GenPIP: In-Memory Acceleration of Genome Analysis via Tight Integration of Basecalling and Read Mapping
This paper introduces a new system called GenPIP that speeds up the process of analyzing genomes by combining two important steps\u2014basecalling and read mapping\u2014into one efficient process. This helps save time and energy while maintaining accuracy.
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- 1 Indexing is a preprocessing step that enables efficient queries to find matches between reference genome subsequences and reads.
- 2 We are motivated to reject useless reads as soon as possible to reduce computation and memory overheads.
- 3 We aim to quantitatively demonstrate the potential benefits of overcoming the limitations of prior works.
- 4 We demonstrate System C to show the potential benefit of eliminating data movement between separate accelerators and CPUs.
Introduction
Long read genome sequencing technologies have advanced genomic fields like personalized medicine, forensic science, evolutionary biology, and infectious disease investigation. Oxford Nanopore Technology (ONT) is a widely-used long-read sequencing technology.
ONT provides portable sequencing devices connected to a computer via a USB interface.
ONT devices generate long reads based on the organism’s DNA sequence.
None of the pull down circuits are active because the left circuit cannot drain current due to high resistance and the right circuit cannot due to the off transistor.
The key idea of the chunk-mapping-based early rejection technique is that a read probably cannot be mapped to the reference genome if enough consecutive chunks in this read cannot be mapped to the reference genome (i.e., the chaining score of.
Methodology
Reads are sent to a separate device for further analysis after basecalling. Basecalling and read mapping are the most time-consuming steps in the genome analysis pipeline due to computationally-intensive algorithms.
Study Design
Our goal is to provide effective in-memory acceleration of the genome analysis pipeline while minimizing data movement and useless computation.
We propose GenPIP, a fast and energy-efficient in-memory acceleration system for the genome analysis pipeline.
Results & Findings
A translated read is associated with a quality score for each base to reflect translation accuracy. Basecalling commonly uses a deep neural network to ensure high accuracy.
- A translated read is associated with a quality score for each base to reflect translation accuracy.
- Basecalling commonly uses a deep neural network to ensure high accuracy.
- Read mapping depends on dynamic programming-based algorithms to find matching locations in the reference genome.
- Large execution time and energy consumption overheads ensue because a significant portion of basecalling output is unused.
- We compare GenPIP with state-of-the-art software tools and a combination of state-of-the-art in-memory accelerators.
Indexing is a preprocessing step that enables efficient queries to find matches between reference genome subsequences and reads.
We are motivated to reject useless reads as soon as possible to reduce computation and memory overheads.
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Practical Applications
Unfortunately, mapping short chunks provides too large a list of possible mapping locations. The seeding component sends a list of the possible match locations to the read mapping controller ( 4 ).
Eighth, the read mapping controller sends the chunk and its possible match locations to the DP units to perform chaining ( 5 ).
If the query string matches one reference string in the ReRAM-based CAM, the ReRAM-based CAM outputs the address (Addr.) to access the corresponding values (i.e., the possible match locations) stored inside the ReRAM-based RAM.
Background and Motivation
This section analyzes the current genome analysis pipeline, identifies performance and energy bottlenecks, and sets the goals for the proposed GenPIP system.
Nanopore Genome Analysis Pipeline
Describes the ONT genome analysis pipeline, detailing the processes of basecalling and read mapping, and the importance of read quality control.
State-of-the-art Solutions
Reviews existing hardware accelerators for basecalling and read mapping, highlighting the advantages of non-volatile memory (NVM)-based processing in memory (PIM) accelerators.
Figures Explained
Frequently Asked Questions
Each read has a length ranging from hundreds to millions of base pairs but has a high sequencing error rate. To store 1 or 0 in a CAM cell, the resistors are programmed to high and low resistance respectively.
Reads are sent to a separate device for further analysis after basecalling. Basecalling and read mapping are the most time-consuming steps in the genome analysis pipeline due to computationally-intensive algorithms.
Indexing is a preprocessing step that enables efficient queries to find matches between reference genome subsequences and reads. We are motivated to reject useless reads as soon as possible to reduce computation and memory overheads.
We conclude that early rejection based on the quality score of chunks should sample a small number of non-consecutive chunks to accurately guess whether or not a read is low-quality. We conclude that GenPIP is very effective at reducing energy compared to.
None of the pull down circuits are active because the left circuit cannot drain current due to high resistance and the right circuit cannot due to the off transistor. The key idea of the chunk-mapping-based early rejection technique is that a read.
This paper introduces a new system called GenPIP that speeds up the process of analyzing genomes by combining two important steps\u2014basecalling and read mapping\u2014into one efficient process. This helps save time and energy while maintaining accuracy.