Accelerating Clustering using Approximate Spanning Tree and Prime Number based Filter

Motivation: Clustering genomic data, including those generated via high-throughput sequencing, is an important preliminary step for assembly and analysis. However, clustering a large number of sequences is time-consuming. Methods: In this paper, we discuss algorithmic performance improvements to our existing clustering system called PEACE via the following two new approaches: (1) using Approximate Spanning Tree (AST) that is computed much faster than the currently used Minimum Spanning Tree (MST) approach, and (2) a novel Prime Numbers based Heuristic (PNH) for generating features and comparing them to further reduce comparison overheads. Results: Experiments conducted using a variety of data sets show that the proposed method significantly improves performance for datasets with large clusters with only minimal degradation in clustering quality. We also compare our methods against wcd-kaboom, a state-of-the-art clustering software. Our experiments show that with AST and PNH underperform wcd-kaboom for datasets that have many small clusters. However, they significantly outperform wcd-kaboom for datasets with large clusters by a conspicuous similar to 550xwith comparable clustering quality. The results indicate that the proposed methods hold considerable promise for accelerating clustering of genomic data with large clusters.