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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

RaPID2: a parallel scalable framework for identity-by-descent segment detection via parallel PBWT.

Kecong Tang¹, Ardalan Naseri², Degui Zhi²

¹Department of Computer Science, University of Central Florida, Orlando, Florida, 32816, United States.

Bioinformatics Advances

|April 6, 2026

Summary

This summary is machine-generated.

RaPID2 is a new framework for identity-by-descent (IBD) detection that significantly speeds up analysis on large genetic datasets. It offers a scalable and efficient solution for modern genomic research, maintaining high accuracy.

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Identity-by-descent (IBD) detection is crucial for genetic analysis, including genealogy, population history, and disease gene mapping.
Previous tools like RaPID were accurate but lacked scalability for large biobank datasets.
High-performance computing (HPC) and parallel processing are essential for analyzing modern genomic data.

Purpose of the Study:

To develop a scalable and efficient framework for identity-by-descent (IBD) detection.
To overcome the limitations of previous IBD detection tools in handling large-scale genomic data.
To provide a robust solution suitable for both HPC and memory-constrained environments.

Main Methods:

Redesigned the RaPID framework into RaPID2 with a focus on parallel and distributed computing.
Eliminated disk I/O bottlenecks and implemented memory-aware haplotype pair partitioning.
Incorporated fault-tolerant execution and supported both fixed and dynamic window sizes.

Main Results:

RaPID2 demonstrates a 32-fold speedup compared to the original RaPID at a 2 cM threshold.
Maintained statistically identical detection power and accuracy.
Achieved efficient performance in both HPC and memory-constrained settings.

Conclusions:

RaPID2 offers a significant advancement in IBD detection for large-scale genomic datasets.
The framework is robust, efficient, and scalable for modern bioinformatics challenges.
RaPID2 provides a valuable tool for researchers in genetics, genealogy, and disease mapping.