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Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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An FM-Index Based High-Throughput Memory-Efficient FPGA Accelerator for Paired-End Short-Read Mapping.

Chung-Hsuan Yang, Yi-Chung Wu, Yen-Lung Chen

IEEE Transactions on Biomedical Circuits and Systems

|July 10, 2023

Summary

This summary is machine-generated.

A novel hardware accelerator significantly speeds up paired-end short-read mapping using an FM-index. This innovation reduces computation time by 92.6% and enhances throughput by up to 18.6x with high accuracy.

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Area of Science:

Bioinformatics
Computer Engineering
Genomics

Background:

Accurate and efficient short-read mapping is crucial for genomic analysis.
Existing methods often face computational bottlenecks, limiting throughput and increasing processing time.

Purpose of the Study:

To develop a hardware accelerator for fast and efficient paired-end short-read mapping.
To significantly reduce memory accesses and computational operations for improved performance.

Main Methods:

Implementation of a Ferragina-Manzini index (FM-index) based hardware accelerator.
Utilizing an interleaved data structure for enhanced data locality.
Developing a lookup table for rapid retrieval of mapping location candidates.
Incorporating conditional filtering and early termination strategies to optimize processing.

Main Results:

Achieved a 92.6% reduction in computation time with only a 2% DRAM memory overhead.
Demonstrated a 1.7-to-18.6× higher throughput compared to state-of-the-art FPGA designs.
Processed over 1 billion short-reads with 99.3% accuracy on a Xilinx Alveo U250 FPGA.

Conclusions:

The proposed FM-index based hardware accelerator offers a significant advancement in short-read mapping efficiency.
The implemented techniques effectively reduce memory accesses and computational load.
This accelerator provides a high-throughput and accurate solution for large-scale genomic data analysis.