We use methods from Data Mining and Knowledge Discovery to design an algorithm for detecting motifs in protein sequences. Based on this approach, we have implemented a program called “GYM”. The Helix-TurnHelix Motif was used as a model system on which to test our program. The program was also extended to detect Homeodomain motifs. The detection results for the two motifs compare favorably with ...

Specificity of mammalian gene regulatory regions is achieved to a large extent through the combinatorial binding of sets of transcription factors to distinct binding sites, discrete combinations of which are often referred to as regulatory modules. Identification and subsequent characterization of gene regulatory modules will be a key step in assembling transcriptional regulatory networks from ...

MOTIVATION Transcription factor (TF) ChIP-seq datasets have particular characteristics that provide unique challenges and opportunities for motif discovery. Most existing motif discovery algorithms do not scale well to such large datasets, or fail to report many motifs associated with cofactors of the ChIP-ed TF. RESULTS We present DREME, a motif discovery algorithm specifically designed to f...

SUMMARY ChIP-Seq data are a new challenge for motif discovery. Such a data typically consists of thousands of DNA segments with base-specific coverage values. We present a new version of our DNA motif discovery software ChIPMunk adapted for ChIP-Seq data. ChIPMunk is an iterative algorithm that combines greedy optimization with bootstrapping and uses coverage profiles as motif positional prefer...

Motif discovery from a set of sequences is a very important problem in biology. Although a lot of research has been done on computational techniques for (sequence) motif discovery, discovering motifs in a large number of sequences still remains challenging. We propose a novel computational framework that combines multiple computational techniques such as pairwise sequence comparison, clustering...

The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms--MAST, FIMO and GLAM2SCAN--allow scannin...

Computational methods offer great hope but limited accuracy in the prediction of functional cis-regulatory elements; improvements are needed to enable synthetic promoter design. We applied an ensemble strategy for de novo soybean cyst nematode (SCN)-inducible motif discovery among promoters of 18 co-expressed soybean genes that were selected from six reported microarray studies involving a comp...

MOTIVATION The goal of motif discovery is to detect novel, unknown, and important signals from biology sequences. In most models, the importance of a motif is equal to the sum of the similarity of every single position. In 2006, Song et al. introduced Aggregated Related Column Score (ARCS) measure which includes correlation information to the evaluation of motif importance. The paper showed tha...