Investigating Power and Limitations of Ensemble Motif Finders Using Metapredictor CE3

Ensemble methods represent a relatively new approach to motif discovery that combines the results returned by "third-party" finders with the aim of achieving a better accuracy than that obtained by the single tools. Besides the choice of the external finders, another crucial element for the success of an ensemble method is the particular strategy adopted to combine the finders' results, a.k.a. learning function. Results appeared in the literature seem to suggest that ensemble methods can provide noticeable improvements over the quality of the most popular tools available for motif discovery. With the goal of better understanding potentials and limitations of ensemble methods, we developed a general software architecture whose major feature is the flexibility with respect to the crucial aspects of ensemble methods mentioned above. The architecture provides facilities for the easy addition of virtually any third-party tool for motif discovery whose code is publicly available, and for the definition of new learning functions. We present a prototype implementation of our architecture, called CE3 (Customizable and Easily Extensible Ensemble). Using CE3, and available ensemble methods, we performed experiments with three well-known datasets. The results presented here are varied. On the one hand, they confirm that ensemble methods cannot be just considered as the universal remedy for "in-silico" motif discovery. On the other hand, we found some encouraging regularities that may help to find a general set up for CE3 (and other ensemble methods as well) able to guarantee substantial improvements over single finders in a systematic way

CE3: Customizable and Easily Extensible Ensemble Tool for Motif Discovery

Ensemble methods (or simply ensembles) for motif discov- ery represent a relatively new approach to improve the ac- curacy of stand-alone motif finders. The performance of an ensemble is clearly determined by the included finders as well as the strategy to combine the results returned by the latter (the so called learning rule). A potential obstacle to a widespread adoption of ensembles is that the choice of the particular finders included is closed. Although possible in principle, the addition to an ensemble of a new "promising" tool requires knowledge of the internals of the ensemble and usually non trivial programming skills. In this research we propose a general architecture for ensem- bles and a prototype called CE3: Customizable and Easily Extensible Ensemble, which is meant to be extensible and customizable at the level of the two key components mod- ules namely external tools finding and learning rule. In this way the user will be able to essentially "simulate" any ex- isting ensemble, create his/her own ensemble according to his/her preferences on finding tools and learning functions and, finally, keep it up to date when new tools and new ideas for learning functions are proposed in literature. These fea- tures also make CE3 a suitable tool to perform experiments that may lead to a proper configuration of ensembles in the research of novel motifs

Direct vs 2-stage approaches to structured motif finding

BACKGROUND: The notion of DNA motif is a mathematical abstraction used to model regions of the DNA (known as Transcription Factor Binding Sites, or TFBSs) that are bound by a given Transcription Factor to regulate gene expression or repression. In turn, DNA structured motifs are a mathematical counterpart that models sets of TFBSs that work in concert in the gene regulations processes of higher eukaryotic organisms. Typically, a structured motif is composed of an ordered set of isolated (or simple) motifs, separated by a variable, but somewhat constrained number of “irrelevant” base-pairs. Discovering structured motifs in a set of DNA sequences is a computationally hard problem that has been addressed by a number of authors using either a direct approach, or via the preliminary identification and successive combination of simple motifs. RESULTS: We describe a computational tool, named SISMA, for the de-novo discovery of structured motifs in a set of DNA sequences. SISMA is an exact, enumerative algorithm, meaning that it finds all the motifs conforming to the specifications. It does so in two stages: first it discovers all the possible component simple motifs, then combines them in a way that respects the given constraints. We developed SISMA mainly with the aim of understanding the potential benefits of such a 2-stage approach w.r.t. direct methods. In fact, no 2-stage software was available for the general problem of structured motif discovery, but only a few tools that solved restricted versions of the problem. We evaluated SISMA against other published tools on a comprehensive benchmark made of both synthetic and real biological datasets. In a significant number of cases, SISMA outperformed the competitors, exhibiting a good performance also in most of the cases in which it was inferior. CONCLUSIONS: A reflection on the results obtained lead us to conclude that a 2-stage approach can be implemented with many advantages over direct approaches. Some of these have to do with greater modularity, ease of parallelization, and the possibility to perform adaptive searches of structured motifs. As another consideration, we noted that most hard instances for SISMA were easy to detect in advance. In these cases one may initially opt for a direct method; or, as a viable alternative in most laboratories, one could run both direct and 2-stage tools in parallel, halting the computations when the first halts

CMStalker: a combinatorial tool for composite motif discovery

Controlling the differential expression of many thousands different genes at any given time is a fundamental task of metazoan organisms and this complex orchestration is controlled by the so-called regulatory genome encoding complex regulatory networks: several Transcription Factors bind to precise DNA regions, so to perform in a cooperative manner a specific regulation task for nearby genes. The in silico prediction of these binding sites is still an open problem, notwithstanding continuous progress and activity in the last two decades. In this paper we describe a new efficient combinatorial approach to the problem of detecting sets of cooperating binding sites in promoter sequences, given in input a database of Transcription Factor Binding Sites encoded as Position Weight Matrices. We present CMStalker, a software tool for composite motif discovery which embodies a new approach that combines a constraint satisfaction formulation with a parameter relaxation technique to explore efficiently the space of possible solutions. Extensive experiments with twelve data sets and eleven state-of-the-art tools are reported, showing an average value of the correlation coefficient of 0.54 (against a value 0.41 of the closest competitor). This improvements in output quality due to CMStalker is statistically significant

FPF-SB: a Scalable Algorithm for Microarray Gene Expression Data Clustering

Efficient and effective analysis of large datasets from microarray gene expression data is one of the keys to time-critical personalized medicine. The issue we address here is the scalability of the data processing software for clustering gene expression data into groups with homogeneous expression profile. In this paper we propose /FPF-SB/, a novel clustering algorithm based on a combination of the Furthest-Point-First (FPF) heuristic for solving the /k/-center problem and a stability-based method for determining the number of clusters /k/. Our algorithm improves the state of the art: it is scalable to large datasets without sacrificing output quality

Efficient Strategies for Partitioning and Querying a Hirerchical Document Space

We consider a problem arising in the efficient management of a Hierachical Document Space, i,e.,partitioning the leaves of a tree among a set of servers in such a way that it is possible to take full advantage of the hierarchical system to efficiently answer user\u27s queries. After providing that the problem is NP-Hard, we devise efficient approximate solutions, and we make a number of experiments which show that allowing for very little space inefficiency can be instrumental to acheiving a significant improvement in the query efficiency

K-Boost: a Scalable Algorithm for High-Quality Clustering of Microarray Gene Expression Data

Motivation: Microarray technology for profiling gene expression levels is a popular tool in modern biological research. Applications range from tissue classification to the detection of metabolic networks, from drug discovery to time-critical personalized medicine. Given the increase in size and complexity of the data sets produced, their analysis is becoming problematic in terms of time/quality tradeoffs. Clustering genes with similar expression profiles is a key initial step for subsequent manipulations and the increasing volumes of data to be analyzed requires methods that are at the same time efficient (completing an analysis in minutes rather than hours) and effective (identifying significant clusters with high biological correlations). Results: In this paper we propose K-Boost, a novel clustering algorithm based on a combination of the Furthest-Point-First (FPF) heuristic for solving the metric k-centers problem, a stability-based method for determining the number of clusters (i.e. the value of k), and a k-means-like cluster refinement. K-Boost is able to detect the optimal number of clusters to produce. It is scalable to large data-sets without sacrificing output quality as measured by several internal and external criteria