Natural similarity measures between position frequency matrices with an application to clustering

By Utz J. Pape, Sven Rahmann, Martin Vingron. 2008

58 citations recorded by the source corpus.

MOTIVATION Transcription factors (TFs) play a key role in gene regulation by binding to target sequences. In silico prediction of potential binding of a TF to a binding site is a well-studied problem in computational biology. The binding sites for one TF are represented by a position frequency matrix (PFM). The discovery of new PFMs requires the comparison to known PFMs to avoid redundancies. In general, two PFMs are similar if they occur at overlapping positions under a null model. Still, most existing methods compute similarity according to probabilistic distances of the PFMs. Here we propose a natural similarity measure based on the asymptotic covariance between the number of PFM hits incorporating both strands. Furthermore, we introduce a second measure based on the same idea to cluster a set of the Jaspar PFMs. RESULTS We show that the asymptotic covariance can be efficiently computed by a two dimensional convolution of the score distributions. The asymptotic covariance approach shows strong correlation with simulated data. It outperforms three alternative methods. The Jaspar clustering yields distinct groups of TFs of the same class. Furthermore, a representative PFM is given for each class. In contrast to most other clustering methods, PFMs with low similarity automatically remain singletons. AVAILABILITY A website to compute the similarity and to perform clustering, the source code and Supplementary Material are available at http://mosta.molgen.mpg.de.

Subfields: Molecular Biology

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In Molecular Biology

1. Dirk Schübeler
2. Jussi Taipale
3. Paul Adrian Ginno
4. P. Beachy
5. Benjamin L. Ebert
6. Siddhartha Jaiswal

Citations in the corpus

• Impact of cytosine methylation on DNA binding specificities of human transcription factors (2017, methodology): “Binding models were then connected to each other if their SSTAT similarity score was > 1.5 × 10−5.”
• Conservation of transcription factor binding specificities across 600 million years of bilateria evolution (2015, methodology): “To compare the similarity of the datasets in detail, we used the motif similarity score from SSTAT (Pape et al., 2008) to draw a dendrogram (Figure 3—figure supplement 1).”
• DNA-binding specificities of human transcription factors. (2013, unknown): “For comparison between all profiles, we used SSTAT (Pape et al., 2008), which differentiates better betweenmonomers and dimers.”