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Related Experiment Video

Updated: Jan 11, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Published on: July 14, 2015

KegAlign: optimizing pairwise alignments with diagonal partitioning.

A Burak Gulhan¹, Richard Burhans^2,3, Robert Harris³

¹Department of Computer Science and Engineering, Penn State University, State College, USA.

|November 18, 2025

Summary

This summary is machine-generated.

KegAlign significantly speeds up genome alignment using GPU acceleration. This novel tool maintains high sensitivity for analyzing thousands of genome assemblies, overcoming previous computational bottlenecks.

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genome sequencing and assembly technologies are advancing rapidly, producing numerous genome assemblies.
Analyzing these assemblies requires multiple sequence alignments, a process hindered by slow pairwise alignment tools like lastZ.

Purpose of the Study:

To develop an optimized, GPU-enabled pairwise alignment tool to accelerate the analysis of large-scale genome assemblies.
To maintain the high sensitivity of existing tools like lastZ for divergent genomes.

Main Methods:

Developed KegAlign, a GPU-enabled pairwise aligner.
Implemented a novel diagonal partitioning parallelization strategy optimized for GPUs.
Leveraged advanced GPU features for computational efficiency.

Main Results:

KegAlign computes human/mouse alignments in under 6 hours on a GPU node without pre-partitioning.
The tool achieves lastZ-level sensitivity, crucial for analyzing divergent genomes.
KegAlign is accessible as source code, a Conda package, and a Galaxy workflow.

Conclusions:

KegAlign offers a significant speedup for genome-wide pairwise alignments.
The tool addresses the bottleneck in analyzing large numbers of genome assemblies.
KegAlign enhances the feasibility of comparative genomics on a massive scale.