Abstract
BackgroundPrevious studies demonstrate the usefulness of using multiple tools and methods for improving the accuracy of motif detection. Over the past years, numerous motif discovery pipelines have been developed. However, they typically report only the top ranked results either from individual motif finders or from a combination of multiple tools and algorithms.ResultsHere we present MODSIDE, a motif discovery pipeline and similarity detector. The pipeline integrated four de novo motif finders: ChIPMunk, MEME, Weeder, and XXmotif. It also incorporated a motif similarity detection tool MOTIFSIM. MODSIDE was designed for delivering not only the predictive results from individual motif finders but also the comparison results for multiple tools. The results include the common significant motifs from multiple tools, the motifs detected by some tools but not by others, and the best matches for each motif in the motif collection of multiple tools. MODSIDE also possesses other useful features for merging similar motifs and clustering motifs into motif trees.ConclusionsWe evaluated MODSIDE and its adopted motif finders on 16 benchmark datasets. The statistical results demonstrate MODSIDE achieves better accuracy than individual motif finders. We also compared MODSIDE with two popular motif discovery pipelines: MEME-ChIP and RSAT peak-motifs. The comparison results reveal MODSIDE attains similar performance as RSAT peak-motifs but better accuracy than MEME-ChIP. In addition, MODSIDE is able to deliver various comparison results that are not offered by MEME-ChIP, RSAT peak-motifs, and other existing motif discovery pipelines.
Highlights
Previous studies demonstrate the usefulness of using multiple tools and methods for improving the accuracy of motif detection
Another advantage is that it allows running multiple tools and methods at once on the same server and eliminates the manual runs of the same dataset on several different motif finders residing on the same standalone server or on several different Web servers
We chose MOTIFSIM for similarity detection because of its unique features that are not offered by all existing pipelines. They include (1) the common significant motifs from multiple tools, (2) the motifs detected by some tools but not by others
Summary
Datasets The pipeline was assessed on 16 benchmark sequence datasets from Tompa et al in Table 2 [27]. MODSIDE versus ChIPMunk, MEME, Weeder, and XXmotif We measured the accuracy of each tool by calculating six statistics in the Evaluation section for the top significant motif produced by each tool for the same sequence dataset. We calculated the average statistics for each pipeline on all sequence datasets as shown in Fig. 3 and in the Additional file 1: Table S2. This can be caused by the nature design and implementation of MEME-ChIP as presented above Both RSAT peak-motifs and MODSIDE expose a similar performance, as their average accuracies are quite similar. MODSIDE has more advantages than MEME-ChIP and RSAT peakmotifs because it offers various comparison results that are not offered by MEME-ChIP, RSAT peak-motifs, and other existing pipelines
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
