SVM2Motif-Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor

Marina M.C. Vidovic, Nico Görnitz, Klaus Robert Müller, Gunnar Rätsch, Marius Kloft

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Identifying discriminative motifs underlying the functionality and evolution of organisms is a major challenge in computational biology. Machine learning approaches such as support vector machines (SVMs) achieve state-of-the-art performances in genomic discrimination tasks, but-due to its black-box character-motifs underlying its decision function are largely unknown. As a remedy, positional oligomer importance matrices (POIMs) allow us to visualize the significance of position-specific subsequences. Although being a major step towards the explanation of trained SVM models, they suffer from the fact that their size grows exponentially in the length of the motif, which renders their manual inspection feasible only for comparably small motif sizes, typically k ≤ 5. In this work, we extend the work on positional oligomer importance matrices, by presenting a new machine-learning methodology, entitled motifPOIM, to extract the truly relevant motifs-regardless of their length and complexity-underlying the predictions of a trained SVM model. Our framework thereby considers the motifs as free parameters in a probabilistic model, a task which can be phrased as a non-convex optimization problem. The exponential dependence of the POIM size on the oligomer length poses a major numerical challenge, which we address by an efficient optimization framework that allows us to find possibly overlapping motifs consisting of up to hundreds of nucleotides. We demonstrate the efficacy of our approach on a synthetic data set as well as a real-world human splice site data set.

Original languageEnglish
Article numbere0144782
JournalPloS one
Issue number12
Publication statusPublished - 2015 Dec 1

Bibliographical note

Funding Information:
We greatly thank Sebastian Schultheiss, Sören Sonnenburg, Christian Widmer, Manu Setty, Alexander Zien and Christina Leslie for interesting discussions and helpful comments. MK acknowledges support by the German Research Foundation through the grant KL 2698/1-1 and KL 2698/2-1. MMCV and NG were supported by BMBF ALICE II grant 01IB15001B. We also acknowledge the support by the German Research Foundation through the grant DFG MU 987/6-1 and RA 1894/1-1. KRM thanks for partial funding by the National Research Foundation of Korea funded by the Ministry of Education, Science, and Technology in the BK21 program and the German Ministry for Education and Research as Berlin Big Data Center BBDC, funding mark 01IS14013A.

Publisher Copyright:
© 2015 Vidovic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ASJC Scopus subject areas

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