ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules

Abstract

BackgroundThe detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected.ResultsWe present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools.ConclusionWe show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input.

COPS: Detecting Co-Occurrence and Spatial Arrangement of Transcription Factor Binding Motifs in Genome-Wide Datasets

“…The performance of COPS was directly compared to available multiple sequence tools, namely Compo [38], ModuleDigger [21] and CPModule [22]. Our attempt to use Compo for analyzing the datasets scanned with COPS was not successful, since the software was not able to handle such large numbers of sequences (in some cases >2000 sequences i.e.…”

“…Therefore we did not proceed with a direct comparison of the performance of the two programs. The comparison of COPS to ModuleDigger [21] and CPModule [22] was carried out using sequence-sets of increasing size from the Twi and Pros datasets and the motif pairs Twi/Bin, Bap/Twi, Tin/Twi, and Ase/Pros, Snail/Pros respectively (described in detail in Materials and Methods). As shown in Figure 4, the performance of COPS with respect to the MCC value is comparable or superior to CPModule at differently sized sequence sets with different TF pairs, while COPS outperforms ModuleDigger in every run.…”

“…The relative performance of COPS was compared to ModuleDigger [21] and CPModule [22] using the Matthews correlation coefficient (MCC). ModuleDigger uses Clover [23] to scan the observed and background sequences and enumerates all frequent co-occurrence motifs, finally ranking the finding by a gene-set specificity score.…”

Computational inference of transcription factor cooperation by fuzzy frequent itemset mining

“…2) Searching frequent itemsets among the obtained fuzzy groups. Frequent itemset mining procedures have been successfully applied in previous approaches [17], [16], [18]. Requiring the TF combinations to repeatedly appear will help to remove spurious occurrences.…”

Cis-regulatory module detection using constraint programming

“…Several techniques have been proposed for the discovery of cis-regulatory modules [2], [3], [4], but they do not consider multiple sequences. Other tools do not take into account the proximity constraint [5], [6], or differ in the evaluation of the CRMs [7].…”