A new approach to the assessment of the quality of predictions of transcription factor binding sites

HIGEDA: a hierarchical gene-set genetics based algorithm for finding subtle motifs in biological sequences

MOTIVATION

Identification of motifs in biological sequences is a challenging problem because such motifs are often short, degenerate, and may contain gaps. Most algorithms that have been developed for motif-finding use the expectation-maximization (EM) algorithm iteratively. Although EM algorithms can converge quickly, they depend strongly on initialization parameters and can converge to local sub-optimal solutions. In addition, they cannot generate gapped motifs. The effectiveness of EM algorithms in motif finding can be improved by incorporating methods that choose different sets of initial parameters to enable escape from local optima, and that allow gapped alignments within motif models.

RESULTS

We have developed HIGEDA, an algorithm that uses the hierarchical gene-set genetic algorithm (HGA) with EM to initiate and search for the best parameters for the motif model. In addition, HIGEDA can identify gapped motifs using a position weight matrix and dynamic programming to generate an optimal gapped alignment of the motif model with sequences from the dataset. We show that HIGEDA outperforms MEME and other motif-finding algorithms on both DNA and protein sequences.

AVAILABILITY AND IMPLEMENTATION

Source code and test datasets are available for download at http://ouray.cudenver.edu/~tnle/, implemented in C++ and supported on Linux and MS Windows.

Library of local descriptors models the core of proteins accurately

In this article, we present a novel approach to describing proteins based on multifragment structure motifs called local descriptors. We collect structurally similar descriptors in groups to construct a compact library of groups of descriptors. To demonstrate its feasibility for a wide spectrum of applications, ranging from structure comparison and analysis to structure prediction, it is critical to show the ability of groups from our library to reproduce proteins accurately. We show that this library describes all local 3D structure patterns occurring in the core of proteins and present an algorithm for reconstruction of accurate global 3D structures. Moreover, we show that the sequence of motifs used in such a construction correlates significantly with the amino acid sequence of the considered protein. Finally, we present how our library may be successfully used for predicting protein sequence based on the structure.