed can be mapped. Despite the uniform distribution of the seeds and their respective locations across all memory channels, during brief intervals, some channels may become overutilized.
Since we utilize all available memory channels by dispatching multiple reads concurrently, the corresponding responses of six seed locations may arrive out of order.
In some cases, certain seeds may be associated with an unusually large number of genome locations, requiring excessively large FIFO depths.
Motivation and Insights: Analysis of Paired-End Read Mapping
Paired-end read mapping offers higher accuracy than single-end mapping, particularly in detecting structural variants. The study analyzes performance bottlenecks and identifies optimization opportunities in the mapping process.
Background
Paired-end read mapping involves aligning both reads of a pair to a reference genome, utilizing the known distance between them. The process includes seeding, chaining, and sequence alignment, with chaining and alignment being the most computationally intensive.
Limitations and Cautions
A useful limitation and caution is that this article summarizes the available paper text and extracted evidence; readers should consult the source paper before treating any interpretation as definitive.
The paper’s conclusions may depend on its source selection, definitions, assumptions, and the scope of its analysis, so follow-up reading is important.
Frequently Asked Questions
The objective of the Partitioned Seeding stage is to extract seeds from each read-pair and encode the DNA segment into a hash value. The design objective is to maximize the number of seeds extracted per second.
A central and computationally demanding task in the analysis stage is read mapping, where reads are aligned to a reference genome. Paired-end read mapping is a technique in genome analysis where both ends of DNA fragments are sequenced and aligned to a.
To efficiently map the seeds, a specialized data structure is required to associate each seed with its corresponding potential mapping locations. The goal of the Paired-Adjacency Filtering ( 3 in Fig. 3 ) is to identify the potential matching locations that lead.
The goal of this work is to improve the performance of the widely used paired-end read mapping algorithm by (1) increasing the effectiveness of the read filtering stage to discard a large fraction of redundant computations, and (2) accelerating overall execution by.
(vi) GenPairX + GenDP: Our ASIC accelerator of GenPair is integrated with GenDP, where GenDP serves as the fallback for reads that GenPairX cannot map (see Fig. 10 ). We evaluate the impact of GenPairX on an end-to-end read alignment use case.
This paper discusses a new technology called GenPairX that helps speed up the process of mapping DNA sequences to reference genomes, which is important for understanding genetics and diseases.