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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Updated: Aug 5, 2025

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
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A framework for high-throughput sequence alignment using real processing-in-memory systems.

Safaa Diab1, Amir Nassereldine1, Mohammed Alser2

  • 1Department of Computer Science, American University of Beirut, Riad El-Solh, Beirut 1107 2020, Lebanon.

Bioinformatics (Oxford, England)
|March 27, 2023
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Summary
This summary is machine-generated.

Alignment-in-Memory (AIM) leverages processing-in-memory (PIM) to accelerate sequence alignment. This novel framework significantly outperforms traditional CPU systems, offering a new direction for bioinformatics research.

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Area of Science:

  • Bioinformatics
  • Computer Architecture
  • Computational Biology

Background:

  • Sequence alignment is crucial for bioinformatics but is memory-bound.
  • Traditional systems face performance limitations due to memory bandwidth bottlenecks.

Purpose of the Study:

  • To introduce Alignment-in-Memory (AIM), a processing-in-memory (PIM) framework for high-throughput sequence alignment.
  • To evaluate AIM's performance on a real-world PIM system (UPMEM).

Main Methods:

  • Developed the AIM framework for sequence alignment on PIM architectures.
  • Evaluated AIM on UPMEM, a programmable PIM system.
  • Compared AIM performance against server-grade multi-threaded CPU systems.

Main Results:

  • AIM substantially outperformed multi-threaded CPU systems for sequence alignment.
  • Performance gains were observed across various algorithms, read lengths, and edit distance thresholds.
  • Demonstrated the viability of PIM for accelerating demanding bioinformatics tasks.

Conclusions:

  • Processing-in-memory offers significant advantages for memory-bound bioinformatics computations like sequence alignment.
  • The AIM framework and UPMEM system show promise for future high-performance bioinformatics.
  • Encourages further development of bioinformatics algorithms on PIM systems.