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An efficient clustering algorithm for partitioning Y-short tandem repeats data.

Ali Seman¹, Zainab Abu Bakar, Mohamed Nizam Isa

¹Center for Computer Sciences, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia. aliseman@tmsk.uitm.edu.my

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|October 9, 2012

Summary

A new algorithm, k-Approximate Modal Haplotypes (k-AMH), enhances Y-Short Tandem Repeats (Y-STR) data clustering by overcoming common partitioning issues. k-AMH achieves superior accuracy, including 100% for two datasets, outperforming existing methods.

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Area of Science:

Population genetics
Bioinformatics
Computational biology

Background:

Y-Short Tandem Repeats (Y-STR) data present challenges for partitioning due to high similarity between objects.
Existing algorithms struggle with non-unique centroids and local minima, leading to suboptimal clustering results.

Purpose of the Study:

To develop a novel algorithm for improved partitioning of Y-STR data.
To address the limitations of existing clustering methods in handling similar Y-STR profiles.

Main Methods:

Introduction of the k-Approximate Modal Haplotypes (k-AMH) algorithm.
Evaluation of k-AMH performance against established algorithms on multiple Y-STR datasets.

Main Results:

k-AMH achieved the highest clustering accuracy on five out of six datasets, with 100% accuracy on two.
The algorithm demonstrated a superior overall mean accuracy score of 0.93 compared to other methods.
k-AMH effectively resolves non-unique centroids and local minima problems inherent in Y-STR data partitioning.

Conclusions:

The k-AMH algorithm offers superior partitioning performance for Y-STR data.
Its effectiveness stems from its ability to overcome centroid and local minima issues.
The algorithm exhibits linear time complexity, O(km(n-k)), ensuring computational efficiency.