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This article is part of the supplement: Selected articles from the 10th International Workshop on Computational Systems Biology (WCSB) 2013: Bioinformatics

Open Access Research

Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data

Juhani Kähärä1* and Harri Lähdesmäki12

Author Affiliations

1 Department of Information and Computer Science, Aalto University School of Science, FI-00076 Aalto, Finland

2 Turku Centre for Biotechnology, Turku University, Turku, Finland

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BMC Bioinformatics 2013, 14(Suppl 10):S2  doi:10.1186/1471-2105-14-S10-S2

Published: 12 August 2013

Abstract

Transcription factor (TF) binding to DNA can be modeled in a number of different ways. It is highly debated which modeling methods are the best, how the models should be built and what can they be applied to. In this study a linear k-mer model proposed for predicting TF specificity in protein binding microarrays (PBM) is applied to a high-throughput SELEX data and the question of how to choose the most informative k-mers to the binding model is studied. We implemented the standard cross-validation scheme to reduce the number of k-mers in the model and observed that the number of k-mers can often be reduced significantly without a great negative effect on prediction accuracy. We also found that the later SELEX enrichment cycles provide a much better discrimination between bound and unbound sequences as model prediction accuracies increased for all proteins together with the cycle number. We compared prediction performance of k-mer and position specific weight matrix (PWM) models derived from the same SELEX data. Consistent with previous results on PBM data, performance of the k-mer model was on average 9%-units better. For the 15 proteins in the SELEX data set with medium enrichment cycles, classification accuracies were on average 71% and 62% for k-mer and PWMs, respectively. Finally, the k-mer model trained with SELEX data was evaluated on ChIP-seq data demonstrating substantial improvements for some proteins. For protein GATA1 the model can distinquish between true ChIP-seq peaks and negative peaks. For proteins RFX3 and NFATC1 the performance of the model was no better than chance.