A Clique-Based Method for Improving Motif Scanning Accuracy

TL;DR

This paper introduces a clique-based approach that enhances motif scanning accuracy by focusing on maximal cliques in a weighted graph derived from motif similarity, significantly improving precision across multiple scanners.

Contribution

The paper proposes a novel clique-based method that leverages in-between similarity analysis to improve motif scanning accuracy, applicable to various iterative scanners.

Findings

Significant increase in precision of motif scans.

Effective across multiple scanning algorithms.

Validated on plant proteomes.

Abstract

We present a new approach for improving motif scanning accuracy, based on analysis of in-between similarity. Given a set of motifs obtained from a scanning process, we construct an associated weighted graph. We also compute the expected weight of an edge in such a graph. It turns out that restricting results to the maximal clique in the graph, computed with respect to the expected weight, greatly increases precision, hence improves accuracy of the scan. We tested the method on an ungapped motif-characterized protein family from five plant proteomes. The method was applied to three iterative motif scanners - PSI-BLAST, JackHMMer and IGLOSS - with very good results