Genome-wide maps of transcription factor (TF) occupancy and regions of open chromatin implicitly contain DNA sequence signals for multiple factors. We present SeqGL, a novel de novo motif discovery algorithm to identify multiple TF sequence signals from ChIP-, DNase-, and ATAC-seq profiles. SeqGL trains a discriminative model using a k-mer feature representation together with group lasso regula...

Motif discovery for the identification of functional regulatory elements underlying gene expression is a challenging problem. Sequence inspection often leads to discovery of novel motifs (including transcription factor sites) with previously uncharacterized function in gene expression. Coupled with the complexity underlying tissue-specific gene expression, there are several motifs that are puta...

The discovery of time series motifs has emerged as one of the most useful primitives in time series data mining. Researchers have shown its utility for exploratory data mining, summarization, visualization, segmentation, classification, clustering, and rule discovery. Although there has been more than a decade of extensive research, there is still no technique to allow the discovery of time ser...

MOTIVATION Motif discovery in sequential data is a problem of great interest and with many applications. However, previous methods have been unable to combine exhaustive search with complex motif representations and are each typically only applicable to a certain class of problems. RESULTS Here we present a generic motif discovery algorithm (Gemoda) for sequential data. Gemoda can be applied ...

The discovery of regulatory motifs enriched in sets of DNA or RNA sequences is fundamental to the analysis of a great variety of functional genomics experiments. These motifs usually represent binding sites of proteins or non-coding RNAs, which are best described by position weight matrices (PWMs). We have recently developed XXmotif, a de novo motif discovery method that is able to directly opt...

The preferential conservation of transcription factor binding sites implies that non-coding sequence data from related species will prove a powerful asset to motif discovery. We present a unified probabilistic framework for motif discovery that incorporates evolutionary information. We treat aligned DNA sequence as a mixture of evolutionary models, for motif and background, and, following the e...

A RNA or DNA sequence motif is a short sequence found within a particular nucleic acid sequence families. Most amino acid and nucleic acid sequences have some level of functional or structural similarities. These similarities are mostly represented by short, contiguous sequences called motif. Motif discovery is an important aspect of molecular biology. This is because the knowledge of these seq...

Motif discovery is the problem of finding unknown patterns that appear frequently in real valued timeseries. Several approaches have been proposed to solve this problem with no a-priori knowledge of the timeseries or motif characteristics. MK algorithm is the de facto standard exact motif discovery algorithm but it can discover a single motif of a known length. In this paper, we argue that it i...

Transcription factors are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in promoters. The de-novo discovery of transcription factor binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not been fully solved yet. Here, we present a de-novo motif discovery...

The difficulties of computational discovery of transcription factor binding sites (TFBS) are well represented by (l, d) planted motif challenge problems. Large d problems are difficult, particularly for profile-based motif discovery algorithms. Their local search in the profile space is apparently incompatible with subtle motifs and large mutational distances between the motif occurrences. Here...