Parallel and Scalable Precise Clustering for Homologous Protein Discovery

TL;DR

This paper introduces ClusterMerge, a parallel algorithm for precise protein clustering that significantly speeds up homologous protein discovery while maintaining high accuracy and scalability.

Contribution

The paper presents ClusterMerge, a novel parallel clustering algorithm that leverages transitive relationships for scalable and efficient homologous protein identification.

Findings

Achieves 99.8% recall of similar pairs compared to full comparison

Attains 604× speedup on 768 cores

Maintains high parallel and distributed scalability

Abstract

This paper presents a new, parallel implementation of clustering and demonstrates its utility in greatly speeding up the process of identifying homologous proteins. Clustering is a technique to reduce the number of comparison needed to find similar pairs in a set of $n$ elements such as protein sequences. Precise clustering ensures that each pair of similar elements appears together in at least one cluster, so that similarities can be identified by all-to-all comparison in each cluster rather than on the full set. This paper introduces ClusterMerge, a new algorithm for precise clustering that uses transitive relationships among the elements to enable parallel and scalable implementations of this approach. We apply ClusterMerge to the important problem of finding similar amino acid sequences in a collection of proteins. ClusterMerge identifies 99.8% of similar pairs found by a full $O(n^2)$ comparison, with only half as many operations. More importantly, ClusterMerge is highly amenable to parallel and distributed computation. Our implementation achieves a speedup of 604$\times$ on 768 cores (1400$\times$ faster than a comparable single-threaded clustering implementation), a strong scaling efficiency of 90%, and a weak scaling efficiency of nearly 100%.