title: Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data.
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.
authors.forename: Juhani
authors.initials: J
authors.lastname: Kähärä
authors.forename: Harri
authors.initials: H
authors.lastname: Lähdesmäki
mesh_terms: D000465:Algorithms; D004247:DNA / Q000235:genetics / Q000378:metabolism; D004268:DNA-Binding Proteins / Q000235:genetics / Q000378:metabolism; D050982:GATA1 Transcription Factor / Q000235:genetics / Q000378:metabolism; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D016014:Linear Models; D050778:NFATC Transcription Factors / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D011485:Protein Binding / Q000235:genetics; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000235:genetics* / Q000378:metabolism; D000071701:Regulatory Factor X Transcription Factors; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism
chemical_list: D004268:DNA-Binding Proteins; D050982:GATA1 Transcription Factor; C494688:GATA1 protein, human; D050778:NFATC Transcription Factors; C494003:NFATC1 protein, human; D011506:Proteins; C000605540:RFX3 protein, human; D000071701:Regulatory Factor X Transcription Factors; D014157:Transcription Factors; D004247:DNA