DECOD: fast and accurate discriminative DNA motif finding.
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ABSTRACT:
SUBMITTER: Huggins P
PROVIDER: S-EPMC3157928 | biostudies-literature | 2011 Sep
REPOSITORIES: biostudies-literature
Publications
DECOD: fast and accurate discriminative DNA motif finding.
Huggins Peter P Zhong Shan S Shiff Idit I Beckerman Rachel R Laptenko Oleg O Prives Carol C Schulz Marcel H MH Simon Itamar I Bar-Joseph Ziv Z
Bioinformatics (Oxford, England) 20110712 17
<h4>Motivation</h4>Motif discovery is now routinely used in high-throughput studies including large-scale sequencing and proteomics. These datasets present new challenges. The first is speed. Many motif discovery methods do not scale well to large datasets. Another issue is identifying discriminative rather than generative motifs. Such discriminative motifs are important for identifying co-factors and for explaining changes in behavior between different conditions.<h4>Results</h4>To address thes ...[more]
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Project description:MotivationHigh-throughput ChIP-seq studies typically identify thousands of peaks for a single transcription factor (TF). It is common for traditional motif discovery tools to predict motifs that are statistically significant against a naïve background distribution but are of questionable biological relevance.ResultsWe describe a simple yet effective algorithm for discovering differential motifs between two sequence datasets that is effective in eliminating systematic biases and scalable to large datasets. Tested on 207 ENCODE ChIP-seq datasets, our method identifies correct motifs in 78% of the datasets with known motifs, demonstrating improvement in both accuracy and efficiency compared with DREME, another state-of-art discriminative motif discovery tool. More interestingly, on the remaining more challenging datasets, we identify common technical or biological factors that compromise the motif search results and use advanced features of our tool to control for these factors. We also present case studies demonstrating the ability of our method to detect single base pair differences in DNA specificity of two similar TFs. Lastly, we demonstrate discovery of key TF motifs involved in tissue specification by examination of high-throughput DNase accessibility data.AvailabilityThe motifRG package is publically available via the bioconductor repository.Contactyzizhen@fhcrc.orgSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:BackgroundDetection of false-positive motifs is one of the main causes of low performance in de novo DNA motif-finding methods. Despite the substantial algorithm development effort in this area, recent comprehensive benchmark studies revealed that the performance of DNA motif-finders leaves room for improvement in realistic scenarios.ResultsUsing large-deviations theory, we derive a remarkably simple relationship that describes the dependence of false positives on dataset size for the one-occurrence per sequence motif-finding problem. As expected, we predict that false-positives can be reduced by decreasing the sequence length or by adding more sequences to the dataset. Interestingly, we find that the false-positive strength depends more strongly on the number of sequences in the dataset than it does on the sequence length, but that the dependence on the number of sequences diminishes, after which adding more sequences does not reduce the false-positive rate significantly. We compare our theoretical predictions by applying four popular motif-finding algorithms that solve the one-occurrence-per-sequence problem (MEME, the Gibbs Sampler, Weeder, and GIMSAN) to simulated data that contain no motifs. We find that the dependence of false positives detected by these softwares on the motif-finding parameters is similar to that predicted by our formula.ConclusionsWe quantify the relationship between the sequence search space and motif-finding false-positives. Based on the simple formula we derive, we provide a number of intuitive rules of thumb that may be used to enhance motif-finding results in practice. Our results provide a theoretical advance in an important problem in computational biology.
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